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Research questionHow can tool-using agents prioritize and compress action-relevant context instead of relying on semantic similarity?Agents may retrieve semantically related material that does not determine what they should do next. Compressing context can also discard evidence needed for a tool call or code change.
AI
AI Agents
AI Memory
Code Generation & Program Synthesis
Inference Optimization
Information Retrieval
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM AgentsThe evidence covers 50 SWE-bench Verified file-retrieval instances using BM25 top-50 candidates; the reported reranking result uses Qwen3.6-Plus, and selected-then-compressed mode reports token savings. The selection protocol can use hosted LLM judges, local surrogates, or lightweight rankers, but the supplied evidence does not establish performance across broader agent tasks or deployments.research paper · Sep 4, 2026SWE-Pruner Pro: The Coder LLM Already Knows What to PruneThe evidence concerns an in-agent small head that labels tool-output lines to keep or prune, evaluated across two open-weight backbones and four multi-turn benchmarks. Reported results include up to 39% fewer prompt and completion tokens while preserving task quality, with additional gains on two benchmarks; no broader deployment evidence is provided.research paper · Jul 20, 2026
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How can LLM agents retrieve relevant skills from large, noisy libraries under context and latency constraints?How can language models compress conversational and document context without reconstruction while preserving task-relevant information?How can LLM agents compress growing factual memories without losing retrievable evidence?How can tool-using agents reduce serial action–observation latency without sacrificing task completion?