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This paper shows how acceptance gates in self-improving LLM agents silently erode redundant safety safeguards ('masked erosion'), since deletions hidden behind other working safeguards go undetected, and demonstrates that harnesses optimized under a strong model can fail safety under weaker models despite verbatim safety rules.
A paper introduces A-RSI (Adaptive Task Refresh for Self-Improvement), a method enabling zero-data multimodal self-improvement by adaptively refreshing tasks during training without requiring new human-labeled data.
This paper studies auxiliary trajectory preferences (confidence-based and entropy-based metrics) to improve small language model self-improvement and transfer, showing UID-Conj. gains of about 2 points over unmodified TTRL and uniform-weighted SFT on AIME tasks.
GitHub Copilot Automations can now learn from real outcomes on GitHub—such as label changes, comment acceptance, and merged PRs—to propose prompt improvements, with a human-in-the-loop review before changes are applied. The feature is available across Copilot Pro, Pro+, Max, Business, and Enterprise plans.
OmniBrain-0 is a lightweight unified embodied agent that coordinates manipulation and navigation through a shared high-level decision model, trained via multi-teacher on-policy distillation and a rubric-guided recursive self-improvement data flywheel, achieving strong cross-domain performance with much lower overhead than its closed-source teacher.
这篇注册研究考察了自改进智能体将运行结果写成教训时，是否会在后续决策中传递运气（随机性）的影响。实验发现，在可靠工具因偶然失败后，LLM撰写的教训会让智能体在后续运行中显著更多地选择较差的工具（11.6% vs 0.4%），而修改记忆写入指令可降低这一危害。
A-RSI 通过监控训练日志中的奖励变化与准确率来决定何时刷新任务池，结合模型自编写程序生成可独立验证的多模态问答对，在零外部数据条件下将基准平均性能从 57.77 提升到 61.05。
作者分享自己使用AI分析会议转录、评估沟通方式并追踪长期进步的实践，探讨AI在个人成长（而非单纯生产力）中的应用潜力，并希望有工具能实现这一功能。
提出了 RSIHub，一个用于构建、比较和扩展 agent-evolution 方法的开源框架，通过可复用的算子与配方简化实验基础设施。论文在 Terminal Bench 2 和 τ³ Banking 上评测了 AHE、Hyperagents、A-Evolve 与 GEPA，最大 pass@1 提升达 13.5 个百分点，但方法排名随设置变化且优化收益不一定迁移到留出任务。
This paper shows that a deployed self-improving science agent already computes an MDL compression ratio that can unsupervisedly separate law-grade from statistical-grade scientific knowledge, recovering canonical laws with high significance while revealing 86% of its own admitted discoveries compress nothing — the first measured instance of a discovery agent auditing its own ledger and finding itself over-claiming.
The paper introduces Phronix, a capability-centric learning paradigm that lets LLM-based agent harnesses acquire, validate, and reuse capabilities from their own executions via a predictor-validator-corrector loop, achieving 93-95% first-attempt success on recurring tasks and boosting cheaper models from ~28% to ~90% on SWE-bench.
ReGE 是一个将智能体历史执行经验合成为自指导信息的框架，使后续任务尝试无需更强教师模型或参数更新即可提升表现，在四个基准上平均提升约 9-10 个百分点，并大幅减少引导构建所需的 token 成本。
研究提出 AutoTavern，一个由 LLM 智能体自我改进的研究系统：验证游戏机制、构建炉石传说酒馆战棋模拟器，并通过真实反馈迭代优化专门的 RL 智能体 TavernAgent，在天梯达到约 15,000 MMR，同时开源了 TavernGym 模拟器和 TavernBench 基准。
该论文提出"错误复现"（error reproduction）机制，指出在LLM智能体外部记忆中持久化抽象规则会循环放大错误，并提出Anti-Persistence记忆设计：仅累积轨迹与结果、按需临时合成指导，从而在多个基准上取得更高成功率与更低LLM调用成本。
一篇微软研究院等机构的论文提出 Self-HPO，一种交替优化 harness 与策略模型的递归自我改进框架，将 Qwen3.5-35B-A3B 在三个编码基准上的平均解决率从 57.8% 提升到 64.8%，且联合增益超过各自增益之和。
LongZero 让大语言模型自行合成多跳推理与多轮检索任务，并用 GRPO 在零外部数据的条件下自我提升长上下文推理能力，在五个长上下文基准上超越依赖外部语料的基线方法。
Harness² 是 Google Research 推出的即插即用递归 agent 框架，让 LLM agent 在无人工验证的情况下自动优化其执行 harness（提示、工具、记忆），在 WorkBuddy-Bench 等专业基准上最高提升 11.5 个点。
论文提出 Self-HPO，一个递归自我改进框架，交替优化编码智能体的 harness 与策略模型，使 Qwen3.5-35B-A3B 在编码基准上的平均解决率从 57.8% 提升至 64.8%。
The post introduces Dan Koe's "Human 3.0" life upgrade model, recommending that you hand over four dimensions of life — mind, body, spirit, and career — to AI as a life coach for self-review and improvement.
Tencent Hunyuan's open-source paper Terminal-RSI proposes a recursive self-improvement framework, alternating between recursive self-rewriting and policy refinement training, raising a 27B terminal agent's task success rate on Terminal-Bench 2.1 from 51.7% to 86.52%.