综述 | Self-Evolving Coding Agents：自进化编程智能体
Ranking
Overall
67
Content
70
Popularity
61
Observed public metrics from 1 member.
Merged summary
TL;DR — A survey (Zhou et al., Nanjing Univ. of Science & Technology / Nanjing Univ., arXiv 2608.03392) that frames "self-evolving coding agents": repo-level software-engineering agents that turn their own past coding attempts and executable feedback into persistent improvements, rather than staying static after deployment.
- Taxonomy by what evolves — five non-exclusive classes: agent framework/scaffold (SICA, STOP, Darwin Gödel Machine), memory (SWE-Exp, EvoCoder, Repository Memory), skills & tools (CODESKILL, GSkill, Socratic-SWE, Live-SWE-Agent), model/policy (Self-play SWE-RL, Agent-RLVR, coder-verifier co-evolution), and workflow/multi-agent topology (SEW, AFlow, EvoAgentX, EvoMAC).
- When and on what evidence — intra-task (fast, local), post-task (distilling full trajectories into reusable memory/skills), and staged batch updates (model/policy retraining); evidence tiers are outcome signals (pass/solve rate), environment feedback (compiler, test logs, shell, CI), and trajectory-derived records.
- Evaluation gap — SWE-bench variants and SWE-Gym are the core repo-level testbeds, with function-level benchmarks (HumanEval, MBPP, LiveCodeBench) as supplements; the survey argues pass/resolve rates are necessary but insufficient, and that evolution itself needs measuring (stability, cross-repo/cross-model transfer, cost, token/step overhead).
- Key risks — benchmark contamination and overfitting the eval harness, unreliable or incomplete feedback signals being baked into memory/skills/weights, stale or repo-overfit experience libraries, and near-total neglect of long-term maintainability, security, and out-of-domain transfer.
Sources (1)
综述 | Self-Evolving Coding Agents：自进化编程智能体
Public signals
Hugging Face upvotes 9
TL;DR — A survey (Zhou et al., Nanjing Univ. of Science & Technology / Nanjing Univ., arXiv 2608.03392) that frames "self-evolving coding agents": repo-level software-engineering agents that turn their own past coding attempts and executable feedback into persistent improvements, rather than staying static after deployment.
- Taxonomy by what evolves — five non-exclusive classes: agent framework/scaffold (SICA, STOP, Darwin Gödel Machine), memory (SWE-Exp, EvoCoder, Repository Memory), skills & tools (CODESKILL, GSkill, Socratic-SWE, Live-SWE-Agent), model/policy (Self-play SWE-RL, Agent-RLVR, coder-verifier co-evolution), and workflow/multi-agent topology (SEW, AFlow, EvoAgentX, EvoMAC).
- When and on what evidence — intra-task (fast, local), post-task (distilling full trajectories into reusable memory/skills), and staged batch updates (model/policy retraining); evidence tiers are outcome signals (pass/solve rate), environment feedback (compiler, test logs, shell, CI), and trajectory-derived records.
- Evaluation gap — SWE-bench variants and SWE-Gym are the core repo-level testbeds, with function-level benchmarks (HumanEval, MBPP, LiveCodeBench) as supplements; the survey argues pass/resolve rates are necessary but insufficient, and that evolution itself needs measuring (stability, cross-repo/cross-model transfer, cost, token/step overhead).
- Key risks — benchmark contamination and overfitting the eval harness, unreliable or incomplete feedback signals being baked into memory/skills/weights, stale or repo-overfit experience libraries, and near-total neglect of long-term maintainability, security, and out-of-domain transfer.