SkillGLoW: Procedural-Family Skill Consolidation for Self-Improving Agents on Long-Horizon Task Streams

arXiv:2609.02217 · cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

Terminology

• Benchmark — 56× in this paper · explained in 2 episodes
• Clustering — 16× in this paper · explained in 2 episodes
• Verifier — 13× in this paper · explained in 2 episodes
• Agent Skills — 5× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Self-Evolving Agents — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Agentic Problem — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Continual Learning — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Procedural Knowledge — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Rethinking Agent Design: From Top-Down Workflows to Bottom-Up Skill Evolution
• SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?
• LiveMathematicianBench: A Live Benchmark for Research-Level Mathematical Reasoning with Proof Sketches
• Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents
• SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
• SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs
• MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
• Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
• Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
• SkillOS: Learning Skill Curation for Self-Evolving Agents
• SkillMAS: Skill Co-Evolution with LLM-based Multi-Agent System
• Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory
• Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
• SkillOpt: Executive Strategy for Self-Evolving Agent Skills
• AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• SkillFlow:Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents
• SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection