self-evolving

Introduces ERSkill, a retrieval-centric framework for self-evolving, skill-guided adaptive memory access in LLM agents. It co-evolves retrieval skills and a routing policy, substantially outperforming strong baselines across agent memory benchmarks.

The article introduces MindMemOS, a portable and self-evolving memory operating layer for AI agents that uses a unified entity-property-time structure, with algorithms for memory refinement and skill evolution. It achieves notable accuracy on LOCOMO and PersonaMem benchmarks and improves SpreadsheetBench performance by 9.2 percentage points.

The author comments on Deepseek Harness's plugin-based philosophy, arguing that it turns everything (GUI/TUI, etc.) into plugins and opposes hardcoding workflows. They compare it with Pi Agent and Prime-Agent, propose the idea of building a self-evolving Agent in a REPL, and have already started implementing it with Codex.

MEGA is a self-evolving infrastructure for coding-agent optimization that distills reusable wisdom from sessions, composes it via a typed Wisdom Graph, and uses operational evidence to continuously improve both the agents and the knowledge guiding their optimization.

GeoForge is a training-free, self-evolving framework for Earth-observation reasoning that structures completed trajectories into nonparametric memories to improve LLM agent planning and tool-use without updating the backbone model.

LinkedIn presents a self-evolving agentic customer support system that integrates RAG with evolutionary auto-prompting and modular evaluation, achieving significant gains in production A/B tests including a 9.0-point increase in QA self-serve and 30.6-point improvement in routing accuracy.

Presents NeSy-Spatial, a neuro-symbolic framework that self-evolves spatial reasoning skills by composing tool-use and geometry skills, improving accuracy on spatial reasoning benchmarks.

BigBang-v1 is a self-evolving 36B LLM from Endless Frontier Lab in Shanghai, trained with AI-generated frontier tasks and achieving strong performance with only 10K high-quality examples across science, coding, tool use, and long context.

Introduces EvoHarness-RL, a framework that learns runtime harness policies for long-horizon LLM agents, enabling them to construct and update external state (belief, progress, experience) during task execution. Using Qwen3-8B on ALFWorld, it achieves 96.9% success and reveals harness annealing and evolution dynamics.

A tweet expressing amazement at the concept of dynamic agent orgs—self-evolving multi-agent systems where the graph structure rewrites itself during execution.

This paper surveys self-evolving coding agents, which improve their future behavior by updating frameworks, memory, skills, tools, or models from prior coding interactions, and presents a taxonomy of what evolves, when, and what software-specific evidence drives it.

Presents SciToolAgent-Evo, an ontology-aware self-evolving LLM agent for open-world scientific tool acquisition, along with the OpenSciToolBench benchmark of 900 realistic tasks. The agent uses an evolving memory and LinUCB-based bandit gate to dynamically explore and acquire novel tools.

AgentStream introduces a unified framework to evaluate self-evolving LLM agents under streaming task scenarios, showing that self-evolution reliability varies across scenarios and is gated by model capability.

SERPO introduces a self-evolving rubric policy optimization framework for test-time reinforcement learning in open-ended generation, replacing answer voting with a closed loop that co-evolves response evidence, query-specific rubrics, and policy parameters, achieving significant improvements on health and research benchmarks.

This paper introduces a Self-Evolving Just-In-Time Memory framework for proactive embodied safety, combining a Risk-Sufficient Topological Belief Graph, Agency-Grounded Factual Memory, and Experience Memory to mitigate hazards without stalling task progress. Experiments on IS-Bench show significant Safe-Success rate improvements (e.g., +30.3% on Qwen3-VL-8B).

Microsoft's strategy for self-evolving agent skills, training them like neural networks with epochs, batch size, learning rates, and validation gates, fully open-source.

Microsoft open-sourced SkillOpt, a tool that enables AI agent skills to self-improve by automatically evaluating and rewriting their instructions, outperforming hand-crafted prompts and other optimizers with no model lock-in.

This paper proposes SEED, a method that internalizes post-hoc skills from trajectories into model parameters through self-evolving online distillation, solving the reward sparsity problem in long-horizon RL training, achieving significant improvements on benchmarks such as ALFWorld.

HealthClaw is an open-source agent architecture for longitudinal personal health management that uses self-evolving memory to improve support over repeated encounters, achieving higher accuracy and privacy compared to baselines across biomedical tasks.

Proposes SEED, a self-evolving on-policy distillation framework that converts completed trajectories into hindsight skills to improve reinforcement learning for interactive agent tasks, achieving consistent performance gains and sample efficiency.