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arXiv:2610.34043v1 Announce Type: cross Abstract: Autonomous agents are increasingly deployed across sequences of related tasks, yet they often fail to retain and improve their knowledge over time. E...
The paper introduces RIKA, a method for reliable knowledge acquisition in continual learning agents. RIKA hierarchically extracts factual, conceptual, procedural, and metacognitive knowledge and uses evidence-based admission to decide which claims are incorporated into the agent’s context, avoiding the reuse of unreliable information. Experiments on six task streams and four model backbones show that RIKA outperforms existing memory and experience-learning systems on aggregate performance.
arXiv:2609.23989v1 Announce Type: new Abstract: Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of tr...
Learn2Play Bench is a new benchmark that tests how well large language model agents learn from experience in unfamiliar, text‑based games with novel or counterintuitive rules. The benchmark provides reproducible feedback, automatic scoring, and varied game instances to evaluate learning across repeated attempts and transfer to new situations. Findings show that retaining full action records aids learning, human players outperform agents, and the choice of harness significantly impacts performance and inference cost.
WikiSkill is a framework that separates raw execution experience, accumulated knowledge, and executable skills, continuously consolidating experience into a persistent knowledge base (wiki). By co‑evolving agent skills with this wiki, the method consistently outperforms state‑of‑the‑art skill‑evolution techniques across diverse benchmarks and models. The study shows that larger models benefit more from evolved skills, smaller models can outperform larger ones when equipped with skills, and that skills transfer effectively across model families, with the wiki’s persistent knowledge being critical for success.
arXiv:2605.08693v3 Announce Type: replace Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and...