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普林斯顿博士后Shilong Liu提出自进化代理的三层分类体系：工件迭代优化、Agent Harness自我改进和无黄金答案的模型学习，系统梳理了相关概念和前沿工作。
This paper investigates how a biased LLM judge silently disables skill retirement in self-evolving agents, showing that false-pass bias across a sharp threshold prevents contribution-based retirement and that the failure is universal across domains, detectable only through a defect-injection audit.
Shilong Liu proposes a taxonomy classifying self-evolving agents into artifact optimization, harness self-improvement, and model learning, providing a common language for emerging agent research.
A paper proposing a mechanism for enterprise agents to improve by safely converting messy daily work into learning data, using a data proxy and control layer, with AREAL2.0 demonstrating online RL from real interaction traces.
This paper introduces SEA, an architecture for self-evolving agents that confines self-modification to a steering adapter and versioned harness around a frozen base model, using anytime-valid gates to audit modifications against a fixed error budget. Experiments on SWE-bench Verified with four base models show that the suite provides a +4 to +5% improvement on strong base models while preventing regressions.
Metis presents a controlled study comparing text and code memory for self-evolving agents, finding they have complementary trade-offs. It proposes a hierarchical dual-representation memory system that improves task accuracy by up to 20.6% and reduces execution cost by up to 22.8% on the AppWorld benchmark.
SEAGym is a new evaluation environment for self-evolving LLM agents that measures agent harness updates across training, validation, test, replay, and cost records, providing complementary signals about the evolution process.
OPD-Evolver proposes a self-evolving agent framework using slow-fast co-evolution and on-policy self-distillation to enhance memory management and policy learning, outperforming existing methods like ReasoningBank and Skill0 across multi-domain benchmarks.
A technical analysis of two approaches to building self-evolving AI agents: model-based (via architecture like SSMs or transformer with fast-weight updates, and training methods) and harness-based (via memory or meta harness that can rewrite itself). The author provides practical recommendations for different audiences.
PACE introduces an anytime-valid commit gate for self-evolving agents that replaces greedy acceptance with a sequential hypothesis test, controlling false-commit probability and reducing churn while matching performance with lower variance.
This paper introduces FinEvolveBench, a benchmark for financial sentiment prediction, and Tree-of-Experience (ToE), a structured experience-management method for LLM agents in low-repetition tasks with implicit rewards. Experiments show that ToE outperforms general-purpose experience mechanisms in such challenging settings.
Socratic-SWE introduces a closed-loop self-evolution framework for software engineering agents that leverages historical solving traces to generate targeted repair tasks, achieving 50.40% on SWE-bench Verified after three iterations.
Researchers from Alibaba/Qwen and Peking University introduce TMEM, a self-evolving parametric memory framework that uses online LoRA weight updates to let LLM agents genuinely learn from experience within a single episode, rather than relying solely on prompt-space memory. TMEM outperforms summary-based and retrieval-based baselines across multiple benchmarks including LoCoMo, LongMemEval-S, and CL-Bench.
OpenSkill is a framework for LLM agents to self-evolve skills and verification signals from open-world resources without target-task supervision, achieving high performance across benchmarks.
EVE-Agent introduces a framework for self-evolving search agents that ensure evidence verifiability by generating questions, answers, and evidence spans, and training on marginal accuracy gain of evidence. This improves grounded correctness without human annotations.
This paper introduces ExpWeaver, a framework that optimizes how self-evolving language model agents utilize past experiences during runtime decision-making. It demonstrates that selectively invoking experience based on reasoning uncertainty improves performance across various environments and models.
This paper introduces a method using knowledge-graph paths as intermediate supervision to improve self-evolving search agents. It addresses bottlenecks in Search Self-Play by grounding question construction in relational context and introducing a Waypoint Coverage Reward for graded partial credit.
This paper introduces SkillOS, a reinforcement learning framework that enables LLM agents to learn long-term skill curation policies for self-evolution, improving performance and generalization across tasks.
Researchers from Harbin Institute of Technology and Singapore Management University investigate safety risks in experience-driven self-evolving LLM agents, finding that even benign task experience can compromise safety in high-risk scenarios due to agents' execution-oriented tendencies, and revealing a fundamental safety–utility trade-off.