row_id string | section string | section_slug string | resource_type string | marker string | title string | url string | url_kind string | domain string | annotation string | description string | key_contribution string | novelty string | impact string | signal string | signal_strength string | source_readme string | source_line int64 | source_url string | date_added string | collection string | collection_slug string | user_goal string | lifecycle_stages string | audience string | loop_layer string | scope_fit string | evidence_class string | evidence_tier string | source_status string | canonical_url string | source_title string | source_description string | authors string | publication_date string | publication_year string | publication_venue string | publisher string | doi string | publication_note string | primary_category string | metadata_source string | github_repo string | github_stars string | github_forks string | github_license string | github_created_at string | github_updated_at string | arxiv_id string | audited_at timestamp[ms] |
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ale-0201 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation | https://iclr.cc/virtual/2026/poster/10009450 | external | iclr.cc | Trains variable-length latent trajectories with time and step-size conditioning plus shortcut consistency, allowing one model to trade compute for quality across inference budgets without retraining. | Trains variable-length latent trajectories with time and step-size conditioning plus shortcut consistency, allowing one model to trade compute for quality across inference budgets without retraining. | Trains variable-length latent trajectories with time and step-size conditioning plus shortcut consistency, allowing one model to trade compute for quality across inference budgets without retraining. | Reuses learned computation inside one model inference rather than repeating a full agent run. Trains variable-length latent trajectories with time and step-size conditioning plus shortcut consistency, allowing one model to trade compute for quality across inference budgets without retraining. | Use LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 804 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L804 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;budget | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://iclr.cc/virtual/2026/poster/10009450 | ICLR Poster LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation ICLR 2026 | Ahmadreza Jeddi; Marco Ciccone; Babak Taati | 2026 | 2026 | International Conference on Learning Representations (ICLR) | International Conference on Learning Representations | Published at ICLR 2026; metadata verified from the official conference poster page. | ICLR proceedings | 2026-08-13T14:24:04 | ||||||||||
ale-0202 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | MoDr: Mixture-of-Depth-Recurrent Transformers for Test-Time Reasoning | https://iclr.cc/virtual/2026/poster/10011117 | external | iclr.cc | Replaces a single recurrent reasoning path with dynamically routed LoRA branches, adding solution-space exploration and load-balanced routing to the Huginn-style depth-recurrent backbone. | Replaces a single recurrent reasoning path with dynamically routed LoRA branches, adding solution-space exploration and load-balanced routing to the Huginn-style depth-recurrent backbone. | Replaces a single recurrent reasoning path with dynamically routed LoRA branches, adding solution-space exploration and load-balanced routing to the Huginn-style depth-recurrent backbone. | Reuses learned computation inside one model inference rather than repeating a full agent run. Replaces a single recurrent reasoning path with dynamically routed LoRA branches, adding solution-space exploration and load-balanced routing to the Huginn-style depth-recurrent backbone. | Use MoDr: Mixture-of-Depth-Recurrent Transformers for Test-Time Reasoning to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 805 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L805 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;verification | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://iclr.cc/virtual/2026/poster/10011117 | ICLR Poster MoDr: Mixture-of-Depth-Recurrent Transformers for Test-Time Reasoning ICLR 2026 | Xiaojing Zhang; Haifeng Wu; Gang He; Jiyang Shen; Bochen Lyu; Zhanxing Zhu | 2026 | 2026 | International Conference on Learning Representations (ICLR) | International Conference on Learning Representations | Published at ICLR 2026; metadata verified from the official conference poster page. | ICLR proceedings | 2026-08-13T14:24:04 | ||||||||||
ale-0203 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | ChainGPT: Dual-Reasoning Model with Recurrent Depth and Multi-Rank State Updates | https://iclr.cc/virtual/2026/poster/10007767 | external | iclr.cc | Combines within-layer multi-substep state updates, state-guided sparse attention, across-layer recurrence, and adaptive stopping to increase latent reasoning depth without extending visible chain-of-thought. | Combines within-layer multi-substep state updates, state-guided sparse attention, across-layer recurrence, and adaptive stopping to increase latent reasoning depth without extending visible chain-of-thought. | Combines within-layer multi-substep state updates, state-guided sparse attention, across-layer recurrence, and adaptive stopping to increase latent reasoning depth without extending visible chain-of-thought. | Reuses learned computation inside one model inference rather than repeating a full agent run. Combines within-layer multi-substep state updates, state-guided sparse attention, across-layer recurrence, and adaptive stopping to increase latent reasoning depth without extending visible chain-of-thought. | Use ChainGPT: Dual-Reasoning Model with Recurrent Depth and Multi-Rank State Updates to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 806 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L806 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;state;exit | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://iclr.cc/virtual/2026/poster/10007767 | ICLR Poster ChainGPT: Dual-Reasoning Model with Recurrent Depth and Multi-Rank State Updates ICLR 2026 | Yunao Zheng; Xiaojie Wang; Lei Ren; Chen Wei | 2026 | 2026 | International Conference on Learning Representations (ICLR) | International Conference on Learning Representations | Published at ICLR 2026; metadata verified from the official conference poster page. | ICLR proceedings | 2026-08-13T14:24:04 | ||||||||||
ale-0204 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models | https://openreview.net/forum?id=eQaJSRZiGn | external | openreview.net | Learns when a token needs extra latent refinement, using a neural decider, depth-aware LoRA, and cross-iteration attention to avoid always paying for or being degraded by additional loops. | Learns when a token needs extra latent refinement, using a neural decider, depth-aware LoRA, and cross-iteration attention to avoid always paying for or being degraded by additional loops. | Learns when a token needs extra latent refinement, using a neural decider, depth-aware LoRA, and cross-iteration attention to avoid always paying for or being degraded by additional loops. | Reuses learned computation inside one model inference rather than repeating a full agent run. Learns when a token needs extra latent refinement, using a neural decider, depth-aware LoRA, and cross-iteration attention to avoid always paying for or being degraded by additional loops. | Use Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 807 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L807 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;budget | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://openreview.net/challenge?redirect=%2Fforum%3Fid%3DeQaJSRZiGn | Verifying your browser | OpenReview | Tianyu Fu; Yichen You; Zekai Chen; Guohao Dai; Huazhong Yang; Yu Wang | 2026 | 2026 | International Conference on Machine Learning (ICML) | OpenReview | Published at ICML 2026; venue and authors verified from the official OpenReview record. | OpenReview | 2026-08-13T14:24:04 | ||||||||||
ale-0205 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers | https://arxiv.org/abs/2606.18206 | external | arxiv.org | Stabilizes very deep recurrence with pre-normalization and residual scaling, then uses latent-state convergence as the halting signal so the model allocates more iterations to harder Sudoku, maze, state-tracking, and ARC-AGI instances without a separate stopping head. | Stabilizes very deep recurrence with pre-normalization and residual scaling, then uses latent-state convergence as the halting signal so the model allocates more iterations to harder Sudoku, maze, state-tracking, and ARC-AGI instances without a separate stopping head. | Stabilizes very deep recurrence with pre-normalization and residual scaling, then uses latent-state convergence as the halting signal so the model allocates more iterations to harder Sudoku, maze, state-tracking, and ARC-AGI instances without a separate stopping head. | Reuses learned computation inside one model inference rather than repeating a full agent run. Stabilizes very deep recurrence with pre-normalization and residual scaling, then uses latent-state convergence as the halting signal so the model allocates more iterations to harder Sudoku, maze, state-tracking, and ARC-AGI i... | Use Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2606.18206; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 808 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L808 | 2026-07-20 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;state;exit | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://openreview.net/pdf/51350b6e425ed0500ac9eb9cec78ba15d9f5d1ba.pdf | [2606.18206] Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers | Looped architectures provide an inductive bias toward learning step-by-step procedures for tasks that require compositional reasoning. The number of effective layers reached by looping determines the quality of the solution these models find. Like deep architectures, looped architectures are prone to a signal propagati... | Sajad Movahedi; Vera Milovanović; Shlomo Libo Feigin; Alexander Theus; Thomas Hofmann; Valentina Boeva; T. Konstantin Rusch; Antonio Orvieto | 2026 | 2026 | Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306 | PMLR | Published in Proceedings of the 43rd International Conference on Machine Learning (ICML), PMLR 306; the linked arXiv record remains available for open access. | cs.AI | PMLR camera-ready record | 2606.18206 | 2026-08-13T14:24:04 | |||||||
ale-0206 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Loop the Loopies! | https://arxiv.org/abs/2607.16051 | external | arxiv.org | Introduces the Loopie family of sparse looped Transformers (20B parameters with 2B active and 6B with 0.6B active); the authors' matched-compute experiments over 3.5T pretraining tokens overtake a reproduced vanilla 30B-A3B baseline after roughly 600B tokens, while post-training plus test-time scaling reaches 35/42 on ... | Introduces the Loopie family of sparse looped Transformers (20B parameters with 2B active and 6B with 0.6B active); the authors' matched-compute experiments over 3.5T pretraining tokens overtake a reproduced vanilla 30B-A3B baseline after roughly 600B tokens, while post-training plus test-time scaling reaches 35/42 on ... | Introduces the Loopie family of sparse looped Transformers (20B parameters with 2B active and 6B with 0.6B active); the authors' matched-compute experiments over 3.5T pretraining tokens overtake a reproduced vanilla 30B-A3B baseline after roughly 600B tokens, while post-training plus test-time scaling reaches 35/42 on ... | Reuses learned computation inside one model inference rather than repeating a full agent run. Introduces the Loopie family of sparse looped Transformers (20B parameters with 2B active and 6B with 0.6B active); the authors' matched-compute experiments over 3.5T pretraining tokens overtake a reproduced vanilla 30B-A3B ba... | Use Loop the Loopies! to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2607.16051; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 814 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L814 | 2026-07-20 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;verification;budget | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2607.16051 | [2607.16051] Loop the Loopies! | We present the Loopie series, consisting of two Mixture-of-Experts (MoE) models: a 20B-parameter model with 2B active parameters and a 6B-parameter model with 0.6B active parameters. Looped Transformers have long faced a challenge: given an N times increase in pre-training compute, increasing the parameter count by a f... | Zitian Gao; Yilong Chen; Yihao Xiao; Xinyu Yang; Ran Tao; Joey Zhou; Bryan Dai | 2026-07-17 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.16051 | 2026-08-13T14:24:04 | ||||||||
ale-0207 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling | https://arxiv.org/abs/2606.04438 | external | arxiv.org | Combines sparse expert routing with weight-shared recurrence through iteration-conditioned adaptive normalization and capacity balancing; under matched parameters, per-token FLOPs, and active-sublayer ratios, the reported 3B model beats its vanilla MoE control on 8 of 9 benchmarks by more than one point on average, wit... | Combines sparse expert routing with weight-shared recurrence through iteration-conditioned adaptive normalization and capacity balancing; under matched parameters, per-token FLOPs, and active-sublayer ratios, the reported 3B model beats its vanilla MoE control on 8 of 9 benchmarks by more than one point on average, wit... | Combines sparse expert routing with weight-shared recurrence through iteration-conditioned adaptive normalization and capacity balancing; under matched parameters, per-token FLOPs, and active-sublayer ratios, the reported 3B model beats its vanilla MoE control on 8 of 9 benchmarks by more than one point on average, wit... | Reuses learned computation inside one model inference rather than repeating a full agent run. Combines sparse expert routing with weight-shared recurrence through iteration-conditioned adaptive normalization and capacity balancing; under matched parameters, per-token FLOPs, and active-sublayer ratios, the reported 3B m... | Use LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2606.04438; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 815 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L815 | 2026-07-20 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;verification;budget | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2606.04438 | [2606.04438] LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling | Mixture-of-Experts (MoE) and looped architectures scale models along two orthogonal axes, namely parameter capacity and effective depth. However, mainstream looped architectures rely on dense backbones that couple parameter count with per-token FLOPs, which makes it impossible to isolate the effect of iterative computa... | Wenkai Chen; Tianshu Li; Wenyong Huang; Yichun Yin; Lifeng Shang; Chengwei Qin | 2026-06-03 | 2026 | arXiv | arXiv | cs.LG | arxiv-api | 2606.04438 | 2026-08-13T14:24:04 | ||||||||
ale-0208 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Sparse Layers are Critical to Scaling Looped Language Models | https://arxiv.org/abs/2605.09165 | external | arxiv.org | Finds that dense looped models lose their scaling advantage while looped MoE models recover expressivity because routing diverges across repeated passes; also shows that loop boundaries form stronger early-exit points than arbitrary layers in standard Transformers. | Finds that dense looped models lose their scaling advantage while looped MoE models recover expressivity because routing diverges across repeated passes; also shows that loop boundaries form stronger early-exit points than arbitrary layers in standard Transformers. | Finds that dense looped models lose their scaling advantage while looped MoE models recover expressivity because routing diverges across repeated passes; also shows that loop boundaries form stronger early-exit points than arbitrary layers in standard Transformers. | Reuses learned computation inside one model inference rather than repeating a full agent run. Finds that dense looped models lose their scaling advantage while looped MoE models recover expressivity because routing diverges across repeated passes; also shows that loop boundaries form stronger early-exit points than arb... | Use Sparse Layers are Critical to Scaling Looped Language Models to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2605.09165; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 816 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L816 | 2026-07-20 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;exit | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2605.09165 | [2605.09165] Sparse Layers are Critical to Scaling Looped Language Models | Looped language models repeat a set of transformer layers through depth, reducing memory costs and providing natural early-exit points at loop boundaries. However, looped models do not scale as favorably as standard transformers with unique layers. We compare standard and Mixture-of-Experts (MoE) transformers, with and... | Ryan Lee; Jacob Biloki; Edward J. Hu; Jonathan May | 2026-05-09 | 2026 | arXiv | arXiv | cs.LG | arxiv-api | 2605.09165 | 2026-08-13T14:24:04 | ||||||||
ale-0209 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | A Mechanistic Analysis of Looped Reasoning Language Models | https://arxiv.org/abs/2604.11791 | external | arxiv.org | Traces cyclic recurrence inside looped language models and finds that many studied layers converge to distinct fixed points, with attention behavior stabilizing as recurrent blocks repeat feedforward-like inference stages; tests how block size, input injection, and normalization shape those dynamics. | Traces cyclic recurrence inside looped language models and finds that many studied layers converge to distinct fixed points, with attention behavior stabilizing as recurrent blocks repeat feedforward-like inference stages; tests how block size, input injection, and normalization shape those dynamics. | Traces cyclic recurrence inside looped language models and finds that many studied layers converge to distinct fixed points, with attention behavior stabilizing as recurrent blocks repeat feedforward-like inference stages; tests how block size, input injection, and normalization shape those dynamics. | Reuses learned computation inside one model inference rather than repeating a full agent run. Traces cyclic recurrence inside looped language models and finds that many studied layers converge to distinct fixed points, with attention behavior stabilizing as recurrent blocks repeat feedforward-like inference stages; tes... | Use A Mechanistic Analysis of Looped Reasoning Language Models to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2604.11791; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 817 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L817 | 2026-07-20 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;verification | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2604.11791 | [2604.11791] A Mechanistic Analysis of Looped Reasoning Language Models | Reasoning has become a central capability in large language models. Recent research has shown that reasoning performance can be improved by looping an LLM's layers in the latent dimension, resulting in looped reasoning language models. Despite promising results, few works have investigated how their internal dynamics d... | Hugh Blayney; Álvaro Arroyo; Johan Obando-Ceron; Pablo Samuel Castro; Aaron Courville; Michael M. Bronstein; Xiaowen Dong | 2026-04-13 | 2026 | arXiv | arXiv | 39 pages, 63 figures | cs.LG | arxiv-api | 2604.11791 | 2026-08-13T14:24:04 | |||||||
ale-0210 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Parcae: Scaling Laws For Stable Looped Language Models | https://openreview.net/forum?id=ri0LAMdhd9 | external | openreview.net | Treats the recurrent block as a dynamical system, constrains its stability, and studies how training and test-time recurrence trade parameters for additional computation. | Treats the recurrent block as a dynamical system, constrains its stability, and studies how training and test-time recurrence trade parameters for additional computation. | Treats the recurrent block as a dynamical system, constrains its stability, and studies how training and test-time recurrence trade parameters for additional computation. | Reuses learned computation inside one model inference rather than repeating a full agent run. Treats the recurrent block as a dynamical system, constrains its stability, and studies how training and test-time recurrence trade parameters for additional computation. | Use Parcae: Scaling Laws For Stable Looped Language Models to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 818 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L818 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;verification | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://openreview.net/challenge?redirect=%2Fforum%3Fid%3Dri0LAMdhd9 | Verifying your browser | OpenReview | Hayden Prairie; Zachary Novack; Taylor Berg-Kirkpatrick; Daniel Y. Fu | 2026 | 2026 | Learning to Iterate Workshop at ICLR 2026 | OpenReview | Workshop paper at the Learning to Iterate Workshop at ICLR 2026; not an ICLR main-conference paper. | OpenReview | 2026-08-13T14:24:04 | ||||||||||
ale-0211 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion | https://arxiv.org/abs/2602.11698 | external | arxiv.org | Recurs over progressively compressed representations so repeated layers specialize across resolutions instead of recomputing every token at full resolution. | Recurs over progressively compressed representations so repeated layers specialize across resolutions instead of recomputing every token at full resolution. | Recurs over progressively compressed representations so repeated layers specialize across resolutions instead of recomputing every token at full resolution. | Reuses learned computation inside one model inference rather than repeating a full agent run. Recurs over progressively compressed representations so repeated layers specialize across resolutions instead of recomputing every token at full resolution. | Use SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2602.11698; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 819 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L819 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;budget | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2602.11698 | [2602.11698] SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion | Recursive (looped) Transformers decouple computational depth from parameter depth by repeatedly applying shared layers, providing an explicit architectural primitive for iterative refinement and latent reasoning. However, early looped Transformers often underperform non-recursive baselines of equal compute. While recen... | Chengting Yu; Xiaobo Shu; Yadao Wang; Yizhen Zhang; Haoyi Wu; You Wu; Rujiao Long; Ziheng Chen; Yuchi Xu; Wenbo Su; Bo Zheng | 2026-02-12 | 2026 | arXiv | arXiv | cs.LG | arxiv-api | 2602.11698 | 2026-08-13T14:24:04 | ||||||||
ale-0212 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Training-Free Looped Transformers | https://arxiv.org/abs/2605.23872 | external | arxiv.org | Retrofits recurrence onto frozen pretrained models by applying a damped mid-stack block as smaller refinement steps, testing when inference-time looping helps without fine-tuning. | Retrofits recurrence onto frozen pretrained models by applying a damped mid-stack block as smaller refinement steps, testing when inference-time looping helps without fine-tuning. | Retrofits recurrence onto frozen pretrained models by applying a damped mid-stack block as smaller refinement steps, testing when inference-time looping helps without fine-tuning. | Reuses learned computation inside one model inference rather than repeating a full agent run. Retrofits recurrence onto frozen pretrained models by applying a damped mid-stack block as smaller refinement steps, testing when inference-time looping helps without fine-tuning. | Use Training-Free Looped Transformers to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2605.23872; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 820 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L820 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;verification | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2605.23872 | [2605.23872] Training-Free Looped Transformers | We introduce training-free looped transformers, in which a lightweight inference-time wrapper loops a contiguous mid-stack block of layers of a frozen checkpoint without additional fine-tuning, continued training, or architectural changes. Unlike prior looped transformer methods that train with the looped structure end... | Lizhang Chen; Jonathan Li; Chen Liang; Ni Lao; Qiang Liu | 2026-05-22 | 2026 | arXiv | arXiv | cs.LG | arxiv-api | 2605.23872 | 2026-08-13T14:24:04 | ||||||||
ale-0213 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers | https://arxiv.org/abs/2604.07822 | external | arxiv.org | Shows systematic generalization and depth extrapolation in controlled recurrent-depth reasoning tasks while documenting overthinking when recurrence exceeds the useful computation horizon. | Shows systematic generalization and depth extrapolation in controlled recurrent-depth reasoning tasks while documenting overthinking when recurrence exceeds the useful computation horizon. | Shows systematic generalization and depth extrapolation in controlled recurrent-depth reasoning tasks while documenting overthinking when recurrence exceeds the useful computation horizon. | Reuses learned computation inside one model inference rather than repeating a full agent run. Shows systematic generalization and depth extrapolation in controlled recurrent-depth reasoning tasks while documenting overthinking when recurrence exceeds the useful computation horizon. | Use Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2604.07822; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 821 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L821 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2604.07822 | [2604.07822] Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers | We study implicit reasoning, i.e. the ability to combine knowledge or rules within a single forward pass. While transformer-based large language models store substantial factual knowledge and rules, they often fail to compose this knowledge for implicit multi-hop reasoning, suggesting a lack of compositional generaliza... | Harsh Kohli; Srinivasan Parthasarathy; Huan Sun; Yuekun Yao | 2026-04-09 | 2026 | arXiv | arXiv | 21 pages, 21 figures. Accepted at COLM 2026 | cs.CL | arxiv-api | 2604.07822 | 2026-08-13T14:24:04 | |||||||
ale-0214 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | DeepLoop: Depth Scaling for Looped Transformers | https://arxiv.org/abs/2607.13491 | external | arxiv.org | Derives residual-scaling rules that account for repeated parameter visits and tests them on GPT-style looped language models, addressing instability that nominal layer depth alone misses. | Derives residual-scaling rules that account for repeated parameter visits and tests them on GPT-style looped language models, addressing instability that nominal layer depth alone misses. | Derives residual-scaling rules that account for repeated parameter visits and tests them on GPT-style looped language models, addressing instability that nominal layer depth alone misses. | Reuses learned computation inside one model inference rather than repeating a full agent run. Derives residual-scaling rules that account for repeated parameter visits and tests them on GPT-style looped language models, addressing instability that nominal layer depth alone misses. | Use DeepLoop: Depth Scaling for Looped Transformers to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2607.13491; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 822 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L822 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;verification | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2607.13491 | [2607.13491] DeepLoop: Depth Scaling for Looped Transformers | Looped Transformers scale sequential computation by applying a compact stack of physical blocks for multiple rounds, increasing unrolled depth without increasing stored parameters. This reuse changes the residual-scaling problem: in an untied Transformer, each residual branch receives and applies its own parameter upda... | Shuzhen Li; Yifan Zhang; Jiacheng Guo; Quanquan Gu; Mengdi Wang | 2026-07-15 | 2026 | arXiv | arXiv | 25 pages | cs.LG | arxiv-api | 2607.13491 | 2026-08-13T14:24:04 | |||||||
ale-0215 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models | https://arxiv.org/abs/2604.21106 | external | arxiv.org | Separates recurrent passes from unique parameter depth and training compute to estimate when an additional loop is worth more than adding distinct layers. | Separates recurrent passes from unique parameter depth and training compute to estimate when an additional loop is worth more than adding distinct layers. | Separates recurrent passes from unique parameter depth and training compute to estimate when an additional loop is worth more than adding distinct layers. | Reuses learned computation inside one model inference rather than repeating a full agent run. Separates recurrent passes from unique parameter depth and training compute to estimate when an additional loop is worth more than adding distinct layers. | Use How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2604.21106; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 823 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L823 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2604.21106 | [2604.21106] How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models | We measure how much one recurrence is worth to a looped (depth-recurrent) transformer, in equivalent unique parameters. From an iso-depth pretraining sweep across recurrence counts $r \in \{1, 2, 4, 8\}$ spanning ${\sim}50\times$ in training compute, we fit a joint scaling law $L = E + A\,(N_\text{once} + r^{\varphi} N... | Kristian Schwethelm; Daniel Rueckert; Georgios Kaissis | 2026-04-22 | 2026 | arXiv | arXiv | v3: substantially refined framing + minor corrections v2: added case studies on truncated-BPTT and hyperconnections | cs.LG | arxiv-api | 2604.21106 | 2026-08-13T14:24:04 | |||||||
ale-0216 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | LoopCoder: Scaling Code Intelligence via Looped Language Models | https://aclanthology.org/2026.findings-acl.796/ | external | aclanthology.org | Scales looped code models through dense-to-loop initialization, recurrent pretraining, and post-training; the Findings of ACL 2026 paper reports a 40B-active/80B-total model trained on more than 12T code and general tokens. | Scales looped code models through dense-to-loop initialization, recurrent pretraining, and post-training; the Findings of ACL 2026 paper reports a 40B-active/80B-total model trained on more than 12T code and general tokens. | Scales looped code models through dense-to-loop initialization, recurrent pretraining, and post-training; the Findings of ACL 2026 paper reports a 40B-active/80B-total model trained on more than 12T code and general tokens. | Reuses learned computation inside one model inference rather than repeating a full agent run. Scales looped code models through dense-to-loop initialization, recurrent pretraining, and post-training; the Findings of ACL 2026 paper reports a 40B-active/80B-total model trained on more than 12T code and general tokens. | Use LoopCoder: Scaling Code Intelligence via Looped Language Models to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 829 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L829 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;budget | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-paper | A | ok | https://aclanthology.org/2026.findings-acl.796/ | LoopCoder: Scaling Code Intelligence via Looped Language Models - ACL Anthology | Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai. Findings of the Association for Computat... | Jian Yang; Wei Zhang; Shuyue Guo; Yizhi Li; Linzheng Chai; Zhengmao Ye; Shukai Liu; Yuyang Song; Jiajun Wu; Che Liu; Tianyu Zheng; Siwei Wu; Leo L; Xudong Ma; Chuan Hao; Ran Tao; Yan Xing; Jianzhou Wang; Mingjie Tang; Aishan Liu; Zhoujun Li; Xianglong Liu; Weifeng Lv; Bryan Dai | 2026 | 2026 | Findings of the Association for Computational Linguistics: ACL 2026 | ACL Anthology | 10.18653/v1/2026.findings-acl.796 | html-meta | 2026-08-13T14:24:04 | |||||||||
ale-0217 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Looped World Models | https://arxiv.org/abs/2606.18208 | external | arxiv.org | Introduces LoopWM, which repeatedly refines action-conditioned latent environment states with a parameter-shared Transformer core, stability constraints, and adaptive early exit for long-horizon simulation. | Introduces LoopWM, which repeatedly refines action-conditioned latent environment states with a parameter-shared Transformer core, stability constraints, and adaptive early exit for long-horizon simulation. | Introduces LoopWM, which repeatedly refines action-conditioned latent environment states with a parameter-shared Transformer core, stability constraints, and adaptive early exit for long-horizon simulation. | Reuses learned computation inside one model inference rather than repeating a full agent run. Introduces LoopWM, which repeatedly refines action-conditioned latent environment states with a parameter-shared Transformer core, stability constraints, and adaptive early exit for long-horizon simulation. | Use Looped World Models to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2606.18208; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 830 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L830 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;exit | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2606.18208 | [2606.18208] Looped World Models | Current world models face a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding errors. We resolve this by introducing Looped World Models (LoopWM), which are the first looped architectures for world modelling. Our method ite... | Hongyuan Adam Lu; Z. L. Victor Wei; Qun Zhang; Jinrui Zeng; Bowen Cao; Lingwei Meng; Mocheng Li; Zezhong Wang; Haonan Yin; Naifu Xue; Minyu Chen; Cenyuan Zhang; Zefan Zhang; Hao Wei; Jiawei Zhou; Haoran Xu; Hao Yang; Ronglai Zuo; Tongda Xu; Yonghao Li; Jian Chen; Hebin Wang; Zeyu Gao; Yang Li; Wei Zhao; Qimin Zhong; Si... | 2026-06-16 | 2026 | arXiv | arXiv | Technical Report | cs.LG | arxiv-api | 2606.18208 | 2026-08-13T14:24:04 | |||||||
ale-0218 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers | https://arxiv.org/abs/2606.31779 | external | arxiv.org | Introduces LOTUS, which loops over parallel latent thought blocks with intermediate supervision and reports explicit-chain-of-thought-level quality at 3B scale with lower thought-phase latency. | Introduces LOTUS, which loops over parallel latent thought blocks with intermediate supervision and reports explicit-chain-of-thought-level quality at 3B scale with lower thought-phase latency. | Introduces LOTUS, which loops over parallel latent thought blocks with intermediate supervision and reports explicit-chain-of-thought-level quality at 3B scale with lower thought-phase latency. | Reuses learned computation inside one model inference rather than repeating a full agent run. Introduces LOTUS, which loops over parallel latent thought blocks with intermediate supervision and reports explicit-chain-of-thought-level quality at 3B scale with lower thought-phase latency. | Use Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2606.31779; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 831 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L831 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2606.31779 | [2606.31779] Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers | Language models typically reason via explicit chain-of-thought (CoT), generating intermediate steps token-by-token. Latent CoT offers an alternative: it performs multi-step reasoning in the model's hidden states, replacing decoded tokens with continuous representations for greater efficiency. However, existing latent C... | Ying Fan; Anej Svete; Kangwook Lee | 2026-06-30 | 2026 | arXiv | arXiv | cs.LG | arxiv-api | 2606.31779 | 2026-08-13T14:24:04 | ||||||||
ale-0219 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification | https://arxiv.org/abs/2605.16048 | external | arxiv.org | Extends tied-depth recurrence beyond Transformers by repeatedly applying a shared state-space block and reshaping inputs across iterations, testing which loop principles transfer across model families. | Extends tied-depth recurrence beyond Transformers by repeatedly applying a shared state-space block and reshaping inputs across iterations, testing which loop principles transfer across model families. | Extends tied-depth recurrence beyond Transformers by repeatedly applying a shared state-space block and reshaping inputs across iterations, testing which loop principles transfer across model families. | Reuses learned computation inside one model inference rather than repeating a full agent run. Extends tied-depth recurrence beyond Transformers by repeatedly applying a shared state-space block and reshaping inputs across iterations, testing which loop principles transfer across model families. | Use Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2605.16048; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 832 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L832 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;verification;state | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2605.16048 | [2605.16048] Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification | State Space Models (SSMs) are inherently recurrent along the sequence dimension, yet depth-recurrence - reusing the same block repeatedly across layers, as recently applied in looped transformers - has not been explored in this model family. We show that a looped SSM with $k$ parameters iterated $L$ times consistently ... | Mónika Farsang; Ramin Hasani; Daniela Rus; Radu Grosu | 2026-05-15 | 2026 | arXiv | arXiv | cs.LG | arxiv-api | 2605.16048 | 2026-08-13T14:24:04 | ||||||||
ale-0220 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Looped Diffusion Language Models | https://arxiv.org/abs/2605.26106 | external | arxiv.org | Applies selective shared-layer recurrence to masked diffusion language models so effective depth can scale during training and inference without adding parameters. | Applies selective shared-layer recurrence to masked diffusion language models so effective depth can scale during training and inference without adding parameters. | Applies selective shared-layer recurrence to masked diffusion language models so effective depth can scale during training and inference without adding parameters. | Reuses learned computation inside one model inference rather than repeating a full agent run. Applies selective shared-layer recurrence to masked diffusion language models so effective depth can scale during training and inference without adding parameters. | Use Looped Diffusion Language Models to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2605.26106; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 833 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L833 | 2026-07-18 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2605.26106 | [2605.26106] Looped Diffusion Language Models | Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models for language modeling, yet the effective design of transformer architectures for MDMs remains underexplored. In this paper, we show that selectively looping the early-middle transformer layers significantly improves both tra... | Sanghyun Lee; Chunsan Hong; Seungryong Kim; Jonghyun Lee; Jongho Park; Dongmin Park | 2026-05-25 | 2026 | arXiv | arXiv | 23 pages | cs.LG | arxiv-api | 2605.26106 | 2026-08-13T14:24:04 | |||||||
ale-0221 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Metis: Memory Foundation Model | https://arxiv.org/abs/2607.26760 | external | arxiv.org | Argues for native memory inside the backbone rather than bolted-on external modules: a persistent memory state compresses history, is read through memory attention, and is maintained gradient-free with forward passes only at inference. The model-level answer to problems the harness-level memory stacks keep patching. | Argues for native memory inside the backbone rather than bolted-on external modules: a persistent memory state compresses history, is read through memory attention, and is maintained gradient-free with forward passes only at inference. The model-level answer to problems the harness-level memory stacks keep patching. | Argues for native memory inside the backbone rather than bolted-on external modules: a persistent memory state compresses history, is read through memory attention, and is maintained gradient-free with forward passes only at inference. The model-level answer to problems the harness-level memory stacks keep patching. | Reuses learned computation inside one model inference rather than repeating a full agent run. Argues for native memory inside the backbone rather than bolted-on external modules: a persistent memory state compresses history, is read through memory attention, and is maintained gradient-free with forward passes only at i... | Use Metis: Memory Foundation Model to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2607.26760; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 834 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L834 | 2026-07-30 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;context;state | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2607.26760 | [2607.26760] Metis: Memory Foundation Model | Recent advances in AI agents have increasingly internalized native capabilities into their underlying foundation models, giving rise to multimodal foundation models and large reasoning models. However, agent memory is still primarily implemented through external modules, leaving the native memory capability largely une... | Zeyu Zhang; Ziliang Guo; Yihang Sun; Xichong Zhang; Xixuan Hao; Zehao Lin; Yang Zhang; Xiaoyan Zhao; Tong Shen; Bo Tang; Zhi-Qin John Xu; Junchi Yan; Haofen Wang; Xu Chen; Feiyu Xiong; Zhiyu Li; Tat-Seng Chua | 2026-07-29 | 2026 | arXiv | arXiv | 46 pages, 11 figures, 16 tables | cs.CL | arxiv-api | 2607.26760 | 2026-08-13T14:24:04 | |||||||
ale-0222 | Model-Level Recurrence | model-level-recurrence | Paper | 📄 | Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory | https://arxiv.org/abs/2607.27919 | external | arxiv.org | Attacks the memory problem one layer below retrieval: decoder-only models entangle long-term memory and reasoning in a single parameter set, so memory capacity cannot be scaled independently of the reasoning capacity you are paying for. | Attacks the memory problem one layer below retrieval: decoder-only models entangle long-term memory and reasoning in a single parameter set, so memory capacity cannot be scaled independently of the reasoning capacity you are paying for. | Attacks the memory problem one layer below retrieval: decoder-only models entangle long-term memory and reasoning in a single parameter set, so memory capacity cannot be scaled independently of the reasoning capacity you are paying for. | Reuses learned computation inside one model inference rather than repeating a full agent run. Attacks the memory problem one layer below retrieval: decoder-only models entangle long-term memory and reasoning in a single parameter set, so memory capacity cannot be scaled independently of the reasoning capacity you are p... | Use Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory to assess inner latent computation as a model capability inside a separately governed agent loop. | Research source arXiv:2607.27919; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 835 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L835 | 2026-08-07 | Learn | learn | Understand how recurrent model computation can power, but not replace, a governed agent loop. | act;context | researcher;evaluator;model-builder;agent-builder | model | adjacent | research-preprint | A | ok | https://arxiv.org/abs/2607.27919 | [2607.27919] Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory | Decoder-only language models entangle long-term memory and reasoning in a single parameter set, making it difficult to scale memory capacity independently. Memory Decoder introduces a parametric long-term memory module but only studies it at a relatively small scale. In this work, we present Memory Decoder at Scale, sc... | Rubin Wei; Jiaqi Cao; Jiarui Wang; Junming Zhang; Qipeng Guo; Bowen Zhou; Zhouhan Lin | 2026-07-30 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.27919 | 2026-08-13T14:24:04 | ||||||||
ale-0223 | Agent Workflow Patterns | agent-workflow-patterns | Tool | 🧰 | Discovery Loop | https://www.discoveryloop.com/ | external | www.discoveryloop.com | Company thesis for automating the experimental loop itself: propose, implement, run, examine, iterate, driven by frontier models over large-scale compute so thousands of experiments run in parallel instead of sequentially by hand. Starts with machine learning research and uses its own stack as the first customer. A sta... | Company thesis for automating the experimental loop itself: propose, implement, run, examine, iterate, driven by frontier models over large-scale compute so thousands of experiments run in parallel instead of sequentially by hand. Starts with machine learning research and uses its own stack as the first customer. A sta... | Company thesis for automating the experimental loop itself: propose, implement, run, examine, iterate, driven by frontier models over large-scale compute so thousands of experiments run in parallel instead of sequentially by hand. Starts with machine learning research and uses its own stack as the first customer. A sta... | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Company thesis for automating the experimental loop itself: propose, implement, run, examine, iterate, driven by frontier models over large-scale compute so thousands of experiments run in parallel instead of sequentially by h... | Use Discovery Loop to turn a recurring-agent idea into an explicit loop contract. | Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption. | high | README.md | 845 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L845 | Design | design | Specify a loop contract and operating pattern. | intake | builder | workflow | enabling | implementation | A | ok | https://www.discoveryloop.com/ | Discovery Loop — Continuous Exploration | Discovery Loop is building AI systems that automate the experimental loops of science and engineering. Continuous Exploration. | discoveryloop.com | domain-fallback | 2026-08-13T14:24:04 | |||||||||||||||
ale-0224 | Agent Workflow Patterns | agent-workflow-patterns | Docs | 📚 | Building Effective Agents | https://www.anthropic.com/engineering/building-effective-agents | external | www.anthropic.com | Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns. | Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns. | Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns. | Orchestration and control flow are made explicit and inspectable. Anthropic's canonical guide to workflows and agents, including evaluator-optimizer and orchestrator-workers patterns. | Use Building Effective Agents to turn a recurring-agent idea into an explicit loop contract. | Primary documentation from a platform, SDK, standard, or framework; strong implementation signal. | high | README.md | 846 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L846 | Design | design | Specify a loop contract and operating pattern. | delegation | builder | workflow | enabling | technical-documentation | A | ok | https://www.anthropic.com/engineering/building-effective-agents | Building Effective AI Agents \ Anthropic | Discover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI. | Anthropic | domain-fallback | 2026-08-13T14:24:04 | |||||||||||||||
ale-0225 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime | https://www.preprints.org/manuscript/202603.1756 | external | www.preprints.org | Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects. | Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects. | Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects. | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Decomposes the harness layer that loops build on into control, agency, and runtime, audits 63 harness works, and proposes a HarnessCard so reported agent gains can be separated from harness effects. | Use Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime to turn a recurring-agent idea into an explicit loop contract. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 847 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L847 | Design | design | Specify a loop contract and operating pattern. | delegation;verification | researcher;evaluator | workflow | enabling | research-paper | A | restricted | https://www.preprints.org/manuscript/202603.1756 | Chaoyue He; Xin Zhou; Di Wang; Hong Xu; Wei Liu; Chunyan Miao | 2026-04-23 | 2026 | Preprints.org | Preprints.org | 10.20944/preprints202603.1756.v2 | Version 2; the primary source states that this preprint is not peer-reviewed. | primary-page | 2026-08-13T14:24:04 | |||||||||||
ale-0226 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | How we built our multi-agent research system | https://www.anthropic.com/engineering/multi-agent-research-system | external | www.anthropic.com | Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops. | Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops. | Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops. | Persistent memory is treated as an external runtime artifact. Detailed orchestrator-worker system with planning, memory, subagents, citation passes, and iterative research loops. | Use How we built our multi-agent research system to turn a recurring-agent idea into an explicit loop contract. | Contextual source from www.anthropic.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 848 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L848 | Design | design | Specify a loop contract and operating pattern. | context;delegation | builder | workflow | enabling | practitioner-analysis | B | ok | https://www.anthropic.com/engineering/multi-agent-research-system | How we built our multi-agent research system \ Anthropic | On the the engineering challenges and lessons learned from building Claude's Research system | Anthropic | domain-fallback | 2026-08-13T14:24:04 | |||||||||||||||
ale-0227 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Building Effective AI Agents: Architecture Patterns and Implementation Frameworks | https://resources.anthropic.com/hubfs/Building%20Effective%20AI%20Agents-%20Architecture%20Patterns%20and%20Implementation%20Frameworks.pdf | external | resources.anthropic.com | PDF overview of agent architecture patterns, including generator-evaluator loops. | PDF overview of agent architecture patterns, including generator-evaluator loops. | PDF overview of agent architecture patterns, including generator-evaluator loops. | Distills reusable agent-control patterns that are not tied to a single vendor implementation. PDF overview of agent architecture patterns, including generator-evaluator loops. | Use Building Effective AI Agents: Architecture Patterns and Implementation Frameworks to turn a recurring-agent idea into an explicit loop contract. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 849 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L849 | Design | design | Specify a loop contract and operating pattern. | delegation;verification | researcher;evaluator | workflow | enabling | research-paper | A | ok | https://resources.anthropic.com/hubfs/Building%20Effective%20AI%20Agents-%20Architecture%20Patterns%20and%20Implementation%20Frameworks.pdf | Anthropic | 2025-12-03 | 2025 | Anthropic eBook | Anthropic | Date verified from the primary PDF creation metadata. | pdf-metadata | 2026-08-13T14:24:04 | ||||||||||||
ale-0228 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | AI Agent Architectures | https://hld.handbook.academy/curriculum/ai-ml-system-design/ai-agent-architectures/ | external | hld.handbook.academy | System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies. | System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies. | System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies. | Persistent memory is treated as an external runtime artifact. System-design overview of ReAct, reflection, planning, tool use, memory, and control strategies. | Use AI Agent Architectures to turn a recurring-agent idea into an explicit loop contract. | Contextual source from hld.handbook.academy; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 850 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L850 | Design | design | Specify a loop contract and operating pattern. | workspace;context | builder | workflow | enabling | practitioner-analysis | B | ok | https://hld.handbook.academy/curriculum/ai-ml-system-design/ai-agent-architectures/ | AI Agent Architectures (ReAct, Reflection, Planning, Tool Use, Memory) - The HLD Handbook | The canonical patterns for turning an LLM into an agent: ReAct's think-act-observe loop, reflection and self-critique, planner-executor decomposition, tool use and function calling, and how agents manage short- and long-term memory. | The HLD Handbook | html-meta | 2026-08-13T14:24:04 | |||||||||||||||
ale-0229 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | What Are Agentic Workflows? | https://weaviate.io/blog/what-are-agentic-workflows | external | weaviate.io | Accessible taxonomy of planning, tool use, reflection, and memory patterns. | Accessible taxonomy of planning, tool use, reflection, and memory patterns. | Accessible taxonomy of planning, tool use, reflection, and memory patterns. | Persistent memory is treated as an external runtime artifact. Accessible taxonomy of planning, tool use, reflection, and memory patterns. | Use What Are Agentic Workflows? to turn a recurring-agent idea into an explicit loop contract. | Contextual source from weaviate.io; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 851 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L851 | Design | design | Specify a loop contract and operating pattern. | workspace;context | builder | workflow | enabling | practitioner-analysis | B | ok | https://weaviate.io/blog/what-are-agentic-workflows | What Are Agentic Workflows? Patterns, Memory, Use Cases, and Examples | Weaviate | Agentic workflows combine AI agents, tools, and agent memory to create adaptive systems. Learn the core patterns, use cases, and real-world examples. | 2025-03-06 | 2025 | weaviate.io | html-meta | 2026-08-13T14:24:04 | |||||||||||||
ale-0230 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | Agent Planning & Reflection Patterns | https://learnaivisually.com/tracks/ai-agents/planning-reflection | external | learnaivisually.com | Visual explanation of plan-execute, observe, reflect, retry, and stop patterns. | Visual explanation of plan-execute, observe, reflect, retry, and stop patterns. | Visual explanation of plan-execute, observe, reflect, retry, and stop patterns. | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Visual explanation of plan-execute, observe, reflect, retry, and stop patterns. | Use Agent Planning & Reflection Patterns to turn a recurring-agent idea into an explicit loop contract. | Contextual source from learnaivisually.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 852 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L852 | Design | design | Specify a loop contract and operating pattern. | budget;exit | builder | workflow | enabling | practitioner-analysis | B | ok | https://learnaivisually.com/tracks/ai-agents/planning-reflection | Agent Planning & Reflection Patterns | Learn AI Visually LAV LAV | When agents should plan, retry, pause, or stop. Reasoning budget, ReAct, Reflexion, and termination logic — each tied to a 'when' decision. | Learn AI Visually | html-meta | 2026-08-13T14:24:04 | |||||||||||||||
ale-0231 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | Agentic Design Patterns | https://addyosmani.com/agents/04-agentic-design-patterns/ | external | addyosmani.com | Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents. | Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents. | Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents. | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Practical overview of ReAct, reflection, tool use, planning, and how to combine them in real-world agents. | Use Agentic Design Patterns to turn a recurring-agent idea into an explicit loop contract. | Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 853 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L853 | Design | design | Specify a loop contract and operating pattern. | workspace | builder | workflow | enabling | practitioner-analysis | B | ok | https://addyosmani.com/agents/04-agentic-design-patterns/ | AddyOsmani.com - Lesson 4: agentic design patterns | Addy Osmani is an engineering and evangelism leader who spent over 14 years at Google leading developer experience across Chrome and, in recent years, AI (Gemini, coding agents, and agentic engineering), most recently as a Director at Google Cloud AI. | Addy Osmani | addyosmani.com | html-meta | 2026-08-13T14:24:04 | ||||||||||||||
ale-0232 | Agent Workflow Patterns | agent-workflow-patterns | Pattern | 🔁 | 12 Factor Agents | https://github.com/humanlayer/12-factor-agents | external | github.com | Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior. | Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior. | Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior. | State persistence is explicit enough for repeated runs and handoff. Operating principles for production agents, including explicit prompts, state ownership, and pause-resume behavior. | Use 12 Factor Agents to turn a recurring-agent idea into an explicit loop contract. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 854 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L854 | Design | design | Specify a loop contract and operating pattern. | state | builder | workflow | enabling | operational-pattern | B | ok | https://github.com/humanlayer/12-factor-agents | GitHub - humanlayer/12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? · GitHub | What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? - humanlayer/12-factor-agents | humanlayer/12-factor-agents | GitHub | html-meta | humanlayer/12-factor-agents | 2026-08-13T14:24:04 | |||||||||||||
ale-0233 | Agent Workflow Patterns | agent-workflow-patterns | Pattern | 🔁 | Durable Execution for Agentic Workflows | https://arizenai.com/durable-execution/ | external | arizenai.com | Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows. | Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows. | Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows. | Durable execution and replay are treated as first-class loop infrastructure. Explains checkpointing, event-sourced journals, replay, and recovery for long-running agent workflows. | Use Durable Execution for Agentic Workflows to turn a recurring-agent idea into an explicit loop contract. | Operational pattern or playbook; signal comes from reusable loop structure and practical transferability. | medium | README.md | 855 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L855 | Design | design | Specify a loop contract and operating pattern. | state | builder | workflow | enabling | operational-pattern | B | ok | https://arizenai.com/durable-execution/ | Durable Execution for Agentic Workflows | Arizen | A while loop is at-most-once across process boundaries. Production agents need exactly-once. The architecture must encode the guarantee. | 2026-03-30 | 2026 | Arizen | html-meta | 2026-08-13T14:24:04 | |||||||||||||
ale-0234 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Loop Engineering Meets Graph Engineering | gallery/loop-graph-reference.md | local_path | Proposes Loop-Graph, a long-horizon agent framework that couples iterative refinement loops with graph-structured persistent memory; the supplied manuscript screenshot reports evaluation on 9,842 real-world tasks, success gains up to 38.6 percentage points, correctness gains up to 27.4 percentage points, fewer redundan... | Proposes Loop-Graph, a long-horizon agent framework that couples iterative refinement loops with graph-structured persistent memory; the supplied manuscript screenshot reports evaluation on 9,842 real-world tasks, success gains up to 38.6 percentage points, correctness gains up to 27.4 percentage points, fewer redundan... | Proposes Loop-Graph, a long-horizon agent framework that couples iterative refinement loops with graph-structured persistent memory; the supplied manuscript screenshot reports evaluation on 9,842 real-world tasks, success gains up to 38.6 percentage points, correctness gains up to 27.4 percentage points, fewer redundan... | Control flow is represented as an inspectable graph rather than an opaque prompt loop. Proposes Loop-Graph, a long-horizon agent framework that couples iterative refinement loops with graph-structured persistent memory; the supplied manuscript screenshot reports evaluation on 9,842 real-world tasks, success gains up to... | Use Loop Engineering Meets Graph Engineering to turn a recurring-agent idea into an explicit loop contract. | Research source; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 856 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L856 | 2026-07-21 | Design | design | Specify a loop contract and operating pattern. | workspace;context;verification;state | researcher;evaluator | workflow | enabling | research-preprint | A | local_ok | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/gallery/loop-graph-reference.md | Loop Engineering Meets Graph Engineering | Lingjiao Chen; Matei Zaharia; Jack Clark; Christopher Re; Chelsea Finn; Ion Stoica | 2026 | 2026 | GitHub | GitHub | Author list transcribed from the manuscript screenshot supplied with the gallery reference; kept in code so audit regeneration preserves it. | repository | 2026-08-13T14:24:04 | |||||||||||
ale-0235 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Code as Agent Harness | https://arxiv.org/abs/2605.18747 | external | arxiv.org | Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on. | Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on. | Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on. | The work separates roles across agents, verifiers, or orchestration layers. Organizes agent infrastructure into harness interface, feedback-driven control, and multi-agent scaling for executable, verifiable, stateful systems; maps the harness layer that loops build on. | Use Code as Agent Harness to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2605.18747; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 857 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L857 | Design | design | Specify a loop contract and operating pattern. | delegation;state | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2605.18747 | [2605.18747] Code as Agent Harness | Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoni... | Xuying Ning; Katherine Tieu; Dongqi Fu; Tianxin Wei; Zihao Li; Yuanchen Bei; Jiaru Zou; Mengting Ai; Zhining Liu; Ting-Wei Li; Lingjie Chen; Yanjun Zhao; Ke Yang; Bingxuan Li; Cheng Qian; Gaotang Li; Xiao Lin; Zhichen Zeng; Ruizhong Qiu; Sirui Chen; Yifan Sun; Xiyuan Yang; Ruida Wang; Rui Pan; Chenyuan Yang; Dylan Zhan... | 2026-05-18 | 2026 | arXiv | arXiv | GitHub: https://github.com/YennNing/Awesome-Code-as-Agent-Harness-Papers | cs.CL | arxiv-api | 2605.18747 | 2026-08-13T14:24:04 | ||||||||
ale-0236 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Agentic Agile-V: From Vibe Coding to Verified Engineering | https://arxiv.org/abs/2605.20456 | external | arxiv.org | Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts. | Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts. | Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts. | Verification is promoted from a final check to a loop-control signal. Proposes a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) with human approval gates, arguing agentic coding needs process control and independent verification, not better prompts. | Use Agentic Agile-V: From Vibe Coding to Verified Engineering to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2605.20456; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 858 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L858 | Design | design | Specify a loop contract and operating pattern. | delegation;verification;escalation | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2605.20456 | [2605.20456] Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development | Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some settings, but current evidence does not support the simple claim that autonomous code generation automatica... | Christopher Koch | 2026-05-19 | 2026 | arXiv | arXiv | 7 pages, 1 figure | cs.SE | arxiv-api | 2605.20456 | 2026-08-13T14:24:04 | ||||||||
ale-0237 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Agentic Software Engineering: Foundational Pillars and a Research Roadmap | https://arxiv.org/abs/2509.06216 | external | arxiv.org | Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside. | Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside. | Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside. | Orchestration and control flow are made explicit and inspectable. Splits agentic SE into an Agent Command Environment for human orchestration and an Agent Execution Environment for agent task execution, a research roadmap for the layers recurring loops run inside. | Use Agentic Software Engineering: Foundational Pillars and a Research Roadmap to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2509.06216; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 859 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L859 | Design | design | Specify a loop contract and operating pattern. | delegation;escalation | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2509.06216 | [2509.06216] Agentic Software Engineering: Foundational Pillars and a Research Roadmap | Agentic Software Engineering (SE 3.0) represents a new era where intelligent agents are tasked not with simple code generation, but with achieving complex, goal-oriented SE objectives. To harness these new capabilities while ensuring trustworthiness, we must recognize a fundamental duality within the SE field in the Ag... | Ahmed E. Hassan; Hao Li; Dayi Lin; Bram Adams; Tse-Hsun Chen; Yutaro Kashiwa; Dong Qiu | 2025-09-07 | 2025 | arXiv | arXiv | cs.SE | arxiv-api | 2509.06216 | 2026-08-13T14:24:04 | |||||||||
ale-0238 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | The Art of Loop Engineering | https://www.langchain.com/blog/the-art-of-loop-engineering | external | www.langchain.com | LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example. | LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example. | LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example. | Verification is promoted from a final check to a loop-control signal. LangChain's account of four stacked loops around agents (core execution, rubric-based verification, event-driven triggers, and trace-driven self-improvement) using a documentation-writing agent as the running example. | Use The Art of Loop Engineering to turn a recurring-agent idea into an explicit loop contract. | Contextual source from www.langchain.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 860 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L860 | Design | design | Specify a loop contract and operating pattern. | trigger;verification | builder | workflow | enabling | practitioner-analysis | B | ok | https://www.langchain.com/blog/the-art-of-loop-engineering | The Art of Loop Engineering | Agents automate real-world work, but reliable performance requires more than a good model, it requires a carefully designed harness built for specific tasks. This post explores the core agent loop, how stacking and extending loops builds more effective agents, and how to instrument each level with LangChain primitives. | LangChain | domain-fallback | 2026-08-13T14:24:04 | |||||||||||||||
ale-0239 | Agent Workflow Patterns | agent-workflow-patterns | Tool | 🧰 | Loopy | https://github.com/Forward-Future/loopy | external | github.com | Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows. | Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows. | Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows. | Verification is promoted from a final check to a loop-control signal. Library of reusable AI-agent loops with verification checks and stopping conditions, plus an installable skill for finding, adapting, and designing repeatable agent workflows. | Use Loopy to turn a recurring-agent idea into an explicit loop contract. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 861 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L861 | Design | design | Specify a loop contract and operating pattern. | verification;exit | builder | workflow | enabling | source-implementation | A | ok | https://github.com/Forward-Future/loopy | GitHub - Forward-Future/loopy: A library of practical AI-agent loops and an installable skill for finding, adapting, and designing repeatable agent workflows. · GitHub | A library of practical AI-agent loops and an installable skill for finding, adapting, and designing repeatable agent workflows. - Forward-Future/loopy | Forward-Future/loopy | GitHub | html-meta | Forward-Future/loopy | 2026-08-13T14:24:04 | |||||||||||||
ale-0240 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | The Factory Model: How Coding Agents Changed Software Engineering | https://addyosmani.com/blog/factory-model/ | external | addyosmani.com | Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running "thirty minutes, an hour, several hours and increasingly days" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay. | Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running "thirty minutes, an hour, several hours and increasingly days" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay. | Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running "thirty minutes, an hour, several hours and increasingly days" and verification, not generation, as the new bottleneck; the fleet-level framing that precedes his June Loop Engineering essay. | Verification is promoted from a final check to a loop-control signal. Addy Osmani's February 2026 essay recasting engineers as operators of parallel agent workstreams, with agents running "thirty minutes, an hour, several hours and increasingly days" and verification, not generation, as the new bottleneck; the fleet-le... | Use The Factory Model: How Coding Agents Changed Software Engineering to turn a recurring-agent idea into an explicit loop contract. | Contextual source from addyosmani.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 862 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L862 | Design | design | Specify a loop contract and operating pattern. | verification | builder | workflow | enabling | practitioner-analysis | B | ok | https://addyosmani.com/blog/factory-model/ | AddyOsmani.com - The Factory Model: How Coding Agents Changed Software Engineering | Software engineering is not about writing code anymore. It is about building the factory that builds your software. | Addy Osmani | addyosmani.com | html-meta | 2026-08-13T14:24:04 | ||||||||||||||
ale-0241 | Agent Workflow Patterns | agent-workflow-patterns | Docs | 📚 | 2026 Agentic Coding Trends Report | https://resources.anthropic.com/2026-agentic-coding-trends-report | external | resources.anthropic.com | Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public). | Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public). | Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is public). | Primary-source operational guidance rather than commentary. Anthropic's official 2026 trends report on the shift from single coding assistants to coordinated agent teams running autonomously for hours or days, with case studies from Rakuten, TELUS, and Zapier (landing page is registration-gated; the direct PDF is publi... | Use 2026 Agentic Coding Trends Report to turn a recurring-agent idea into an explicit loop contract. | Primary documentation from a platform, SDK, standard, or framework; strong implementation signal. | high | README.md | 863 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L863 | Design | design | Specify a loop contract and operating pattern. | delegation;verification | builder | workflow | enabling | technical-documentation | A | ok | https://resources.anthropic.com/2026-agentic-coding-trends-report | 2026 Agentic Coding Trends Report | How coding agents are transforming software development - and what it means for engineering teams in 2026. Insights on multi-agent systems, human-AI collaboration, and scaling agentic coding across organizations. Includes case studies from Rakuten, TELUS, Zapier, and more. | 2026 | Anthropic | url-date | 2026-08-13T14:24:04 | ||||||||||||||
ale-0242 | Agent Workflow Patterns | agent-workflow-patterns | Tool | 🧰 | HomeRail | https://github.com/xiaotianfotos/homerail | external | github.com | TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end. | TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end. | TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end. | Control flow is represented as an inspectable graph rather than an opaque prompt loop. TypeScript runtime that turns one-off agent chats into auditable, reusable DAG workflows on self-hosted hardware, with a DAG engine, CLI, and voice front end. | Use HomeRail to turn a recurring-agent idea into an explicit loop contract. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 864 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L864 | Design | design | Specify a loop contract and operating pattern. | delegation;verification | builder | workflow | enabling | source-implementation | A | ok | https://github.com/xiaotianfotos/homerail | GitHub - xiaotianfotos/homerail: Voice-first local agent orchestration runtime for auditable DAG workflows. · GitHub | Voice-first local agent orchestration runtime for auditable DAG workflows. - xiaotianfotos/homerail | xiaotianfotos/homerail | GitHub | html-meta | xiaotianfotos/homerail | 2026-08-13T14:24:04 | |||||||||||||
ale-0243 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | Old and New Apps, via Modern Coding Agents | https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/ | external | terrytao.wordpress.com | Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is autom... | Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is autom... | Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-level design decisions stay with the author while implementation is autom... | Verification is promoted from a final check to a loop-control signal. Terence Tao's July 11, 2026 account of porting roughly two dozen Java 1.0 applets to JavaScript and resurrecting long-abandoned projects through iterative agent sessions, concluding that domain expertise remains the human verification layer: high-lev... | Use Old and New Apps, via Modern Coding Agents to turn a recurring-agent idea into an explicit loop contract. | Contextual source from terrytao.wordpress.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 865 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L865 | Design | design | Specify a loop contract and operating pattern. | verification;escalation | builder | workflow | enabling | practitioner-analysis | B | ok | https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/ | Old and new apps, via modern coding agents | What's new | I have been interested in machine-assisted ways to do and teach mathematics from as far back as 1999, when I started coding several applets in Java 1.0, both for my complex analysis and linear alge… | 2026-07-11 | 2026 | What's new | html-meta | 2026-08-13T14:24:04 | |||||||||||||
ale-0244 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable | https://arxiv.org/abs/2607.13285 | external | arxiv.org | Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use. | Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use. | Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use. | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Introduces a behavior-centered source map and behavior-guided program decomposition for evolving harnesses, improving behavior localization and edit planning on two agent harnesses while reducing planner token use. | Use Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.13285; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 866 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L866 | 2026-07-17 | Design | design | Specify a loop contract and operating pattern. | budget | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.13285 | [2607.13285] Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable | The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, environments, and requirements evolve, the harness must be continually modified. Before such a change can be made, a d... | Ruhan Wang; Yucheng Shi; Zongxia Li; Zhongzhi Li; Yue Yu; Junyao Yang; Kishan Panaganti; Haitao Mi; Dongruo Zhou; Leoweiliang | 2026-07-14 | 2026 | arXiv | arXiv | 29 pages, 6 figures. Project page: https://ruhan-wang.github.io/Harness-Handbook/ | cs.AI | arxiv-api | 2607.13285 | 2026-08-13T14:24:04 | |||||||
ale-0245 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | MemoHarness: Agent Harnesses That Learn from Experience | https://arxiv.org/abs/2607.14159 | external | arxiv.org | Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions. | Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions. | Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions. | Persistent memory is treated as an external runtime artifact. Lets an agent adapt six harness dimensions per case using a dual-layer experience memory; results improve over fixed harnesses, while broad robustness and the contribution of each adaptive component remain open questions. | Use MemoHarness: Agent Harnesses That Learn from Experience to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.14159; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 867 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L867 | 2026-07-17 | Design | design | Specify a loop contract and operating pattern. | context | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.14159 | [2607.14159] MemoHarness: Agent Harnesses That Learn from Experience | An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. While harness design strongly affects agent behavior, most automatic improvement methods optimize narrower artifacts such as prompts, pipelines,... | Yue Huang; Wenjie Wang; Han Bao; Yuchen Ma; Xiaonan Luo; Yi Nian; Haomin Zhuang; Zheyuan Liu; Yue Zhao; Xiangliang Zhang | 2026-07-14 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.14159 | 2026-08-13T14:24:04 | ||||||||
ale-0246 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Recursive Harness Self-Improvement | https://arxiv.org/abs/2607.15524 | external | arxiv.org | Represents an agent loop as a prompt-level harness specification and iteratively revises it from pairwise feedback over its own history; across 30 synthetic machine-learning research tasks, a few revisions lift low-reasoning agents above the corresponding maximum-reasoning setting while cutting inference cost by up to ... | Represents an agent loop as a prompt-level harness specification and iteratively revises it from pairwise feedback over its own history; across 30 synthetic machine-learning research tasks, a few revisions lift low-reasoning agents above the corresponding maximum-reasoning setting while cutting inference cost by up to ... | Represents an agent loop as a prompt-level harness specification and iteratively revises it from pairwise feedback over its own history; across 30 synthetic machine-learning research tasks, a few revisions lift low-reasoning agents above the corresponding maximum-reasoning setting while cutting inference cost by up to ... | Context is managed as durable loop state rather than a single prompt payload. Represents an agent loop as a prompt-level harness specification and iteratively revises it from pairwise feedback over its own history; across 30 synthetic machine-learning research tasks, a few revisions lift low-reasoning agents above the ... | Use Recursive Harness Self-Improvement to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.15524; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 868 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L868 | 2026-07-20 | Design | design | Specify a loop contract and operating pattern. | context;budget | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.15524 | [2607.15524] Recursive Harness Self-Improvement | Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This motivates harness-in-the-loop learning: optimizing harnesses for both immediate agent performance and the quality of traces used for future ... | Hyunin Lee; Jinglue Xu; Jeffrey Seely; Donghyun Lee; Matei Zaharia; Yujin Tang | 2026-07-17 | 2026 | arXiv | arXiv | This work addresses the first half of the model-harness coevolution loop | cs.LG | arxiv-api | 2607.15524 | 2026-08-13T14:24:04 | |||||||
ale-0247 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents | https://arxiv.org/abs/2607.15557 | external | arxiv.org | Filters roughly 821,000 crawled agent skills into a 96,401-item corpus with a 16-class taxonomy and utility, robustness, and safety facets; evaluations across three benchmarks, two harnesses, and two open models report gains up to 7.5 percentage points while exposing both coverage and harness boundaries. The announced ... | Filters roughly 821,000 crawled agent skills into a 96,401-item corpus with a 16-class taxonomy and utility, robustness, and safety facets; evaluations across three benchmarks, two harnesses, and two open models report gains up to 7.5 percentage points while exposing both coverage and harness boundaries. The announced ... | Filters roughly 821,000 crawled agent skills into a 96,401-item corpus with a 16-class taxonomy and utility, robustness, and safety facets; evaluations across three benchmarks, two harnesses, and two open models report gains up to 7.5 percentage points while exposing both coverage and harness boundaries. The announced ... | Packages the evidence as queryable CSV and JSONL rather than only a rendered page. Filters roughly 821,000 crawled agent skills into a 96,401-item corpus with a 16-class taxonomy and utility, robustness, and safety facets; evaluations across three benchmarks, two harnesses, and two open models report gains up to 7.5 pe... | Use SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.15557; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 869 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L869 | 2026-07-20 | Design | design | Specify a loop contract and operating pattern. | verification | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.15557 | [2607.15557] SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents | Agent skills, SKILL files that package reusable procedural knowledge for an LLM agent, are a popular mechanism for extending agent capabilities. Public repositories now host them in large and growing numbers, yet these artifacts are fragmented, redundant, and uneven in quality, and their value in practice is unclear. A... | Yanze Wang; Pengfei Yao; Tianyi Sun; Chuanrui Hu; Yan Xiao; Xiaotian Luo; Yunyun Han; Yifan Chen; Jun Sun; Yafeng Deng | 2026-07-17 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.15557 | 2026-08-13T14:24:04 | ||||||||
ale-0248 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents | https://arxiv.org/abs/2607.15715 | external | arxiv.org | Compares a fixed extraction workflow with reflective variants under one evaluation harness, measuring tool execution, retries, reflection, memory use, runtime, and recovery so agentic mechanisms can be judged by observable process changes rather than output scores alone. | Compares a fixed extraction workflow with reflective variants under one evaluation harness, measuring tool execution, retries, reflection, memory use, runtime, and recovery so agentic mechanisms can be judged by observable process changes rather than output scores alone. | Compares a fixed extraction workflow with reflective variants under one evaluation harness, measuring tool execution, retries, reflection, memory use, runtime, and recovery so agentic mechanisms can be judged by observable process changes rather than output scores alone. | Evaluation data is used as the feedback signal for improving loop behavior. Compares a fixed extraction workflow with reflective variants under one evaluation harness, measuring tool execution, retries, reflection, memory use, runtime, and recovery so agentic mechanisms can be judged by observable process changes rathe... | Use Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.15715; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 870 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L870 | 2026-07-20 | Design | design | Specify a loop contract and operating pattern. | workspace;context;verification;budget | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.15715 | [2607.15715] Behavioral Controllability of Agentic Models for Information Extraction: From Fixed Workflows to Reflective Agents | Large language model (LLM) agents are increasingly used for complex information-extraction tasks, yet it remains unclear whether agentic components such as reflection and memory lead to observable and controllable improvements over fixed LLM workflows. We study this question through conference-paper dataset extraction,... | Lujia Zhang; Xingzhou Chen; Hongwei Feng | 2026-07-17 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.15715 | 2026-08-13T14:24:04 | ||||||||
ale-0249 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports | https://arxiv.org/abs/2607.15684 | external | arxiv.org | Manually analyzes 255 issue reports from Codex, Gemini CLI, LangChain, and CrewAI to classify bugs triggered by an interaction between model output and harness behavior; silent failures, weak test oracles, and stochastic reproduction motivate dedicated replay and fault-localization support. | Manually analyzes 255 issue reports from Codex, Gemini CLI, LangChain, and CrewAI to classify bugs triggered by an interaction between model output and harness behavior; silent failures, weak test oracles, and stochastic reproduction motivate dedicated replay and fault-localization support. | Manually analyzes 255 issue reports from Codex, Gemini CLI, LangChain, and CrewAI to classify bugs triggered by an interaction between model output and harness behavior; silent failures, weak test oracles, and stochastic reproduction motivate dedicated replay and fault-localization support. | Durable execution and replay are treated as first-class loop infrastructure. Manually analyzes 255 issue reports from Codex, Gemini CLI, LangChain, and CrewAI to classify bugs triggered by an interaction between model output and harness behavior; silent failures, weak test oracles, and stochastic reproduction motivate ... | Use Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.15684; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 871 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L871 | 2026-07-20 | Design | design | Specify a loop contract and operating pattern. | trigger;intake;verification;state | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.15684 | [2607.15684] Understanding Agent-Reactive Bugs at the Model-Harness Boundary: An Empirical Study of LLM Agent Issue Reports | LLM agents span command-line interfaces (e.g., Codex) and agent frameworks (e.g., LangChain), integrating backend LLMs with harness code that parses model outputs, controls agent loops, and manages context. Both the harness and LLM-generated responses jointly shape an agent's execution. This architecture gives rise to ... | Jingyi Chen; Songqiang Chen; Hengcheng Zhu; Jialun Cao; Jiasi Shen; Shing-Chi Cheung | 2026-07-17 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.15684 | 2026-08-13T14:24:04 | ||||||||
ale-0250 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery | https://arxiv.org/abs/2607.16038 | external | arxiv.org | Organizes long-running scientific work around goal-scoped review gates, domain translators, an agent runtime and workflow engine, and an evidence DAG that links claims to provenance; eight end-to-end cases include multi-day research sprints, while deeper team collaboration remains planned work. | Organizes long-running scientific work around goal-scoped review gates, domain translators, an agent runtime and workflow engine, and an evidence DAG that links claims to provenance; eight end-to-end cases include multi-day research sprints, while deeper team collaboration remains planned work. | Organizes long-running scientific work around goal-scoped review gates, domain translators, an agent runtime and workflow engine, and an evidence DAG that links claims to provenance; eight end-to-end cases include multi-day research sprints, while deeper team collaboration remains planned work. | Control flow is represented as an inspectable graph rather than an opaque prompt loop. Organizes long-running scientific work around goal-scoped review gates, domain translators, an agent runtime and workflow engine, and an evidence DAG that links claims to provenance; eight end-to-end cases include multi-day research ... | Use SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.16038; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 872 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L872 | 2026-07-20 | Design | design | Specify a loop contract and operating pattern. | objective;intake | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.16038 | [2607.16038] SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery | Scientific work increasingly spans heterogeneous artifacts -- papers, code, datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions -- yet general-purpose AI assistants rarely preserve these objects as a coherent, auditable research state. We present SciForge, a multimodal research-na... | SciForge Team; Zhangyang Gao; Minghao Fang; Yifei Liu; Hanhui Yang; Xinyu Gu; Shixiang Tang; Siqi Sun; Lei Bai; Cheng Tan; Mengdi Liu; Hao Wu; Shuizhou Chen | 2026-07-17 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.16038 | 2026-08-13T14:24:04 | ||||||||
ale-0251 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | Coding Agents 2.0: Interface, Inference, and Verification | https://www.gradient.com/blog/posts/coding-agents-2/ | external | www.gradient.com | Argues the coding-agent bottleneck has shifted from generation to three infrastructure problems: interfaces for parallel agent fleets, a coding-optimized inference stack with prefix caching, and verification layers built on formal proofs and trace diffing. | Argues the coding-agent bottleneck has shifted from generation to three infrastructure problems: interfaces for parallel agent fleets, a coding-optimized inference stack with prefix caching, and verification layers built on formal proofs and trace diffing. | Argues the coding-agent bottleneck has shifted from generation to three infrastructure problems: interfaces for parallel agent fleets, a coding-optimized inference stack with prefix caching, and verification layers built on formal proofs and trace diffing. | Verification is promoted from a final check to a loop-control signal. Argues the coding-agent bottleneck has shifted from generation to three infrastructure problems: interfaces for parallel agent fleets, a coding-optimized inference stack with prefix caching, and verification layers built on formal proofs and trace di... | Use Coding Agents 2.0: Interface, Inference, and Verification to turn a recurring-agent idea into an explicit loop contract. | Contextual source from www.gradient.com; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 873 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L873 | 2026-07-22 | Design | design | Specify a loop contract and operating pattern. | verification | builder | workflow | enabling | practitioner-analysis | B | ok | https://www.gradient.com/blog/posts/coding-agents-2/ | Coding Agents 2.0: Interface, Inference, and Verification | Gradient Ventures The coding agent stack — Market map | In under a year, writing software became the biggest thing people do with large language models. The revenue followed the tokens: Cursor reached about $4B in annualized revenue in under four years and recently <a href="https://techcrunch.com/2026/04/22/how-spacex-preempted-a-2b-fundraise-with-a-60b-buyout-offer/">agree... | Gradient Ventures | html-meta | 2026-08-13T14:24:04 | ||||||||||||||
ale-0252 | Agent Workflow Patterns | agent-workflow-patterns | Blog | 📝 | Towards a Harness That Can Do Anything | https://eardatasci.github.io/c/ambiance/index.html | external | eardatasci.github.io | Essay on designing a general-purpose agent harness, examining what capability boundaries, tool surfaces, and verification hooks a do-anything harness actually requires. | Essay on designing a general-purpose agent harness, examining what capability boundaries, tool surfaces, and verification hooks a do-anything harness actually requires. | Essay on designing a general-purpose agent harness, examining what capability boundaries, tool surfaces, and verification hooks a do-anything harness actually requires. | Verification is promoted from a final check to a loop-control signal. Essay on designing a general-purpose agent harness, examining what capability boundaries, tool surfaces, and verification hooks a do-anything harness actually requires. | Use Towards a Harness That Can Do Anything to turn a recurring-agent idea into an explicit loop contract. | Contextual source from eardatasci.github.io; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 874 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L874 | 2026-07-22 | Design | design | Specify a loop contract and operating pattern. | workspace;verification | builder | workflow | enabling | practitioner-analysis | B | ok | https://eardatasci.github.io/c/ambiance/index.html | arda tasci | eardatasci.github.io | domain-fallback | 2026-08-13T14:24:04 | |||||||||||||||
ale-0253 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | NVIDIA-labs OO Agents: Native Python Object-Oriented Agents | https://arxiv.org/abs/2607.20709 | external | arxiv.org | NVIDIA's model-agnostic framework (NOOA) where an agent is a Python object: methods are actions, fields are state, docstrings are prompts, and type annotations are contracts; a method body of '...' is completed at runtime by an LLM-driven agent loop while normal bodies stay deterministic, letting agent behavior be test... | NVIDIA's model-agnostic framework (NOOA) where an agent is a Python object: methods are actions, fields are state, docstrings are prompts, and type annotations are contracts; a method body of '...' is completed at runtime by an LLM-driven agent loop while normal bodies stay deterministic, letting agent behavior be test... | NVIDIA's model-agnostic framework (NOOA) where an agent is a Python object: methods are actions, fields are state, docstrings are prompts, and type annotations are contracts; a method body of '...' is completed at runtime by an LLM-driven agent loop while normal bodies stay deterministic, letting agent behavior be test... | State persistence is explicit enough for repeated runs and handoff. NVIDIA's model-agnostic framework (NOOA) where an agent is a Python object: methods are actions, fields are state, docstrings are prompts, and type annotations are contracts; a method body of '...' is completed at runtime by an LLM-driven agent loop wh... | Use NVIDIA-labs OO Agents: Native Python Object-Oriented Agents to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.20709; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 875 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L875 | 2026-07-24 | Design | design | Specify a loop contract and operating pattern. | verification;state | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.20709 | [2607.20709] NVIDIA-labs OO Agents: Native Python Object-Oriented Agents | Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agents (NOOA), a model-agnostic Python framework for building reliable AI agents. NOOA takes a simpler approach: an agent is a Python object. Its methods are the actions th... | Paul Furgale; Severin Klingler; James Nolan; Matt Staats; Gaia Di Lorenzo; Elisa Martinez Abad; Christian Schüller; Razvan Dinu; Alessio Devoto; Pascal Berard; Gal Kaplun; Elad Sarafian; Riccardo Roveri; Leon Derczynski; Ricardo Silveira Cabral | 2026-07-22 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.20709 | 2026-08-13T14:24:04 | ||||||||
ale-0254 | Agent Workflow Patterns | agent-workflow-patterns | Tool | 🧰 | deer-workflow | https://github.com/deerwork-ai/deer-workflow | external | github.com | Code-first runtime that keeps orchestration and control flow in TypeScript while delegating only the semantic work to replaceable agent runtimes (Codex CLI and Claude supported out of the box). Emits an observable JSONL event stream for downstream automation and ships an interactive TUI for watching execution. | Code-first runtime that keeps orchestration and control flow in TypeScript while delegating only the semantic work to replaceable agent runtimes (Codex CLI and Claude supported out of the box). Emits an observable JSONL event stream for downstream automation and ships an interactive TUI for watching execution. | Code-first runtime that keeps orchestration and control flow in TypeScript while delegating only the semantic work to replaceable agent runtimes (Codex CLI and Claude supported out of the box). Emits an observable JSONL event stream for downstream automation and ships an interactive TUI for watching execution. | Orchestration and control flow are made explicit and inspectable. Code-first runtime that keeps orchestration and control flow in TypeScript while delegating only the semantic work to replaceable agent runtimes (Codex CLI and Claude supported out of the box). Emits an observable JSONL event stream for downstream automa... | Use deer-workflow to turn a recurring-agent idea into an explicit loop contract. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 876 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L876 | 2026-07-28 | Design | design | Specify a loop contract and operating pattern. | delegation | builder | workflow | enabling | source-implementation | A | ok | https://github.com/deerwork-ai/deer-workflow | GitHub - deerwork-ai/deer-workflow: An open-source graph engineering runtime that keeps orchestration in TypeScript and delegates semantic work to replaceable Agent runtimes. · GitHub | An open-source graph engineering runtime that keeps orchestration in TypeScript and delegates semantic work to replaceable Agent runtimes. - deerwork-ai/deer-workflow | deerwork-ai/deer-workflow | GitHub | html-meta | deerwork-ai/deer-workflow | 2026-08-13T14:24:04 | ||||||||||||
ale-0255 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Living-Harness Is an Interactive-Agent Evolver | https://arxiv.org/abs/2607.26598 | external | arxiv.org | Converts each completed trajectory and its evaluator signals into posterior evidence for bounded harness updates, so the scaffold itself evolves via episodic memory and state graphs instead of staying static. Directly operationalizes the self-improving-loop premise: the harness is the learned artifact, not the weights. | Converts each completed trajectory and its evaluator signals into posterior evidence for bounded harness updates, so the scaffold itself evolves via episodic memory and state graphs instead of staying static. Directly operationalizes the self-improving-loop premise: the harness is the learned artifact, not the weights. | Converts each completed trajectory and its evaluator signals into posterior evidence for bounded harness updates, so the scaffold itself evolves via episodic memory and state graphs instead of staying static. Directly operationalizes the self-improving-loop premise: the harness is the learned artifact, not the weights. | Control flow is represented as an inspectable graph rather than an opaque prompt loop. Converts each completed trajectory and its evaluator signals into posterior evidence for bounded harness updates, so the scaffold itself evolves via episodic memory and state graphs instead of staying static. Directly operationalizes... | Use Living-Harness Is an Interactive-Agent Evolver to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.26598; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 877 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L877 | 2026-07-30 | Design | design | Specify a loop contract and operating pattern. | context;state | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.26598 | [2607.26598] Living-Harness Is an Interactive-Agent Evolver | Large language model (LLM) agents may recover from a failure within an episode or after a retry, yet the same execution failure can recur in later tasks because post-episode feedback rarely revises the persistent harness that guides future interactions. Static harnesses improve reliability through fixed tools, context,... | Yuetian Du; Yucheng Wang; He Xu; Jiexu Xu; Shanwen Tan; Bing Zhao; Boyu Yang; Zhijie Xu; Ming Kong; Hu Wei; Jie Liu; Qiang Zhu | 2026-07-29 | 2026 | arXiv | arXiv | cs.MA | arxiv-api | 2607.26598 | 2026-08-13T14:24:04 | ||||||||
ale-0256 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | CHILL-Harness: Counterfactual Harness Learning for Efficient Reasoning in Long-Horizon Agents | https://arxiv.org/abs/2607.25825 | external | arxiv.org | Uses counterfactual causal learning to let an agent's harness adapt orchestration per task and environment, cutting long-horizon compute while holding success rate. One of the first papers to treat harness configuration as a learnable causal policy rather than a hand-tuned constant. | Uses counterfactual causal learning to let an agent's harness adapt orchestration per task and environment, cutting long-horizon compute while holding success rate. One of the first papers to treat harness configuration as a learnable causal policy rather than a hand-tuned constant. | Uses counterfactual causal learning to let an agent's harness adapt orchestration per task and environment, cutting long-horizon compute while holding success rate. One of the first papers to treat harness configuration as a learnable causal policy rather than a hand-tuned constant. | Orchestration and control flow are made explicit and inspectable. Uses counterfactual causal learning to let an agent's harness adapt orchestration per task and environment, cutting long-horizon compute while holding success rate. One of the first papers to treat harness configuration as a learnable causal policy rathe... | Use CHILL-Harness: Counterfactual Harness Learning for Efficient Reasoning in Long-Horizon Agents to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.25825; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 878 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L878 | 2026-07-30 | Design | design | Specify a loop contract and operating pattern. | delegation | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.25825 | [2607.25825] CHILL-Harness: Counterfactual Harness Learning for Efficient Reasoning in Long-Horizon Agents | Agent harnesses have become the operational infrastructure of modern large language model agents, coordinating context, tools, verification, and execution control to translate latent model capability into reliable long-horizon behavior. However, reliable long-horizon behavior requires harness control to adapt to task d... | Jiarun Fu; Lizhong Ding; Sida Chen; Honglei Xin; Chunhui Zhang; Pengqi Li; Qiuning Wei; Ye Yuan; Guoren Wang | 2026-07-28 | 2026 | arXiv | arXiv | cs.MA | arxiv-api | 2607.25825 | 2026-08-13T14:24:04 | ||||||||
ale-0257 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | A Control System, a Dataset, and a Recipe for Making Frozen LLM Agents Learn a Domain | https://arxiv.org/abs/2607.25415 | external | arxiv.org | RL-optimizes the harness around a frozen model by treating it as a fixed action space rather than allowing unconstrained self-modification, evaluated across tool-use workflows, code generation, and retrieval QA with released code and data. The bounded-action-space framing is the safety-relevant counterpoint to open-end... | RL-optimizes the harness around a frozen model by treating it as a fixed action space rather than allowing unconstrained self-modification, evaluated across tool-use workflows, code generation, and retrieval QA with released code and data. The bounded-action-space framing is the safety-relevant counterpoint to open-end... | RL-optimizes the harness around a frozen model by treating it as a fixed action space rather than allowing unconstrained self-modification, evaluated across tool-use workflows, code generation, and retrieval QA with released code and data. The bounded-action-space framing is the safety-relevant counterpoint to open-end... | Packages the evidence as queryable CSV and JSONL rather than only a rendered page. RL-optimizes the harness around a frozen model by treating it as a fixed action space rather than allowing unconstrained self-modification, evaluated across tool-use workflows, code generation, and retrieval QA with released code and dat... | Use A Control System, a Dataset, and a Recipe for Making Frozen LLM Agents Learn a Domain to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.25415; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 879 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L879 | 2026-07-30 | Design | design | Specify a loop contract and operating pattern. | workspace;context | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.25415 | [2607.25415] A Control System, a Dataset, and a Recipe for Making Frozen LLM Agents Learn a Domain | Production LLM agents are increasingly assembled from a frozen model wrapped in a harness: a prompt template, a tool set, a memory/retrieval layer, a planning strategy, and a verification policy. Two 2026 systems, Meta-Harness (Lee et al., 2026) and HyperAgents (Meta AI, 2026), show that this harness can itself be opti... | Debjyoti Paul | 2026-07-28 | 2026 | arXiv | arXiv | 8 pages, 1 figure, 3 tables. Code and dataset: https://github.com/dpaul0501/context-optimization-rl | cs.AI | arxiv-api | 2607.25415 | 2026-08-13T14:24:04 | |||||||
ale-0258 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents | https://arxiv.org/abs/2607.27083 | external | arxiv.org | Frames how many tools an agent should acquire as an optimal-stopping problem, training on the gap between stopping now and optimal continuation and proving the objective aligns with the stopping target under heterogeneous costs. Directly addresses when to halt, one of the least-formalized parts of loop design. | Frames how many tools an agent should acquire as an optimal-stopping problem, training on the gap between stopping now and optimal continuation and proving the objective aligns with the stopping target under heterogeneous costs. Directly addresses when to halt, one of the least-formalized parts of loop design. | Frames how many tools an agent should acquire as an optimal-stopping problem, training on the gap between stopping now and optimal continuation and proving the objective aligns with the stopping target under heterogeneous costs. Directly addresses when to halt, one of the least-formalized parts of loop design. | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Frames how many tools an agent should acquire as an optimal-stopping problem, training on the gap between stopping now and optimal continuation and proving the objective aligns with the stopping target under heterogeneous cost... | Use Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.27083; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 880 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L880 | 2026-07-30 | Design | design | Specify a loop contract and operating pattern. | objective;workspace;budget;exit | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.27083 | [2607.27083] Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents | As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure. Routers and retrievers can ... | Yicheng Feng; Yan Zhang; Yan Cheng; Wei Qi | 2026-07-29 | 2026 | arXiv | arXiv | cs.LG | arxiv-api | 2607.27083 | 2026-08-13T14:24:04 | ||||||||
ale-0259 | Agent Workflow Patterns | agent-workflow-patterns | Tool | 🧰 | Skill Recorder | https://github.com/microsoft/skill-recorder | external | github.com | Microsoft's tool for authoring loops from demonstration instead of from prose. It captures a real work session locally, clicks, app and window switches, pages visited, optional spoken narration, then uses GitHub Copilot CLI to reconstruct what you actually did as one intent plus an ordered step list you review and edit... | Microsoft's tool for authoring loops from demonstration instead of from prose. It captures a real work session locally, clicks, app and window switches, pages visited, optional spoken narration, then uses GitHub Copilot CLI to reconstruct what you actually did as one intent plus an ordered step list you review and edit... | Microsoft's tool for authoring loops from demonstration instead of from prose. It captures a real work session locally, clicks, app and window switches, pages visited, optional spoken narration, then uses GitHub Copilot CLI to reconstruct what you actually did as one intent plus an ordered step list you review and edit... | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Microsoft's tool for authoring loops from demonstration instead of from prose. It captures a real work session locally, clicks, app and window switches, pages visited, optional spoken narration, then uses GitHub Copilot CLI to... | Use Skill Recorder to turn a recurring-agent idea into an explicit loop contract. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 881 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L881 | 2026-08-02 | Design | design | Specify a loop contract and operating pattern. | workspace | builder | workflow | enabling | source-implementation | A | ok | https://github.com/microsoft/skill-recorder | GitHub - microsoft/skill-recorder: Desktop app that records your on-screen work session and uses the GitHub Copilot CLI to reconstruct it as an intent + ordered steps, then builds a reusable Skill or Automation for Microsoft Scout, Microsoft Copilot Cowork, or Copilot Studio. · GitHub | Desktop app that records your on-screen work session and uses the GitHub Copilot CLI to reconstruct it as an intent + ordered steps, then builds a reusable Skill or Automation for Microsoft Scout, Microsoft Copilot Cowork, or Copilot Studio. - microsoft/skill-recorder | microsoft/skill-recorder | GitHub | html-meta | microsoft/skill-recorder | 2026-08-13T14:24:04 | ||||||||||||
ale-0260 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | SIGIL: Compiling Agent Skills into Typed Harnesses | https://arxiv.org/abs/2607.27309 | external | arxiv.org | Identifies that prose skill files force an agent to re-derive control flow on every run, then introduces 'skill compilation', lowering prose skills into executable harnesses through a typed IR (AG-IR). Reports 86% vs 56% of mandated steps executed, 2.3x more completed procedures, and 42% fewer tokens, stable across mod... | Identifies that prose skill files force an agent to re-derive control flow on every run, then introduces 'skill compilation', lowering prose skills into executable harnesses through a typed IR (AG-IR). Reports 86% vs 56% of mandated steps executed, 2.3x more completed procedures, and 42% fewer tokens, stable across mod... | Identifies that prose skill files force an agent to re-derive control flow on every run, then introduces 'skill compilation', lowering prose skills into executable harnesses through a typed IR (AG-IR). Reports 86% vs 56% of mandated steps executed, 2.3x more completed procedures, and 42% fewer tokens, stable across mod... | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Identifies that prose skill files force an agent to re-derive control flow on every run, then introduces 'skill compilation', lowering prose skills into executable harnesses through a typed IR (AG-IR). Reports 86% vs 56% of ma... | Use SIGIL: Compiling Agent Skills into Typed Harnesses to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.27309; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 882 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L882 | 2026-08-02 | Design | design | Specify a loop contract and operating pattern. | budget | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.27309 | [2607.27309] SIGIL: Compiling Agent Skills into Typed Harnesses | AI-Integrated agents increasingly acquire capability from skills: prose procedure files loaded into a model's context and run by a tool-calling loop. A skill is described to the runtime but never encoded in it, so the model re-derives its control flow on every run and may skip mandated verification. Across 30 skills an... | Jayanaka Dantanarayana; Savini Kashmira; Lingjia Tang; Jason Mars | 2026-07-29 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.27309 | 2026-08-13T14:24:04 | ||||||||
ale-0261 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems | https://arxiv.org/abs/2607.29241 | external | arxiv.org | Identifies a concrete instability in self-improving loops: when the LLM both picks the direction of a modification and generates the concrete hypothesis, search becomes unstable under limited experiment budgets. | Identifies a concrete instability in self-improving loops: when the LLM both picks the direction of a modification and generates the concrete hypothesis, search becomes unstable under limited experiment budgets. | Identifies a concrete instability in self-improving loops: when the LLM both picks the direction of a modification and generates the concrete hypothesis, search becomes unstable under limited experiment budgets. | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Identifies a concrete instability in self-improving loops: when the LLM both picks the direction of a modification and generates the concrete hypothesis, search becomes unstable under limited experiment budgets. | Use RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.29241; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 883 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L883 | 2026-08-05 | Design | design | Specify a loop contract and operating pattern. | budget | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.29241 | [2607.29241] RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems | Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes. While LLM-based agents can automate this trial-and-error process, allowing the LLM to both select modification directions and generate concrete hypotheses often leads ... | Haoran Ling; Yuecheng Li; Zeyu Song; Jing Yao; Shuwen Kang; Chi Lu; Wenjin Wu; Peng Jiang | 2026-07-31 | 2026 | arXiv | arXiv | 9 pages, 2 figures | cs.IR | arxiv-api | 2607.29241 | 2026-08-13T14:24:04 | |||||||
ale-0262 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution | https://arxiv.org/abs/2607.26722 | external | arxiv.org | Attacks the instability of harness self-evolution by anchoring evidence at function level, recalibrating which historical insights still apply given current state, and distilling role-conditioned search intent. Answers the practical question of when a past lesson should still steer the loop rather than mislead it. | Attacks the instability of harness self-evolution by anchoring evidence at function level, recalibrating which historical insights still apply given current state, and distilling role-conditioned search intent. Answers the practical question of when a past lesson should still steer the loop rather than mislead it. | Attacks the instability of harness self-evolution by anchoring evidence at function level, recalibrating which historical insights still apply given current state, and distilling role-conditioned search intent. Answers the practical question of when a past lesson should still steer the loop rather than mislead it. | State persistence is explicit enough for repeated runs and handoff. Attacks the instability of harness self-evolution by anchoring evidence at function level, recalibrating which historical insights still apply given current state, and distilling role-conditioned search intent. Answers the practical question of when a ... | Use DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.26722; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 884 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L884 | 2026-08-05 | Design | design | Specify a loop contract and operating pattern. | state | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.26722 | [2607.26722] DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution | Harness plays a critical role in large language model agent performance, and building a high-performing harness requires substantial expert effort. Therefore, recent research has increasingly explored harness self-evolution, which iteratively proposes, evaluates, and improves harnesses using historical trial experience... | Hanghui Guo; Weijie Shi; Zhangze Chen; Shengxiang Xu; Yishu Wang; Yimei Zhang; Wangze Ni; Jia Zhu; Shimin Di | 2026-07-29 | 2026 | arXiv | arXiv | 9 pages | cs.MA | arxiv-api | 2607.26722 | 2026-08-13T14:24:04 | |||||||
ale-0263 | Agent Workflow Patterns | agent-workflow-patterns | Paper | 📄 | SkillMentor: LLM Agent Self-Evolution via Learning Blind-Spot Diagnosis | https://arxiv.org/abs/2607.27360 | external | arxiv.org | Treats blind-spot discovery as a learnable capability rather than assuming failures are already surfaced: an RL-trained Mentor policy generates diagnostic probe tasks, clusters recurring failure patterns, and converts them into reusable corrective skills without touching the executor or needing human labels. | Treats blind-spot discovery as a learnable capability rather than assuming failures are already surfaced: an RL-trained Mentor policy generates diagnostic probe tasks, clusters recurring failure patterns, and converts them into reusable corrective skills without touching the executor or needing human labels. | Treats blind-spot discovery as a learnable capability rather than assuming failures are already surfaced: an RL-trained Mentor policy generates diagnostic probe tasks, clusters recurring failure patterns, and converts them into reusable corrective skills without touching the executor or needing human labels. | Distills reusable agent-control patterns that are not tied to a single vendor implementation. Treats blind-spot discovery as a learnable capability rather than assuming failures are already surfaced: an RL-trained Mentor policy generates diagnostic probe tasks, clusters recurring failure patterns, and converts them int... | Use SkillMentor: LLM Agent Self-Evolution via Learning Blind-Spot Diagnosis to turn a recurring-agent idea into an explicit loop contract. | Research source arXiv:2607.27360; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 885 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L885 | 2026-08-05 | Design | design | Specify a loop contract and operating pattern. | intake;escalation | researcher;evaluator | workflow | enabling | research-preprint | A | ok | https://arxiv.org/abs/2607.27360 | [2607.27360] SkillMentor: LLM Agent Self-Evolution via Learning Blind-Spot Diagnosis | Agent self-evolution has primarily focused on learning how to act, while overlooking an equally important capability: learning to discover what an agent does not know. Existing approaches typically assume that failure discovery is given, focusing on how to repair failures once they are identified. We ask whether blind-... | Xiaoyi Bao; Yuanzhen Xie; Yunzhi Tan; Jinghang Gu; Zhongqing Wang; Chu-Ren Huang; Bo Hu; Zang Li | 2026-07-29 | 2026 | arXiv | arXiv | cs.AI | arxiv-api | 2607.27360 | 2026-08-13T14:24:04 | ||||||||
ale-0264 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | SWE-agent | https://github.com/SWE-agent/SWE-agent | external | github.com | Agent-computer interface and autonomous software engineering agent for repository tasks. | Agent-computer interface and autonomous software engineering agent for repository tasks. | Agent-computer interface and autonomous software engineering agent for repository tasks. | Uses real automated software-engineering systems as evidence for practical loop architectures. Agent-computer interface and autonomous software engineering agent for repository tasks. | Use SWE-agent to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 894 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L894 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | builder | agent | direct | source-implementation | A | ok | https://github.com/SWE-agent/SWE-agent | GitHub - SWE-agent/SWE-agent: SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024] · GitHub | SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024] - GitHub - SWE-agent/SWE-agent: SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It ca... | SWE-agent/SWE-agent | GitHub | html-meta | SWE-agent/SWE-agent | 2026-08-13T14:24:04 | |||||||||||||
ale-0265 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering | https://arxiv.org/abs/2405.15793 | external | arxiv.org | Paper behind SWE-agent and its interface design. | Paper behind SWE-agent and its interface design. | Paper behind SWE-agent and its interface design. | Uses real automated software-engineering systems as evidence for practical loop architectures. Paper behind SWE-agent and its interface design. | Use SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering to choose an implementation surface for repeatable agent work. | Research source arXiv:2405.15793; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 895 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L895 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | researcher;evaluator | agent | direct | research-paper | A | ok | https://proceedings.neurips.cc/paper_files/paper/2024/hash/5a7c947568c1b1328ccc5230172e1e7c-Abstract-Conference.html | [2405.15793] SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering | Language model (LM) agents are increasingly being used to automate complicated tasks in digital environments. Just as humans benefit from powerful software applications, such as integrated development environments, for complex tasks like software engineering, we posit that LM agents represent a new category of end user... | John Yang; Carlos E. Jimenez; Alexander Wettig; Kilian Lieret; Shunyu Yao; Karthik Narasimhan; Ofir Press | 2024 | 2024 | Advances in Neural Information Processing Systems 37 (NeurIPS) | Neural Information Processing Systems Foundation | 10.52202/079017-1601 | Published in Advances in Neural Information Processing Systems 37 (NeurIPS); the linked arXiv record remains available for open access. | cs.SE | NeurIPS proceedings and DOI records | 2405.15793 | 2026-08-13T14:24:04 | |||||||
ale-0266 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | mini-SWE-agent | https://mini-swe-agent.com/latest/ | external | mini-swe-agent.com | Minimal coding agent that is useful for understanding the core loop without a large framework. | Minimal coding agent that is useful for understanding the core loop without a large framework. | Minimal coding agent that is useful for understanding the core loop without a large framework. | Uses real automated software-engineering systems as evidence for practical loop architectures. Minimal coding agent that is useful for understanding the core loop without a large framework. | Use mini-SWE-agent to choose an implementation surface for repeatable agent work. | Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption. | high | README.md | 896 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L896 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | builder | agent | direct | implementation | A | ok | https://mini-swe-agent.com/latest/ | Overview - mini-SWE-agent documentation | mini-swe-agent.com | domain-fallback | 2026-08-13T14:24:04 | ||||||||||||||||
ale-0267 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | OpenHands | https://github.com/All-Hands-AI/OpenHands | external | github.com | Open platform for AI software developers as generalist agents. | Open platform for AI software developers as generalist agents. | Open platform for AI software developers as generalist agents. | Uses real automated software-engineering systems as evidence for practical loop architectures. Open platform for AI software developers as generalist agents. | Use OpenHands to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 897 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L897 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | builder | agent | direct | source-implementation | A | ok | https://github.com/OpenHands/OpenHands | GitHub - OpenHands/OpenHands: 🙌 OpenHands: AI-Driven Development · GitHub | 🙌 OpenHands: AI-Driven Development. Contribute to OpenHands/OpenHands development by creating an account on GitHub. | All-Hands-AI/OpenHands | GitHub | html-meta | All-Hands-AI/OpenHands | 2026-08-13T14:24:04 | |||||||||||||
ale-0268 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | OpenHands: An Open Platform for AI Software Developers as Generalist Agents | https://arxiv.org/abs/2407.16741 | external | arxiv.org | Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation. | Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation. | Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation. | Evaluation data is used as the feedback signal for improving loop behavior. Paper describing OpenHands, CodeActAgent, benchmarks, and generalist agent evaluation. | Use OpenHands: An Open Platform for AI Software Developers as Generalist Agents to choose an implementation surface for repeatable agent work. | Research source arXiv:2407.16741; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 898 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L898 | Build | build | Choose runtimes, tools, and delegation surfaces. | verification | researcher;evaluator | agent | direct | research-paper | A | ok | https://proceedings.iclr.cc/paper_files/paper/2025/hash/a4b6ad6b48850c0c331d1259fc66a69c-Abstract-Conference.html | [2407.16741] OpenHands: An Open Platform for AI Software Developers as Generalist Agents | Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and af... | Xingyao Wang; Boxuan Li; Yufan Song; Frank F. Xu; Xiangru Tang; Mingchen Zhuge; Jiayi Pan; Yueqi Song; Bowen Li; Jaskirat Singh; Hoang H. Tran; Fuqiang Li; Ren Ma; Mingzhang Zheng; Bill Qian; Yanjun Shao; Niklas Muennighoff; Yizhe Zhang; Binyuan Hui; Junyang Lin; Robert Brennan; Hao Peng; Heng Ji; Graham Neubig | 2025 | 2025 | International Conference on Learning Representations (ICLR) | International Conference on Learning Representations | Published in International Conference on Learning Representations (ICLR); the linked arXiv record remains available for open access. | cs.SE | ICLR proceedings record | 2407.16741 | 2026-08-13T14:24:04 | ||||||||
ale-0269 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | Agentless | https://github.com/OpenAutoCoder/Agentless | external | github.com | Workflow-based approach for software issue resolution using localization, repair, and patch validation. | Workflow-based approach for software issue resolution using localization, repair, and patch validation. | Workflow-based approach for software issue resolution using localization, repair, and patch validation. | Uses real automated software-engineering systems as evidence for practical loop architectures. Workflow-based approach for software issue resolution using localization, repair, and patch validation. | Use Agentless to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 899 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L899 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake | builder | agent | direct | source-implementation | A | ok | https://github.com/OpenAutoCoder/Agentless | GitHub - OpenAutoCoder/Agentless: Agentless🐱: an agentless approach to automatically solve software development problems · GitHub | Agentless🐱: an agentless approach to automatically solve software development problems - OpenAutoCoder/Agentless | OpenAutoCoder/Agentless | GitHub | html-meta | OpenAutoCoder/Agentless | 2026-08-13T14:24:04 | |||||||||||||
ale-0270 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | Agentless: Demystifying LLM-based Software Engineering Agents | https://arxiv.org/abs/2407.01489 | external | arxiv.org | Useful contrast case: strong results through structured workflow rather than a fully open-ended agent. | Useful contrast case: strong results through structured workflow rather than a fully open-ended agent. | Useful contrast case: strong results through structured workflow rather than a fully open-ended agent. | Uses real automated software-engineering systems as evidence for practical loop architectures. Useful contrast case: strong results through structured workflow rather than a fully open-ended agent. | Use Agentless: Demystifying LLM-based Software Engineering Agents to choose an implementation surface for repeatable agent work. | Research source arXiv:2407.01489; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 900 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L900 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | researcher;evaluator | agent | direct | research-preprint | A | ok | https://arxiv.org/abs/2407.01489 | [2407.01489] Agentless: Demystifying LLM-based Software Engineering Agents | Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software d... | Chunqiu Steven Xia; Yinlin Deng; Soren Dunn; Lingming Zhang | 2024-07-01 | 2024 | arXiv | arXiv | cs.SE | arxiv-api | 2407.01489 | 2026-08-13T14:24:04 | |||||||||
ale-0271 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | AutoCodeRover | https://github.com/AutoCodeRoverSG/auto-code-rover | external | github.com | Autonomous program improvement system for issue localization, patch generation, and validation. | Autonomous program improvement system for issue localization, patch generation, and validation. | Autonomous program improvement system for issue localization, patch generation, and validation. | Uses real automated software-engineering systems as evidence for practical loop architectures. Autonomous program improvement system for issue localization, patch generation, and validation. | Use AutoCodeRover to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 901 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L901 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake | builder | agent | direct | source-implementation | A | ok | https://github.com/AutoCodeRoverSG/auto-code-rover | GitHub - AutoCodeRoverSG/auto-code-rover: A project structure aware autonomous software engineer aiming for autonomous program improvement. Resolved 37.3% tasks (pass@1) in SWE-bench lite and 46.2% tasks (pass@1) in SWE-bench verified with each task costs less than $0.7. · GitHub | A project structure aware autonomous software engineer aiming for autonomous program improvement. Resolved 37.3% tasks (pass@1) in SWE-bench lite and 46.2% tasks (pass@1) in SWE-bench verified with each task costs less than $0.7. - AutoCodeRoverSG/auto-code-rover | AutoCodeRoverSG/auto-code-rover | GitHub | html-meta | AutoCodeRoverSG/auto-code-rover | 2026-08-13T14:24:04 | |||||||||||||
ale-0272 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | AutoCodeRover: Autonomous Program Improvement | https://arxiv.org/abs/2404.05427 | external | arxiv.org | Paper on autonomous code repair loops over real repositories. | Paper on autonomous code repair loops over real repositories. | Paper on autonomous code repair loops over real repositories. | Uses real automated software-engineering systems as evidence for practical loop architectures. Paper on autonomous code repair loops over real repositories. | Use AutoCodeRover: Autonomous Program Improvement to choose an implementation surface for repeatable agent work. | Research source arXiv:2404.05427; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 902 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L902 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | researcher;evaluator | agent | direct | research-paper | A | ok | https://doi.org/10.1145/3650212.3680384 | [2404.05427] AutoCodeRover: Autonomous Program Improvement | Researchers have made significant progress in automating the software development process in the past decades. Recent progress in Large Language Models (LLMs) has significantly impacted the development process, where developers can use LLM-based programming assistants to achieve automated coding. Nevertheless, software... | Yuntong Zhang; Haifeng Ruan; Zhiyu Fan; Abhik Roychoudhury | 2024-09-11 | 2024 | Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) | Association for Computing Machinery | 10.1145/3650212.3680384 | Published in Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA); the linked arXiv record remains available for open access. | cs.SE | ACM DOI record | 2404.05427 | 2026-08-13T14:24:04 | |||||||
ale-0273 | Coding-Agent Loop Systems | coding-agent-loop-systems | List | 🧭 | SWE-bench reading list | https://github.com/SWE-bench/reading-list | external | github.com | Maintained map of software engineering agent systems and related papers. | Maintained map of software engineering agent systems and related papers. | Maintained map of software engineering agent systems and related papers. | Uses real automated software-engineering systems as evidence for practical loop architectures. Maintained map of software engineering agent systems and related papers. | Use SWE-bench reading list to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 903 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L903 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | builder | agent | direct | curated-index | C | ok | https://github.com/SWE-bench/reading-list | GitHub - SWE-bench/reading-list: Academic papers and works related to SWE-bench and SWE-agents · GitHub | Academic papers and works related to SWE-bench and SWE-agents - SWE-bench/reading-list | SWE-bench/reading-list | GitHub | html-meta | SWE-bench/reading-list | 2026-08-13T14:24:04 | |||||||||||||
ale-0274 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code | https://arxiv.org/abs/2602.06875 | external | arxiv.org | ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain. | ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain. | ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain. | The work separates roles across agents, verifiers, or orchestration layers. ICSE'26 observe-analyze-repair loop with instrumentation, analysis, and repair agents, a history-learning mechanism, and a rollback to the last good state; iteration alone drives most of the gain. | Use TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code to choose an implementation surface for repeatable agent work. | Research source arXiv:2602.06875; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 904 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L904 | Build | build | Choose runtimes, tools, and delegation surfaces. | delegation;state | researcher;evaluator | agent | direct | research-paper | A | ok | https://conf.researchr.org/details/icse-2026/icse-2026-research-track/145/TraceCoder-A-Trace-Driven-Multi-Agent-Framework-for-Automated-Debugging-of-LLM-Gener | [2602.06875] TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code | Large Language Models (LLMs) often generate code with subtle but critical bugs, especially for complex tasks. Existing automated repair methods typically rely on superficial pass/fail signals, offering limited visibility into program behavior and hindering precise error localization. In addition, without a way to learn... | Jiangping Huang; Wenguang Ye; Weisong Sun; Jian Zhang; Mingyue Zhang; Yang Liu | 2026-04-12 | 2026 | Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE) | Association for Computing Machinery | 10.1145/3744916.3773187 | Published in Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE); the linked arXiv record remains available for open access. | cs.SE | ICSE program and camera-ready records | 2602.06875 | 2026-08-13T14:24:04 | |||||||
ale-0275 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase | https://arxiv.org/abs/2603.25697 | external | arxiv.org | Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions. | Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions. | Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions. | Uses real automated software-engineering systems as evidence for practical loop architectures. Production loop where an agent exercises a spec surface as a synthetic power user behind ground-truth tests and quality gates, reporting 285+ self-correcting iterations and 1,000+ merged PRs with zero detected regressions. | Use The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase to choose an implementation surface for repeatable agent work. | Research source arXiv:2603.25697; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 905 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L905 | Build | build | Choose runtimes, tools, and delegation surfaces. | verification | researcher;evaluator | agent | direct | research-preprint | A | ok | https://arxiv.org/abs/2603.25697 | [2603.25697] The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase | Code production is now a commodity; the bottleneck is knowing what to build and proving it works. We present the Kitchen Loop, a framework for autonomous, self-evolving software built on a unified trust model: (1) a specification surface enumerating what the product claims to support; (2) 'As a User x 1000', where an L... | Yannick Roy | 2026-03-26 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2603.25697 | 2026-08-13T14:24:04 | |||||||||
ale-0276 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures | https://arxiv.org/abs/2604.03515 | external | arxiv.org | Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine. | Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine. | Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine. | State persistence is explicit enough for repeated runs and handoff. Dissects 13 open-source coding-agent scaffolds and identifies five composable loop primitives (ReAct, generate-test-repair, plan-execute, retry, tree search) that real agents layer, mapping how control loop, tools, and state combine. | Use Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures to choose an implementation surface for repeatable agent work. | Research source arXiv:2604.03515; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 906 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L906 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;verification;state;budget | researcher;evaluator | agent | direct | research-preprint | A | ok | https://arxiv.org/abs/2604.03515 | [2604.03515] Inside the Scaffold: A Source-Code Taxonomy of Coding Agent Architectures | LLM-based coding agents can localize bugs, generate patches, and run tests with diminishing human oversight, yet the scaffolding code that surrounds the language model (the control loop, tool definitions, state management, and context strategy) remains poorly understood. Existing surveys classify agents by abstract cap... | Benjamin Rombaut | 2026-04-03 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2604.03515 | 2026-08-13T14:24:04 | |||||||||
ale-0277 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | A Self-Improving Coding Agent | https://arxiv.org/abs/2504.15228 | external | arxiv.org | An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop. | An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop. | An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop. | Verification is promoted from a final check to a loop-control signal. An agent that edits its own code and tools and re-runs against a benchmark, lifting itself from 17% to 53% on a SWE-bench Verified subset, a concrete self-modifying improvement loop. | Use A Self-Improving Coding Agent to choose an implementation surface for repeatable agent work. | Research source arXiv:2504.15228; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 907 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L907 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;verification | researcher;evaluator | agent | direct | research-preprint | A | ok | https://arxiv.org/abs/2504.15228 | [2504.15228] A Self-Improving Coding Agent | Recent advancements in Large Language Models (LLMs) have spurred interest in deploying LLM agents to undertake tasks in the world. LLMs are often deployed in agent systems: code that orchestrates LLM calls and provides them with tools. We demonstrate that an agent system, equipped with basic coding tools, can autonomou... | Maxime Robeyns; Martin Szummer; Laurence Aitchison | 2025-04-21 | 2025 | arXiv | arXiv | Submitted as a preprint to NeurIPS 2025 | cs.AI | arxiv-api | 2504.15228 | 2026-08-13T14:24:04 | ||||||||
ale-0278 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality | https://arxiv.org/abs/2607.03691 | external | arxiv.org | Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions. | Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions. | Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regressions from model regressions. | Context is managed as durable loop state rather than a single prompt payload. Longitudinal study of 35 Qwen Code CLI releases with the model held constant, tracing coding-agent shifts to specific scaffolding changes in system prompts, tools, context management, and reasoning loops, and separating scaffolding regression... | Use Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality to choose an implementation surface for repeatable agent work. | Research source arXiv:2607.03691; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 908 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L908 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context | researcher;evaluator | agent | direct | research-preprint | A | ok | https://arxiv.org/abs/2607.03691 | [2607.03691] Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality | Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops. While these agen... | Oussama Ben Sghaier; Hao Li; Bram Adams; Ahmed E. Hassan | 2026-07-04 | 2026 | arXiv | arXiv | cs.SE | arxiv-api | 2607.03691 | 2026-08-13T14:24:04 | |||||||||
ale-0279 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | ToFu: A White-Box, Token-Efficient Agent Harness for Researchers | https://arxiv.org/abs/2607.11423 | external | arxiv.org | MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses. | MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses. | MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-efficiency gains over existing harnesses. | Orchestration and control flow are made explicit and inspectable. MIT-licensed white-box agent harness built on the thesis that agent behavior is set by the orchestration code around the model as much as the model itself, letting researchers inspect, modify, and evaluate its orchestration logic with reported token-effi... | Use ToFu: A White-Box, Token-Efficient Agent Harness for Researchers to choose an implementation surface for repeatable agent work. | Research source arXiv:2607.11423; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 909 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L909 | 2026-07-15 | Build | build | Choose runtimes, tools, and delegation surfaces. | delegation;budget | researcher;evaluator | agent | direct | research-preprint | A | ok | https://arxiv.org/abs/2607.11423 | [2607.11423] ToFu: A White-Box, Token-Efficient Agent Harness for Researchers | Agentic coding tools present new opportunities to transform research workflows. The performance of agent systems built depends on both large language models (LLMs) and the harness around LLMs, which is the orchestration code that determines an agent's behavior. We present ToFu, an agentic harness for researchers that r... | Junhao Ruan; Yuan Ge; Bei Li; Yongjing Yin; Yuchun Fan; Xin Chen; Jingang Wang; Chenglong Wang; Jingbo Zhu; Tong Xiao | 2026-07-13 | 2026 | arXiv | arXiv | cs.CL | arxiv-api | 2607.11423 | 2026-08-13T14:24:04 | ||||||||
ale-0280 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | When Does Restricting a Coding Agent to execute_code Help? | https://arxiv.org/abs/2607.10569 | external | arxiv.org | Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition. | Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition. | Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition. | Uses real automated software-engineering systems as evidence for practical loop architectures. Regime-by-design ablation measuring when constraining a coding agent to a single execute_code action helps or hurts, separating task regime from agent design so harness choices can be made from evidence rather than intuition. | Use When Does Restricting a Coding Agent to execute_code Help? to choose an implementation surface for repeatable agent work. | Research source arXiv:2607.10569; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 910 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L910 | 2026-07-15 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | researcher;evaluator | agent | direct | research-paper | A | ok | https://arxiv.org/abs/2607.10569 | [2607.10569] When Does Restricting a Coding Agent to execute_code Help? A Regime $\times$ Agent-Design Ablation | Modern coding agents expose multiple tool surfaces -- IDE primitives, bash, and Model Context Protocol (MCP) code-execution -- and the field has shipped three contradictory claims about which one matters. We run the missing crossed comparison: an integrity-clean three-arm ablation (baseline / bash_only / code_only) on ... | Hong Yang; Qi Yu; Travis Desell | 2026 | 2026 | KDD Workshop on Agentic Software Engineering (SE 3.0) | ACM SIGKDD | Accepted at KDD Workshop on Agentic Software Engineering (SE 3.0); the linked arXiv record is the available paper version. | cs.SE | Current arXiv acceptance note and official non-archival workshop page | 2607.10569 | 2026-08-13T14:24:04 | |||||||
ale-0281 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | Agentic Synthesis against Counterexample-Supplemented Sketches | https://arxiv.org/abs/2607.15854 | external | arxiv.org | Turns human-approved failures into durable policy: a coding agent revises a code-shaped sketch, regenerates code and prompt surfaces, preserves provenance, and must pass a regression gate before seeing the next case. | Turns human-approved failures into durable policy: a coding agent revises a code-shaped sketch, regenerates code and prompt surfaces, preserves provenance, and must pass a regression gate before seeing the next case. | Turns human-approved failures into durable policy: a coding agent revises a code-shaped sketch, regenerates code and prompt surfaces, preserves provenance, and must pass a regression gate before seeing the next case. | Durable execution and replay are treated as first-class loop infrastructure. Turns human-approved failures into durable policy: a coding agent revises a code-shaped sketch, regenerates code and prompt surfaces, preserves provenance, and must pass a regression gate before seeing the next case. | Use Agentic Synthesis against Counterexample-Supplemented Sketches to choose an implementation surface for repeatable agent work. | Research source arXiv:2607.15854; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 911 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L911 | 2026-07-20 | Build | build | Choose runtimes, tools, and delegation surfaces. | escalation | researcher;evaluator | agent | direct | research-preprint | A | ok | https://arxiv.org/abs/2607.15854 | [2607.15854] Agentic Synthesis against Counterexample-Supplemented Sketches | Coding agents can fix a failing example without preserving the domain rule that made it fail. We present agentic synthesis against counterexample-supplemented sketches, a repository-native method for systems whose policy is discovered during implementation. A human starts with a partial sketch, and a coding agent compi... | Muness Castle; Eric Rubeck | 2026-07-17 | 2026 | arXiv | arXiv | 32 pages, 5 displayed figures (4 distinct screenshots). Includes the CatSynth artifact supplement. Code and captured experiment artifacts: https://github.com/open-horizon-labs/counterexample-supplemented-sketches Clarifies the two-check CESS method and Developer change authority; adds the protocol-correct CatSynth reru... | cs.SE | arxiv-api | 2607.15854 | 2026-08-13T14:24:04 | |||||||
ale-0282 | Coding-Agent Loop Systems | coding-agent-loop-systems | Paper | 📄 | Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration | https://arxiv.org/abs/2607.15769 | external | arxiv.org | Defines a repository-hosted governance contract connecting contributor evidence with maintainer verification and decision authority; after an audit of 50 repositories, a 15-reviewer study improves exact risk-label recovery from 15/37 to 37/38 with the manifest-supported materials. | Defines a repository-hosted governance contract connecting contributor evidence with maintainer verification and decision authority; after an audit of 50 repositories, a 15-reviewer study improves exact risk-label recovery from 15/37 to 37/38 with the manifest-supported materials. | Defines a repository-hosted governance contract connecting contributor evidence with maintainer verification and decision authority; after an audit of 50 repositories, a 15-reviewer study improves exact risk-label recovery from 15/37 to 37/38 with the manifest-supported materials. | Verification is promoted from a final check to a loop-control signal. Defines a repository-hosted governance contract connecting contributor evidence with maintainer verification and decision authority; after an audit of 50 repositories, a 15-reviewer study improves exact risk-label recovery from 15/37 to 37/38 with th... | Use Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration to choose an implementation surface for repeatable agent work. | Research source arXiv:2607.15769; inspect its method and evaluation before treating results as production evidence. | medium | README.md | 912 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L912 | 2026-07-20 | Build | build | Choose runtimes, tools, and delegation surfaces. | verification | researcher;evaluator | agent | direct | research-preprint | A | ok | https://arxiv.org/abs/2607.15769 | [2607.15769] Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration | Generative AI and coding agents are intensifying a central governance tension in open-source software (OSS): they scale contribution generation faster than maintainers can assess risk, evidence, and accountability. Existing responses improve agent-readability and traceability, but project rules must also organize contr... | Jinjin Gao; Luyang Li; Shufen Guo; Ligang He; Xiaoning Sun | 2026-07-17 | 2026 | arXiv | arXiv | Preprint. Under journal review | cs.SE | arxiv-api | 2607.15769 | 2026-08-13T14:24:04 | |||||||
ale-0283 | Coding-Agent Loop Systems | coding-agent-loop-systems | Pattern | 🔁 | Ralph | https://ghuntley.com/ralph/ | external | ghuntley.com | Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory. | Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory. | Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory. | Persistent memory is treated as an external runtime artifact. Geoffrey Huntley's original Ralph technique: run one agent in a bare loop with fresh context per iteration and the filesystem plus specs as memory. | Use Ralph to choose an implementation surface for repeatable agent work. | Operational pattern or playbook; signal comes from reusable loop structure and practical transferability. | medium | README.md | 917 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L917 | Build | build | Choose runtimes, tools, and delegation surfaces. | context | builder | agent | direct | operational-pattern | B | ok | https://ghuntley.com/ralph/ | Ralph Wiggum as a "software engineer" | How Ralph Wiggum went from 'The Simpsons' to the biggest name in AI right now - Venture Beat 😎Here's a cool little field report from a Y Combinator hackathon event where they put Ralph Wiggum to the test. "We Put a Coding Agent in a While Loop and It Shipped | 2025-07-14 | 2025 | Geoffrey Huntley | html-meta | 2026-08-13T14:24:04 | |||||||||||||
ale-0284 | Coding-Agent Loop Systems | coding-agent-loop-systems | Pattern | 🔁 | everything is a ralph loop | https://ghuntley.com/loop/ | external | ghuntley.com | Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop. | Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop. | Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop. | Durable execution and replay are treated as first-class loop infrastructure. Follow-up essay arguing the loop, not the agent, is the durable engineering unit: one task per iteration, deterministic context, and verification inside the loop. | Use everything is a ralph loop to choose an implementation surface for repeatable agent work. | Operational pattern or playbook; signal comes from reusable loop structure and practical transferability. | medium | README.md | 918 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L918 | Build | build | Choose runtimes, tools, and delegation surfaces. | context;verification | builder | agent | direct | operational-pattern | B | ok | https://ghuntley.com/loop/ | everything is a ralph loop | I’ve been thinking about how I build software is so very very different how I used to do it three years ago. No, I’m not talking about acceleration through usage of AI but instead at a more fundamental level of approach, techniques and best practices. Standard software practices | 2026-01-17 | 2026 | Geoffrey Huntley | html-meta | 2026-08-13T14:24:04 | |||||||||||||
ale-0285 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | how-to-ralph-wiggum | https://github.com/ghuntley/how-to-ralph-wiggum | external | github.com | Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions. | Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions. | Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions. | Uses real automated software-engineering systems as evidence for practical loop architectures. Reference repository documenting the Ralph Wiggum technique end to end, from the bare loop script to guardrails and conventions. | Use how-to-ralph-wiggum to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 919 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L919 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | builder | agent | direct | source-implementation | A | ok | https://github.com/ghuntley/how-to-ralph-wiggum | GitHub - ghuntley/how-to-ralph-wiggum: The Ralph Wiggum Technique—the AI development methodology that reduces software costs to less than a fast food worker's wage. · GitHub | The Ralph Wiggum Technique—the AI development methodology that reduces software costs to less than a fast food worker's wage. - ghuntley/how-to-ralph-wiggum | ghuntley/how-to-ralph-wiggum | GitHub | html-meta | ghuntley/how-to-ralph-wiggum | 2026-08-13T14:24:04 | |||||||||||||
ale-0286 | Coding-Agent Loop Systems | coding-agent-loop-systems | Blog | 📝 | A Brief History of Ralph | https://www.humanlayer.dev/blog/brief-history-of-ralph | external | www.humanlayer.dev | Traces how the bare-loop technique spread from a provocation to a production practice among early adopters. | Traces how the bare-loop technique spread from a provocation to a production practice among early adopters. | Traces how the bare-loop technique spread from a provocation to a production practice among early adopters. | Uses real automated software-engineering systems as evidence for practical loop architectures. Traces how the bare-loop technique spread from a provocation to a production practice among early adopters. | Use A Brief History of Ralph to choose an implementation surface for repeatable agent work. | Contextual source from www.humanlayer.dev; useful for practice signals or boundary conditions, not independent validation. | contextual | README.md | 920 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L920 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | builder | agent | direct | practitioner-analysis | B | ok | https://www.humanlayer.dev/blog/brief-history-of-ralph | A Brief History of Ralph | HumanLayer Blog | The Ralph Wiggum Technique went viral in the last week of 2025. Here's the story of ralph since the first time I met Geoff in June of 2025. | 2026 | 2026 | humanlayer.dev | html-meta | 2026-08-13T14:24:04 | |||||||||||||
ale-0287 | Coding-Agent Loop Systems | coding-agent-loop-systems | Pattern | 🔁 | Ralph Copilot | https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf | external | github.com | Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`. | Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`. | Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`. | Persistent memory is treated as an external runtime artifact. Language-agnostic Ralph loop implementation using fresh context, filesystem memory, `PRD.md`, and `PROGRESS.md`. | Use Ralph Copilot to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 921 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L921 | Build | build | Choose runtimes, tools, and delegation surfaces. | context | builder | agent | direct | operational-pattern | B | ok | https://github.com/giocaizzi/ralph-copilot/tree/e5b2813cc876c73a8c9d3398c0115da0d15f63cf | GitHub - giocaizzi/ralph-copilot at e5b2813cc876c73a8c9d3398c0115da0d15f63cf · GitHub | Copilot implementation of Ralph loop. Contribute to giocaizzi/ralph-copilot development by creating an account on GitHub. | giocaizzi/ralph-copilot | GitHub | html-meta | giocaizzi/ralph-copilot | 2026-08-13T14:24:04 | |||||||||||||
ale-0288 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | Ralph (snarktank) | https://github.com/snarktank/ralph | external | github.com | Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes. | Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes. | Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes. | State persistence is explicit enough for repeated runs and handoff. Ryan Carson's PRD-driven Ralph implementation that re-runs Amp or Claude Code with a fresh instance per iteration, gates each story on typecheck and tests, and persists state in prd.json, progress.txt, and Git history until every story passes. | Use Ralph (snarktank) to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 922 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L922 | Build | build | Choose runtimes, tools, and delegation surfaces. | verification;state | builder | agent | direct | source-implementation | A | ok | https://github.com/snarktank/ralph | GitHub - snarktank/ralph: Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete. · GitHub | Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete. - GitHub - snarktank/ralph: Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete. | snarktank/ralph | GitHub | html-meta | snarktank/ralph | 2026-08-13T14:24:04 | |||||||||||||
ale-0289 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | ralph-claude-code | https://github.com/frankbria/ralph-claude-code | external | github.com | Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop. | Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop. | Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop. | Uses real automated software-engineering systems as evidence for practical loop architectures. Loop runner that repeatedly re-executes Claude Code against project requirements, using dual-condition exit detection, rate limiting, and a circuit breaker to decide when the loop should stop. | Use ralph-claude-code to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 923 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L923 | Build | build | Choose runtimes, tools, and delegation surfaces. | exit | builder | agent | direct | source-implementation | A | ok | https://github.com/frankbria/ralph-claude-code | GitHub - frankbria/ralph-claude-code: Autonomous AI development loop for Claude Code with intelligent exit detection · GitHub | Autonomous AI development loop for Claude Code with intelligent exit detection - frankbria/ralph-claude-code | frankbria/ralph-claude-code | GitHub | html-meta | frankbria/ralph-claude-code | 2026-08-13T14:24:04 | |||||||||||||
ale-0290 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | ralph-orchestrator | https://github.com/mikeyobrien/ralph-orchestrator | external | github.com | Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard. | Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard. | Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard. | Orchestration and control flow are made explicit and inspectable. Multi-backend implementation of the Ralph Wiggum technique that keeps a coding agent looping until task completion, using role-scoped hat personas that coordinate through events, with human-in-the-loop controls and a monitoring dashboard. | Use ralph-orchestrator to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 924 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L924 | Build | build | Choose runtimes, tools, and delegation surfaces. | delegation;escalation;exit | builder | agent | direct | source-implementation | A | ok | https://github.com/mikeyobrien/ralph-orchestrator | GitHub - mikeyobrien/ralph-orchestrator: An improved implementation of the Ralph Wiggum technique for autonomous AI agent orchestration · GitHub | An improved implementation of the Ralph Wiggum technique for autonomous AI agent orchestration - mikeyobrien/ralph-orchestrator | mikeyobrien/ralph-orchestrator | GitHub | html-meta | mikeyobrien/ralph-orchestrator | 2026-08-13T14:24:04 | |||||||||||||
ale-0291 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | ralphex | https://github.com/umputun/ralphex | external | github.com | Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan. | Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan. | Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan. | Uses real automated software-engineering systems as evidence for practical loop architectures. Extended Ralph loop runner that creates a Git branch per plan, executes tasks in fresh sessions with a commit after each, runs a multi-phase review pipeline with parallel review agents, and archives the completed plan. | Use ralphex to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 925 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L925 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | builder | agent | direct | source-implementation | A | ok | https://github.com/umputun/ralphex | GitHub - umputun/ralphex: Extended Ralph loop for autonomous AI-driven plan execution · GitHub | Extended Ralph loop for autonomous AI-driven plan execution - umputun/ralphex | umputun/ralphex | GitHub | html-meta | umputun/ralphex | 2026-08-13T14:24:04 | |||||||||||||
ale-0292 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | ralph (iannuttall) | https://github.com/iannuttall/ralph | external | github.com | File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends. | File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends. | File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends. | Persistent memory is treated as an external runtime artifact. File-based Ralph-style agent loop that executes one JSON PRD story per iteration with fresh model context, using Git and on-disk state as memory across Claude, Codex, Droid, and OpenCode backends. | Use ralph (iannuttall) to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 926 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L926 | Build | build | Choose runtimes, tools, and delegation surfaces. | context;state | builder | agent | direct | source-implementation | A | ok | https://github.com/iannuttall/ralph | GitHub - iannuttall/ralph: A minimal, file‑based agent loop for autonomous coding. · GitHub | A minimal, file‑based agent loop for autonomous coding. - iannuttall/ralph | iannuttall/ralph | GitHub | html-meta | iannuttall/ralph | 2026-08-13T14:24:04 | |||||||||||||
ale-0293 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | ralph-loop-agent | https://github.com/vercel-labs/ralph-loop-agent | external | github.com | Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger. | Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger. | Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger. | Verification is promoted from a final check to a loop-control signal. Vercel Labs implementation of the Ralph loop for the AI SDK: an outer loop re-runs the agent with verifier feedback until a verifyCompletion check passes or iteration, token, or cost stop conditions trigger. | Use ralph-loop-agent to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 927 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L927 | Build | build | Choose runtimes, tools, and delegation surfaces. | trigger;budget;exit | builder | agent | direct | source-implementation | A | ok | https://github.com/vercel-labs/ralph-loop-agent | GitHub - vercel-labs/ralph-loop-agent: Continuous Autonomy for the AI SDK · GitHub | Continuous Autonomy for the AI SDK. Contribute to vercel-labs/ralph-loop-agent development by creating an account on GitHub. | vercel-labs/ralph-loop-agent | GitHub | html-meta | vercel-labs/ralph-loop-agent | 2026-08-13T14:24:04 | |||||||||||||
ale-0294 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | Open Ralph Wiggum | https://github.com/Th0rgal/open-ralph-wiggum | external | github.com | Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends. | Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends. | Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends. | Context is managed as durable loop state rather than a single prompt payload. Agent-agnostic CLI that runs the Ralph Wiggum loop by feeding the same prompt to a fresh agent instance each iteration, with task tracking, live status monitoring, and mid-loop context injection across six coding-agent backends. | Use Open Ralph Wiggum to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 928 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L928 | Build | build | Choose runtimes, tools, and delegation surfaces. | context | builder | agent | direct | source-implementation | A | ok | https://github.com/Th0rgal/open-ralph-wiggum | GitHub - Th0rgal/open-ralph-wiggum: Type `ralph "prompt"` to start open code in a ralph loop. Also supports a prompt file & status check. Open Code, Claude Code, Codex, Copilot · GitHub | Type `ralph "prompt"` to start open code in a ralph loop. Also supports a prompt file & status check. Open Code, Claude Code, Codex, Copilot - Th0rgal/open-ralph-wiggum | Th0rgal/open-ralph-wiggum | GitHub | html-meta | Th0rgal/open-ralph-wiggum | 2026-08-13T14:24:04 | |||||||||||||
ale-0295 | Coding-Agent Loop Systems | coding-agent-loop-systems | Pattern | 🔁 | Compound Engineering | https://every.to/guides/compound-engineering | external | every.to | Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph. | Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph. | Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph. | Persistent memory is treated as an external runtime artifact. Every's named plan-work-review-compound loop, where each run feeds lessons back into `AGENTS.md`-style memory so the next loop is easier; the self-improving counterpart to Ralph. | Use Compound Engineering to choose an implementation surface for repeatable agent work. | Operational pattern or playbook; signal comes from reusable loop structure and practical transferability. | medium | README.md | 933 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L933 | Build | build | Choose runtimes, tools, and delegation surfaces. | context | builder | agent | direct | operational-pattern | B | ok | https://every.to/guides/compound-engineering | Compound Engineering - Every | The AI-native engineering philosophy | every.to | domain-fallback | 2026-08-13T14:24:04 | |||||||||||||||
ale-0296 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | Gas Town | https://github.com/steveyegge/gastown | external | github.com | Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration. | Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration. | Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration. | The work separates roles across agents, verifiers, or orchestration layers. Steve Yegge's multi-agent orchestrator that runs 20-30 parallel coding agents with coordinator, worker, and merge-queue roles; the structured-orchestration end of the spectrum that Ralph anchors with bare iteration. | Use Gas Town to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 934 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L934 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake;delegation | builder | agent | direct | source-implementation | A | ok | https://github.com/gastownhall/gastown | GitHub - gastownhall/gastown: Gas Town - multi-agent workspace manager · GitHub | Gas Town - multi-agent workspace manager. Contribute to gastownhall/gastown development by creating an account on GitHub. | steveyegge/gastown | GitHub | html-meta | steveyegge/gastown | 2026-08-13T14:24:04 | |||||||||||||
ale-0297 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | Amp | https://ampcode.com/ | external | ampcode.com | Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices. | Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices. | Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices. | The work separates roles across agents, verifiers, or orchestration layers. Agentic coding tool built around threads, subagents, and an opinionated harness, with an owner's manual that documents loop-style operating practices. | Use Amp to choose an implementation surface for repeatable agent work. | Working implementation, framework, runtime, or repository; signal comes from usable code and ecosystem adoption. | high | README.md | 935 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L935 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;context;delegation | builder | agent | direct | implementation | A | ok | https://ampcode.com/ | Amp | Amp is the frontier agent that lets you wield the full power of leading models. | ampcode.com | domain-fallback | 2026-08-13T14:24:04 | |||||||||||||||
ale-0298 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | karl | https://github.com/kayoslab/karl | external | github.com | Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases. | Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases. | Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases. | The work separates roles across agents, verifiers, or orchestration layers. Autonomous multi-agent development loop with planner, reviewer, architect, tester, developer, deployment, and retry phases. | Use karl to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 936 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L936 | Build | build | Choose runtimes, tools, and delegation surfaces. | delegation;budget | builder | agent | direct | source-implementation | A | ok | https://github.com/kayoslab/karl | GitHub - kayoslab/karl: Autonomous multi-agent development loop · GitHub | Autonomous multi-agent development loop. Contribute to kayoslab/karl development by creating an account on GitHub. | kayoslab/karl | GitHub | html-meta | kayoslab/karl | 2026-08-13T14:24:04 | |||||||||||||
ale-0299 | Coding-Agent Loop Systems | coding-agent-loop-systems | Pattern | 🔁 | joelclaw agent-loop skill | https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md | external | github.com | Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files. | Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files. | Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files. | Durable execution and replay are treated as first-class loop infrastructure. Durable Planner-Implementor-Reviewer-Judge coding loops via Inngest events and progress files. | Use joelclaw agent-loop skill to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 937 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L937 | Build | build | Choose runtimes, tools, and delegation surfaces. | workspace;delegation;verification;state | builder | agent | direct | operational-pattern | B | ok | https://github.com/joelhooks/joelclaw/blob/main/skills/agent-loop/SKILL.md | joelclaw/skills/agent-loop/SKILL.md at main · joelhooks/joelclaw · GitHub | Personal AI operating system — blog, architecture decisions, and the journey from zero to a composable agent system. - joelclaw/skills/agent-loop/SKILL.md at main · joelhooks/joelclaw | joelhooks/joelclaw | GitHub | html-meta | joelhooks/joelclaw | 2026-08-13T14:24:04 | |||||||||||||
ale-0300 | Coding-Agent Loop Systems | coding-agent-loop-systems | Tool | 🧰 | ARIS (Auto-Research-In-Sleep) | https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep | external | github.com | Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate. | Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate. | Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate. | Verification is promoted from a final check to a loop-control signal. Markdown-only skills that run autonomous overnight ML research loops on Claude Code, Codex, or other LLM agents, iterating idea discovery and experiments with cross-model review as the verification gate. | Use ARIS (Auto-Research-In-Sleep) to choose an implementation surface for repeatable agent work. | Inspectable GitHub source; popularity is context, not proof of reliability. | medium | README.md | 938 | https://github.com/ChaoYue0307/awesome-loop-engineering/blob/main/README.md#L938 | Build | build | Choose runtimes, tools, and delegation surfaces. | intake;verification | builder | agent | direct | source-implementation | A | ok | https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep | GitHub - wanshuiyin/Auto-claude-code-research-in-sleep: ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent. · GitHub | ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent. - wanshuiyin/Auto-claude-code-research-in-sleep | wanshuiyin/Auto-claude-code-research-in-sleep | GitHub | html-meta | wanshuiyin/Auto-claude-code-research-in-sleep | 2026-08-13T14:24:04 |
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