skill-evolution

WikiSkill is a framework that co-evolves agent skills with a persistent knowledge base, consistently outperforming state-of-the-art methods and enabling effective skill transfer across models.

A paper from Google discusses separating skill-evolution systems into components like raw execution traces and a persistent wiki of knowledge for AI agents, related to self-improvement concepts.

This article introduces how Warp uses Claude to build self-improving AI agents, automatically optimizing skills through human feedback, and summarizes best practices for effective agent development.

A Google paper discusses maintaining agent skills by separating raw execution traces from persistent knowledge in a wiki-like structure.

WikiSkill introduces a framework that co-evolves agent skills with a persistent knowledge base to systematically accumulate experience and improve performance across models, demonstrating benefits over state-of-the-art skill-evolution methods.

This article introduces the Hermes Must-Install List, featuring plugins like the all-in-one plugin suite, Web Command Center, model-switching wizard, and skill self-evolution tools, aimed at enhancing Hermes' autonomy and productivity.

This paper introduces SkillMisevo-Gym and SkillMisevo-Bench to study how self-improving LLM agents can evolve unsafe skills from compromised experience, plus SafeEvolve as a mitigation wrapper. Experiments across 25 agent-method configurations show skill misevolution is widespread and can persist across sessions, though SafeEvolve reduces fresh-session harm significantly.

The paper introduces DIVE, a diversity-driven framework that enables frozen LLMs to self-improve by evolving persistent natural-language skills from task experience and verifier feedback, without parameter updates. It outperforms existing methods on math and logical reasoning tasks and transfers across model scales.

SkillEvo introduces a method to continuously improve AI agent skills by using multi-turn interaction feedback and governance layers to maintain evolution gradients, surpassing self-reflection and single-turn QA-driven approaches.

This paper introduces SESA, a self-evolving skill-augmented search agent that co-evolves task generation and skill memory via tool-augmented search self-play. It improves accuracy across seven QA benchmarks over baselines while supporting memory-free deployment.

Introduces KnowAct-GUIClaw, a framework for personal GUI assistants with self-evolving memory and skill, achieving state-of-the-art performance on the MobileWorld benchmark and outperforming closed-source models like GPT-5.5.

This paper proposes a method for co-evolving evaluation metrics and skills in self-improving LLM agent systems, demonstrating that metrics can be evolved and that a co-evolution approach recovers most of the performance of a ground-truth-driven oracle across code generation, text-to-SQL, and report generation tasks.

ComfyClaw is an agentic skill evolution framework for ComfyUI image generation workflows, using typed graph editing and region-level VLM verifiers to translate visual failures into repair suggestions, outperforming baselines across multiple configurations.

SkillHone is a harness for continual agent skill evolution that uses persistent decision history and practice feedback to improve performance on research and tool-mediated analysis tasks. It outperforms existing methods on GAIA and WebWalkerQA-EN benchmarks.

SkillAudit introduces a framework for evolving LLM agent skills without ground-truth feedback by using paired trajectory auditing and contrastive evaluation. It achieves 73.9% average task reward across 89 tasks, outperforming baseline methods.

VisualClaw is a self-evolving multimodal agent that reduces deployment costs through hybrid encoding and skill evolution, while improving video-QA accuracy across multiple benchmarks.

SkillCAT is a training-free framework for LLM agent skill self-evolution that addresses limitations of single-trace bias, unverified merging, and full corpus loading via three stages: Contrastive Causal Extraction, Assessment-Augmented Evolution, and Topology-Aware Task Execution, achieving up to 40.40% improvement on benchmarks.

SkillChain automates the lifecycle of per-intent skill specifications for image-based e-commerce AI assistants, improving response quality and user engagement through iterative refinement and routing alignment.

Bayesian-Agent presents a framework that treats reusable skills and SOPs as hypotheses, using Bayesian inference to guide agent behavior and improve task performance through posterior-guided harness optimization. It achieves significant improvements on multiple benchmarks with deepseek-v4-flash.

Verilog-Evolve is a feedback-driven framework that iteratively refines Verilog code generated by large language models, using functional simulation, synthesis, and timing metrics to promote better candidates and evolve reusable repair skills across tasks.