self-evolving

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.

Proposes EVOQUANT, a self-evolving framework that uses LLMs and a verifier pipeline to automate quantitative trading strategy optimization, achieving significant Sharpe ratio improvements across A-share and crypto markets.

Introduces Critic Experience Bank (CEB), a self-evolving critic framework for step-level confidence estimation in LLM agents that leverages a memory bank of past judgments and consequences to improve calibration without training.

This paper presents a method for compiling repeated standard operating procedure steps into validated, versioned tools before deployment, replacing inference-time code generation. In a fulfillment center alarm-triage system, this approach reduces p50 latency by 42% and end-to-end error rate by up to 53%.