1 citations · 1 across the 4 of their papers we have counts for
10 papers
SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?
Udari Madhushani Sehwag, Elaine Lau, Haniyeh Ehsani Oskouie +14
Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly physical validation. While ex…
PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning
Afra Feyza Akyürek, Advait Gosai, Chen Bo Calvin Zhang +21
Frontier model progress is often measured by academic benchmarks, which offer a limited view of performance in real-world professional contexts. Existing evaluations often fail to…
ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents
Manasi Sharma, Chen Bo Calvin Zhang, Chaithanya Bandi +13
Deep Research (DR) is an emerging agent application that leverages large language models (LLMs) to address open-ended queries. It requires the integration of several capabilities,…
Beyond Seeing: Evaluating Multimodal LLMs on Tool-Enabled Image Perception, Transformation, and Reasoning
Xingang Guo, Utkarsh Tyagi, Advait Gosai +8
Multimodal Large Language Models (MLLMs) are increasingly applied in real-world scenarios where user-provided images are often imperfect, requiring active image manipulations such…
TutorBench: A Benchmark To Assess Tutoring Capabilities Of Large Language Models
Rakshith S Srinivasa, Zora Che, Chen Bo Calvin Zhang +11
As students increasingly adopt large language models (LLMs) as learning aids, it is crucial to build models that are adept at handling the nuances of tutoring: they need to identif…
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
Xiang Deng, Jeff Da, Edwin Pan +19
We introduce SWE-Bench Pro, a substantially more challenging benchmark that builds upon the best practices of SWE-BENCH [25], but is explicitly designed to capture realistic, compl…