arXiv AI
From Knowledge Access to Source Learning: Developing Source-Specific Competence
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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 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
3d ago
Recurrent Harness for Agentic Continual Learning
ReHarness is a recurrent harness framework designed for agentic continual learning, enabling language model agents to adapt online to distribution shifts. It augments forward read/write operations with backward revision, using feedback from each instance’s trajectory to refine the harness state. Experiments on three continual learning benchmarks and multiple language model backbones show that ReHarness outperforms baseline methods, producing source‑grounded revisions that improve stability and plasticity.
arXiv AI
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REVERE: Reflective Evolving Research Engineer
arXiv:2603. 20667v2 Announce Type: replace-cross Abstract: Existing prompt-optimization techniques rely on local signals, causing poor generalization across tasks.
arXiv AI
Oct 1
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.