5 citations · 5 across the 6 of their papers we have counts for
13 papers · 1 filter
When Deep Research Agents Stagnate: Enhancing Reasoning with Retrieval-Aware Agent Control
Heydar Soudani, Elizabeth Lingg, Faegheh Hasibi +1
In this paper, we analyze the reasoning trajectories of a variety of DRAs and show that existing agents often suffer from reasoning stagnation: the majority of iterations contribut…
Uncertainty Quantification for Multimodal Retrieval Augmented Generation
Simon Binz, Heydar Soudani, Faegheh Hasibi
Retrieval Augmented Generation (RAG) improves the question answering capabilities of Large Language Models (LLMs) by incorporating external knowledge and has recently been extended…
Uncertainty Quantification for Retrieval-Augmented Reasoning
Heydar Soudani, Hamed Zamani, Faegheh Hasibi
Retrieval-augmented reasoning (RAR) is a recent evolution of retrieval-augmented generation (RAG) that employs multiple reasoning steps for retrieval and generation. While effectiv…
FACE: A Fine-Grained Reference-Free Evaluator for Conversational Information Access
Hideaki Joko, Faegheh Hasibi
A systematic, reliable, and low-cost evaluation of Conversational Information Access (CIA) systems remains an open challenge. Existing reference-based evaluation methods are proven…
Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agents
Mahta Rafiee, Heydar Soudani, Zahra Abbasiantaeb +3
Deep research agents have emerged as LLM-based systems designed to perform multi-step information seeking and reasoning over large, open-domain sources to answer complex questions…
OrLog: Resolving Complex Queries with LLMs and Probabilistic Reasoning
Mohanna Hoveyda, Jelle Piepenbrock, Arjen P de Vries +2
Resolving complex information needs that come with multiple constraints should consider enforcing the logical operators encoded in the query (i.e., conjunction, disjunction, negati…