arXiv AI
CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection
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ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL
ContextPilot is a proactive context‑management framework designed to improve long‑horizon agentic reasoning with large language models. It expands the toolset to include planning, long‑term memory, and soft context offloading, and introduces a reinforcement‑learning strategy that focuses on critical editing decisions and assigns action‑level advantages. Experiments on long‑context QA and deep search tasks demonstrate that ContextPilot achieves stronger performance with a more compact working context, outperforming existing baselines across various base models and benchmarks.
arXiv AI
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Retrieval-Augmented LLM Agents: Learning to Learn from Experience
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ACE: Pluggable Adaptive Context Elasticizer across Agents
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arXiv AI
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AMBER: Training Long-Horizon Web Agents through Append-Only Memory
The paper introduces AMBER, an append‑only memory framework for language‑model agents that interact over long horizons. AMBER lets agents jointly learn to reason, act, and write free‑form memory, guaranteeing retention by construction and enabling end‑to‑end reinforcement learning without extensive curated data. Experiments on WebArena Lite show AMBER outperforms overwrite‑based memory by 4.09 percentage points in average success and improves task completion rates in repeated runs.
arXiv Computer Vision
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IntentQA: Intent Question Answering in Videos by Cognitive Context Reasoning
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arXiv AI
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Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning
arXiv:2606. 18831v1 Announce Type: cross Abstract: Long-context reasoning is an essential capability for large language models, particularly when they are deployed as autonomous agents that must reason over lengthy trajectories.