self-evolving-agents

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.

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.

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.