Escaping the Context Bottleneck: Active Context Curation for LLM Agents via Reinforcement Learning explores A reinforcement learning-based framework actively curates context for LLM agents, reducing noise and improving performance on long-horizon tasks while significantly cutting token consumption.. Commercial viability score: 6/10 in LLM Agents.
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Page Freshness
Canonical route: /paper/escaping-the-context-bottleneck-active-context-curation-for-llm-agents-via-reinforcement-learning
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Agent Handoff
Canonical ID escaping-the-context-bottleneck-active-context-curation-for-llm-agents-via-reinforcement-learning | Route /paper/escaping-the-context-bottleneck-active-context-curation-for-llm-agents-via-reinforcement-learning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/escaping-the-context-bottleneck-active-context-curation-for-llm-agents-via-reinforcement-learningMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.11462"
}
}source_context
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 6.0
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