12 papers
Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems
Patrick Emami, Sameera Horawalavithana, Truc Nguyen +11
Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists"…
MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding
Sai Munikoti, Ian Stewart, Chengping Chai +4
The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-specific datasets that integrat…
SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
Nithin Somasekharan, Youssef Hassan, Shiyao Lin +5
Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants, and a growing body of benchmarks evaluates their capabilities across knowledge retrieval, reas…
Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery
Renuka Chintalapati, Sid Raskar, Anurag Acharya +3
Coding agents accumulate extensive context during long-running tasks, yet fixed context windows force practitioners to choose between truncation and task failure. While numerous me…
Reward Design for Physical Reasoning in Vision-Language Models
Derek Lilienthal, Manisha Mukherjee, Sameera Horawalavithana
Physical reasoning over visual inputs demands tight integration of visual perception, domain knowledge, and multi-step symbolic inference. Yet even state-of-the-art Vision Language…
Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models
Sameera Horawalavithana, Lauren Phillips, Ian Stewart +2
Vision-Language Models (VLMs) have rapidly advanced by leveraging powerful pre-trained Large Language Models (LLMs) as core reasoning backbones. As new and more capable LLMs emerge…