ACARE: An Agent-Context-Aware Reranker for iterative search

In agentic search, conventional rerankers score candidate documents against an agent-generated sub-query, without considering the original question or the agent’s reasoning trace. We introduce the Agent-Context-Aware Reranker (ACARe), a 4B decoder-only LLM that reranks documents using this context when available. Across three benchmarks, ACARe improves dataset-averaged nDCG@10 by 0.9–2.0 percentage points over the base reranker, with fewer searches for all five agents, each from a different LLM family. In answer generation across two agents on ClimbMix, ACARe raises accuracy by 8.2–9.1 percentage points and reduces searches relative to no reranking, while matching or exceeding the base reranker’s mean accuracy with higher evidence precision. On BrowseComp-Plus, ACARe helps smaller agents close more of the nDCG@10 gap to larger agents without reranking: 51–74%, compared with 26–46% using the base reranker. We will release the model, training data, and code upon acceptance.