7 papers
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…
CMASS: A ClimbMix Agentic Search Suite for Evaluating Search Agents Across Heterogeneous Tasks
Sahel Sharifymoghaddam, Lingwei Gu, Yijun Ge +1
Evaluating advances in agentic search requires testing whether methods generalize across tasks and corpus scale. Changing web search services impede controlled comparisons, while s…
ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget
Nandan Thakur, Zijian Chen, Xueguang Ma +1
Search agents, which integrate language models (LMs) with web search, are becoming crucial for answering complex user queries. Constructing training datasets for deep research task…
AgentIR: Reasoning-Aware Retrieval for Deep Research Agents
Zijian Chen, Xueguang Ma, Shengyao Zhuang +3
Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike human users who issue and refine queries without documenting their intermediate t…
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…