ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL
Long-horizon agentic tasks require large language models (LLMs) to iteratively retrieve, integrate, and maintain dispersed information across multi-turn interactions, but preserving all interaction histories leads to a continuously growing working context. Recent proactive context management methods allow models to edit their own working context with specialized tools, yet they still face three key limitations: (1) a limited toolset restricted to search, deletion, and summarization, with no support for global planning, long-term memory, and adaptive compression; (2) inefficient exploration tha
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 57%Show HN: ContextVault – Shared memory layer for your AI and your team →
- FuzzySimilar title/name (fuzzy) · 87%NirDiamant/GenAI_Agents →
“Fuzzy title match (0.94): “ContextPilot: Teaching Agents for Proactive Context Manageme” ≈ “NirDiamant/GenAI_Agents””
- FuzzySimilar title/name (fuzzy) · 84%Unity-Technologies/ml-agents →
“Fuzzy title match (0.92): “ContextPilot: Teaching Agents for Proactive Context Manageme” ≈ “Unity-Technologies/ml-agents””
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%rasbt/LLMs-from-scratch →
“Shared author/contributor keys: yin”
- LinkedLinked via arxiv author · 85%Zhuoshi Pan →
“ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL”
- LinkedLinked via arxiv author · 85%Qizhi Pei →
“ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL”