arXiv Computation and Language
Unified Context Evolution for LLM Agents
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv AI
Jun 9
MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution
arXiv:2606. 07603v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions.
arXiv AI
Oct 7
Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction
The paper introduces SAGA, a framework that enables large language model agents to evolve by abstracting experiences into reusable principles, procedures, and episodic descriptions. SAGA transforms interaction trajectories into hierarchical knowledge with explicit applicability conditions, linking them back to execution evidence. Experiments on ScienceWorld and ALFWorld show that this execution–abstraction feedback loop improves task performance, and ablation studies confirm the importance of contextual instantiation and action regulation.
arXiv AI
Jul 28
ACM: Agentic Context Management for Long Horizon Tasks
arXiv:2607. 23809v1 Announce Type: new Abstract: Agentic tasks are inherently long-horizon and multi-turn, constantly accumulating context through interactions with the environment.
arXiv Machine Learning
Oct 8
SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
SkillRL is a framework that enhances large language model agents by automatically discovering and evolving skills from raw experience. It builds a hierarchical skill library called SkillBank, uses an adaptive retrieval strategy for heuristics, and allows the skill library to co‑evolve with the agent’s policy during reinforcement learning. These techniques reduce token usage and improve reasoning, achieving state‑of‑the‑art results on ALFWorld, WebShop, and seven search‑augmented tasks, outperforming baselines by 15.3% and remaining robust as task complexity grows.
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
Oct 19
Darwin-Claw: Environment-Adaptive Experience Consolidation for Recursively Self-Evolving Agents
Darwin‑Claw is a new framework for large language model agents that enables environment‑adaptive, self‑evolving behavior. It systematically distills episodic experiences into prototype vectors through a hierarchical experience consolidation system, providing context‑sensitive perception patches and explicit optimization signals for skill refinement. The method is deployable with mainstream agent systems and, according to experiments on WildClawBench, delivers significant performance gains while maintaining controllability and stability.