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Compute Allocation for Reasoning-Intensive Retrieval Agents
Sreeja Apparaju, Nilesh Gupta
As agents operate over long horizons, their memory stores grow continuously, making retrieval critical to accessing relevant information. Many agent queries require reasoning-inten…
Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders
Benjamin Rozonoyer, Chong You, Michael Boratko +5
The success of Large Language Models (LLMs) has motivated a shift toward generative approaches to retrieval and ranking, aiming to supersede classical Dual Encoders (DEs) and Cross…
Scalable In-context Ranking with Generative Models
Nilesh Gupta, Chong You, Srinadh Bhojanapalli +3
In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, ca…
LLM-guided Hierarchical Search for End-to-end Reasoning Intensive Retrieval
Nilesh Gupta, Wei-Cheng Chang, Ngot Bui +2
Search systems are increasingly used for reasoning-intensive queries, where what makes a document relevant requires understanding or reasoning over the query-document relation rath…