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MORE 是一个自进化训练框架，通过两阶段范式与 MATE-PPO 联合学习推理模式选择和计算预算分配，使智能体在编码、数学和通用推理基准上以更短的响应长度和更低的推理时间取得一致的性能提升。
The author argues that beyond agent harnesses, agent interfaces are a crucial direction for self-evolving agents, enabling better efficiency, lower costs, and more reliable human-agent collaboration as seen with Artifacts. The post quotes DAIR.AI highlighting a paper on evolving agent interfaces as a trend to watch for agent builders.
Meta AI 提出 Retrospective Self-Distillation (RSD)，一个利用特权回顾性上下文让模型对自身动作重新打分并回灌策略的多轮自进化框架，在 ALFWorld、ScienceWorld 和 SearchQA 上相对基线提升最高 59.7%。
这篇关于 Evo² 的文章提出将自进化 Agent 的两条路径（修改模型权重与修改 Harness）连接起来，让 Proposer 与 Reflector 双角色进入持续训练循环，在 Polyglot 上达到 82.1%、SWE-bench Verified 上达到 48.3%，且演化出的 Harness 可直接迁移复用。
Evo²提出权重-支架协同进化框架，通过双角色（Proposer/Reflector）管线和EvoTIDE训练流程（SFT+GRPO），在Polyglot达到82.1%、SWE-bench Verified达到48.3%，显著优于现有自进化方法，并可跨模型与任务迁移。
本文提出EmbodiedCLBench，首个用于评估自进化智能体持续学习能力的具身仿真基准，采用组合泛化为核心评估原则。实验结果显示当前智能体虽能从基础任务经验中获益，但改进程度无法随学习样本数量有效扩展。
This paper proposes Population-Curated Policy Optimization (PCPO), a sample-efficient parametric self-evolution method that helps LLM algorithm-design agents escape in-context evolutionary stagnation by training on self-generated algorithms. Trained on only 4 chip cases, PCPO outperforms OpenEvolve and ShinkaEvolve across 16 EDA cases, matches GPT-5.5 with an 8B model, and achieves 8.27× average speedup on GPU kernel design.
The paper introduces SAGE, a statistical acceptance gate for LLM agents that self-evolve by editing persistent skill documents, using per-item paired comparisons and a one-sided paired test to prevent regressions and avoid the Optimizer's Curse, achieving lower regression rates and higher scores across five benchmarks and four backbone LLMs.
This paper identifies "co-cheating" in self-evolving search agents, where a proposer and solver converge on shared errors that inflate internal reward without improving true correctness. It proposes CrossFit, a cross-fitted verification scheme that substantially reduces false agreement and boosts performance on seven search benchmarks with Qwen3.5-4B/9B models.
ExpVoyager introduces a dynamic framework for agent skill synthesis by navigating past experiences on demand, demonstrating improvements in task performance and scalability.
EvolveTrade introduces a self-evolving framework for LLM trading agents that refines tool-use policies using decision traces and portfolio feedback, improving Sharpe Ratio and cumulative returns across market regimes.
This paper proposes Feedback-Enriched Environments (FEEs) to bootstrap self-evolving agents in long-horizon tasks by enriching feedback for reinforcement learning, showing improved stability and performance on benchmarks using Qwen3 models and RL algorithms.
This paper introduces HarnessEvo to decompose LLM agent harnesses into separately-evolvable slots, revealing that optimization value is localized in specific components like reflection/control, and that uniform budget-splitting is sub-optimal, advocating for targeted budget concentration.
This survey paper connects self-evolving LLM-based agents with dynamic graph transformation, proposing a framework to model agent state as dynamic graphs and organizing existing methods for their evolution.
SAGE is a framework for automating storyboard generation in short drama production using self-evolving rules and attribution-guided updates, achieving expert-level performance and reducing authoring time in commercial deployment.
This article introduces a new paper from DeepSeek that proposes a programming paradigm for spatiotemporal composability, addressing dynamic composability challenges in self-evolving agents.
PACE-Bench introduces a simulator-grounded benchmark for evaluating self-evolving agents on physics adaptation tasks involving iterative code redesign after environmental mutations, revealing that mechanism redesign is a major bottleneck compared to parameter inference.
SkillZip is a new method for compressing the accumulated skills of self-evolving agents without evaluation rollouts, by finding a minimal faithful structural explanation that reuses repeated rules and procedures while preserving rare exceptions.
RoMeRL introduces a reduced-order memory reinforcement learning method for self-evolving LLM agents that balances feedback coverage and avoids the memory-reward trap. Experiments on ALFWorld and LifelongAgentBench show improved task performance, an 80% reduction in Cold-Q ratio, higher feedback density, and fewer maintained memories and LLM calls.
This paper introduces SkillJack, the first attack targeting the experience-to-skill pipeline of self-evolving agents, showing that poisoned experiences can be transformed into persistent malicious skills that evade detection and survive deletion of original records.