arXiv AI
Aug 5
ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
arXiv:2608. 03874v1 Announce Type: new Abstract: Modern agent frameworks equip large language models with external skill libraries to solve complex tasks.
ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve their skills and whether the resulting skills improve task-solving capabilities.
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arXiv AI
Jun 3
AgentCL: Toward Rigorous Evaluation of Continual Learning in Language Agents
arXiv:2606. 02461v2 Announce Type: replace Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.
arXiv AI
Jun 2
AGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agents
arXiv:2606. 02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.
arXiv AI
Aug 18
Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents
arXiv:2608. 15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.
arXiv AI
Jun 4
Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents
arXiv:2606. 04815v1 Announce Type: cross Abstract: Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments.
arXiv AI
Jun 18
Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems
arXiv:2606. 18837v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks.
arXiv Machine Learning
Jul 30
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.
arXiv AI
Jun 3
From Context to Skills: Can Language Models Learn from Context Skillfully?
arXiv:2604. 27660v3 Announce Type: replace Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge.
arXiv AI
3d ago
Semantic Projection for Continual Self-Evolution of Language Agents
arXiv:2609.36626v1 Announce Type: new Abstract: Language-model agents increasingly rely on persistent natural-language skills to adapt beyond their frozen model parameters. When a shared skill is rep...
arXiv Machine Learning
Jul 10
When Does Continual Learning Require Learning
arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?
arXiv Machine Learning
Jun 9
SkillHone: A Harness for Continual Agent Skill Evolution Through Persistent Decision History
arXiv:2606. 08671v1 Announce Type: new Abstract: Agent skills extend language-model agents with task-specific procedures, scripts, and references, but the tasks and environments they target continually change.
arXiv AI
2d ago
Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents
Rep2Skill introduces a representation-guided framework that enables large language model agents to self-evolve their textual skills by analyzing internal representation trajectories from agent rollouts. The method identifies execution turns that deviate from successful dynamics and uses these signals, together with execution contexts, as actionable feedback for targeted skill revision. Experiments with two open-source LLMs across two agent environments demonstrate that Rep2Skill consistently outperforms purely text-based approaches, showing that incorporating internal representations can enhance agent self-improvement.