ACARE: A 4B Reranker Scores Documents Against the Agent's Full Reasoning Context
_reachsumit · x · 2026-10-28
The ACARE paper tackles an agentic search blind spot: documents can match an agent-generated sub-query without helping answer the user's original question. ACARE is a compact 4B LLM reranker that scores documents using both the original question and the agent's reasoning trace. Across benchmarks and agent families it improves retrieval quality over its base reranker while reducing search effort, boosts answer accuracy, and helps smaller agents narrow the gap to larger ones. Model, training data, and code will be released.
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