Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
HARPA: A Testability-Driven, Literature-Grounded Framework for Research Ideation
While there has been a surge of interest in automated scientific discovery (ASD), especially with the emergence of LLMs, it remains challenging for tools to generate hypotheses that are both…
LitPivot: Developing Well-Situated Research Ideas Through Dynamic Contextualization and Critique within the Literature Landscape
Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This requires iteratively reviewing literature and refining an…
Context-Aware RL for Agentic and Multimodal LLMs
Large language models (LLMs) often fail when answering requires identifying a small but decisive piece of evidence within a long or complex context, such as a single line in a tool trace or a subtle…
XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?
When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g.,"How do I parse XML with regex?"). In such cases, often referred to as the XY-problem, LLMs must…
Measuring AI Scientists: From Exams to Discovery
Large language models and agentic systems are increasingly embedded across the scientific work-flow, from literature synthesis and hypothesis generation to code execution, data analysis and writing.…
Process-Oriented Evaluation of AI-Assisted Scientific Writing
Bad writing hinders the publication of science. The role of artificial intelligence (AI) in generating and editing scientific texts remains unsettled. Abstracts serve as the critical gateway to…
ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery
Scientific artifacts such as models and datasets are foundations for research. With the rapid growth of platforms like HuggingFace, researchers now have access to a large number of artifacts. Yet, a…
Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind, Low-Vision, and Sighted Scientists
Visual diagrams, figures, and tables are central to scientific papers, and convey information beyond what is captured in text. While blind or low-vision (BLV) scientists have traditionally relied on…
Narrative Scaffolding: A Narrative-First Framework for Data-Driven Sensemaking
When exploring data, analysts construct narratives about what the data means by asking questions, generating visualizations, reflecting on patterns, and revising their interpretations as new…
Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning
Scientific problem solving poses unique challenges for LLMs, requiring both deep domain knowledge and the ability to apply such knowledge through complex reasoning. While automated scientific…