Tagged articles

Skill Evolution

12 articles · Page 1 of 1

Nov 4, 2026 · Artificial Intelligence

SkillHone: Tencent WeChat's Continuous Skill Evolution Framework for LLM Agents

Tencent WeChat introduces SkillHone, a framework that persists decision history — failure evidence, candidate fixes, test results, and accept/reject reasons — enabling LLM agents to cumulatively improve skills from real-world failures, with benchmarks showing 13–18% accuracy gains over baselines.

Agent FrameworksContinuous ImprovementDecision History

0 likes · 6 min read

SkillHone: Tencent WeChat's Continuous Skill Evolution Framework for LLM Agents

Machine Heart

Sep 11, 2026 · Artificial Intelligence

PhyAgentOS v1.0.0: Executable, Verifiable, Evolvable Harness for Physical Agents

PhyAgentOS v1.0.0 introduces an open-source harness that unifies heterogeneous robots, verifies task outcomes with evidence, enables bounded recovery from failures, and evolves skills through verified experience, achieving 98.6% success on LIBERO and measurable gains on CALVIN and RoboCasa benchmarks.

PhyAgentOSSkill EvolutionVLA

0 likes · 17 min read

PhyAgentOS v1.0.0: Executable, Verifiable, Evolvable Harness for Physical Agents

PaperAgent

Sep 1, 2026 · Artificial Intelligence

Google Publishes Two Agent Skill Papers in One Day: WikiSkill and SKILL.state Break New Ground

Google released two Agent Skill papers—WikiSkill and SKILL.state—introducing a structured knowledge layer for skill evolution and a state‑machine execution model that dramatically reduces prompt length, improves accuracy, and demonstrates strong cross‑model transfer and robustness across a suite of benchmarks.

Agent SkillLLMSKILL.state

0 likes · 13 min read

Google Publishes Two Agent Skill Papers in One Day: WikiSkill and SKILL.state Break New Ground

PaperAgent

Aug 9, 2026 · Artificial Intelligence

Tsinghua Unveils Two Breakthrough Papers on LLM Agent Skills

The article reviews Tsinghua University's two new papers—GSE, which introduces a global skill‑relation graph, clustering, and replay verification to make agent skills continuously improve, and SkillSentry, which uses ability contracts and adaptive honey‑world testing to ensure skill safety—detailing their methods, experimental results, and practical implications.

AI safetyAgentGSE

0 likes · 8 min read

Tsinghua Unveils Two Breakthrough Papers on LLM Agent Skills

PaperAgent

Aug 4, 2026 · Artificial Intelligence

How Peking University’s Two Papers Redefine Agent Skill Evolution

Two recent Peking University papers, VeriSkill and SESA, demonstrate that treating agent skills as self‑evolving memory—updated from failures via responsibility attribution, lesson abstraction, and failure distillation—yields significant performance gains across verification and search tasks and transfers across models.

AgentLLMProgram Verification

0 likes · 9 min read

How Peking University’s Two Papers Redefine Agent Skill Evolution

Machine Heart

Jul 23, 2026 · Artificial Intelligence

Teaching Agents to Evolve: The Hierarchical Skill Meta‑Evolving Framework HiSME

HiSME, a lightweight hierarchical skill meta‑evolution framework from Tsinghua and Huawei, enables LLM agents to accumulate execution experience without updating model parameters by evolving both task‑specific skills and the meta‑skills that generate and maintain them, improving performance on multi‑turn tool use and open‑world tasks.

HiSMELLM AgentsMeta-Learning

0 likes · 11 min read

Teaching Agents to Evolve: The Hierarchical Skill Meta‑Evolving Framework HiSME

PaperAgent

Jul 8, 2026 · Artificial Intelligence

Why Agent Skills Need Self‑Evolution: A Survey of 19 Frameworks and 10 Benchmarks

This survey from Rutgers and UNC Charlotte systematically reviews 19 agent‑skill evolution methods and 10 evaluation benchmarks, revealing critical gaps such as the lack of longitudinal tracking, binary pass/fail metrics, and one‑time security checks, and highlighting how separating diagnosis from rewrite improves cross‑task performance.

AgentReinforcement LearningSelf-Improving AI

0 likes · 9 min read

Why Agent Skills Need Self‑Evolution: A Survey of 19 Frameworks and 10 Benchmarks

Linyb Geek Road

Jun 29, 2026 · Artificial Intelligence

Understanding Loop Engineering: Concepts, Insights, and Practical Applications

The article explains Loop Engineering by distinguishing it from basic Agent Loops, outlines its six core components, showcases a text‑classification example, and discusses when the approach boosts efficiency versus when traditional Human‑in‑the‑Loop remains preferable.

AI agentsSkill Evolutionagent loop

0 likes · 21 min read

Understanding Loop Engineering: Concepts, Insights, and Practical Applications

PaperAgent

May 16, 2026 · Artificial Intelligence

A First Systematic Survey of Agent Skills: Taxonomy, Techniques, and Applications

This survey analyzes the emerging field of Agent Skills, defining a formal skill model, categorizing acquisition pathways, detailing retrieval strategies, and outlining a five‑stage evolution process, while highlighting large‑scale skill repositories and their implications for AI product design.

AI agentsAgent SkillsSkill Evolution

0 likes · 9 min read

A First Systematic Survey of Agent Skills: Taxonomy, Techniques, and Applications

PaperAgent

Apr 22, 2026 · Artificial Intelligence

How SkillClaw Enables Collective Evolution of Agent Skills in Real-World Use

SkillClaw introduces a centralized evolution framework that transforms user interactions into structured evidence, allowing LLM agents to refine, create, or skip skills based on aggregated success and failure patterns, with nightly validation ensuring only proven improvements are deployed, resulting in consistent performance gains across diverse tasks.

AI workflowLLM AgentsSkill Evolution

0 likes · 13 min read

How SkillClaw Enables Collective Evolution of Agent Skills in Real-World Use

Machine Heart

Apr 14, 2026 · Artificial Intelligence

EverOS Global Beta Unveils Self‑Evolving Memory Layer for AI Agents

EverOS launches a global beta of its next‑generation memory infrastructure that lets autonomous agents automatically extract experience, cluster it semantically, and evolve reusable skills, boosting OpenClaw task success rates by up to 234.8% while addressing context‑window limits, multimodal retrieval, and developer transparency.

AI MemoryEverOSEvoAgentBench

0 likes · 21 min read

EverOS Global Beta Unveils Self‑Evolving Memory Layer for AI Agents

SuanNi

Apr 2, 2026 · Artificial Intelligence

EvoSkill: Turning AI Failures into 12% Accuracy Gains with Automated Skill Evolution

The EvoSkill framework introduced by Sentient and Virginia Tech researchers equips large language models with a text‑feedback loop that automatically discovers, refines, and validates reusable agent Skills, boosting task‑specific accuracy by 12.1% and enabling cross‑domain transfer without altering the underlying model parameters.

AIAutomated LearningCross-Domain Transfer

0 likes · 11 min read

EvoSkill: Turning AI Failures into 12% Accuracy Gains with Automated Skill Evolution