16 citations · 25 across the 6 of their papers we have counts for
8 papers
BOReFT: Manifold Steering of Language Models for Black-box Optimization
Dhruv Agarwal, Rico Angell, Kavitha Srinivas +4
Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typically improve proposals throug…
Baikal: Structured Search for Deep Research over Data Lakes
Dhruv Agarwal, Rishitha Guttapalle Mohan, Aarti Kumari +5
Deep research over data lakes requires an LLM agent to investigate evidence across thousands of heterogeneous tables and passages to synthesize a report. Existing methods perform i…
Evidence-Informed LLM Beliefs for Continual Scientific Discovery
Dhruv Agarwal, Reece Adamson, Andrew McCallum +3
Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, where a reward signal guides w…
MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time
Peter Phan, Dhruv Agarwal, Kavitha Srinivas +3
Large language models (LLMs) are increasingly being applied to black-box optimization tasks, from program synthesis to molecule design. Prior work typically leverages in-context le…
AutoDiscovery: Open-ended Scientific Discovery via Bayesian Surprise
Dhruv Agarwal, Bodhisattwa Prasad Majumder, Reece Adamson +8
The promise of autonomous scientific discovery (ASD) hinges not only on answering questions, but also on knowing which questions to ask. Most recent works in ASD explore the use of…
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
Dhruv Agarwal, Rajarshi Das, Sopan Khosla +1
We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within…
Machine Reading Comprehension using Case-based Reasoning
Dung Thai, Dhruv Agarwal, Mudit Chaudhary +6
We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our meth…
Entity Linking and Discovery via Arborescence-based Supervised Clustering
Dhruv Agarwal, Rico Angell, Nicholas Monath +1
Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions. In this p…