5 papers · 1 filter
ACARE: Agent-Context-Aware Reranker for Iterative Search
Sahel Sharifymoghaddam, Yauhen Babakhin, Reza Esfandiarpoor +3
In agentic search, a document can match an agent-generated sub-query without helping answer the user's original question. Conventional rerankers score documents against the sub-que…
Projecting BrowseComp-Plus onto ClimbMix: Toward More Realistic Corpora for Agentic Search
Sahel Sharifymoghaddam, Lingwei Gu, Yijun Ge +1
The BrowseComp-Plus benchmark disentangled the evaluation of agentic search by replacing opaque web search with a fixed corpus, so that an agent's role can be separated from the re…
Still Fresh? Evaluating Temporal Drift in Retrieval Benchmarks
Nathan Kuissi, Suraj Subrahmanyan, Nandan Thakur +1
Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora. However, temporal changes in technical corpora, such as API…
Rerank Before You Reason: Analyzing Reranking Tradeoffs through Effective Token Cost in Deep Search Agents
Sahel Sharifymoghaddam, Jimmy Lin
Deep research agents rely on iterative retrieval and reasoning to answer complex queries, but scaling test-time computation raises significant efficiency concerns. We study how to…
Lighting the Way for BRIGHT: Reproducible Baselines with Anserini, Pyserini, and RankLLM
Sahel Sharifymoghaddam, Yijun Ge, Jimmy Lin
Retrieval benchmarks for large language models (LLMs) should reflect the long, reasoning-intensive queries typical of retrieval-augmented generation (RAG). We present a systematic…