5 papers
A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline
Kai A. Horstmann, Ethan Lin, Alice A. Robie +2
Agentic AI offers a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months…
FormulaCode: Evaluating Agentic Optimization on Large Codebases
Atharva Sehgal, James Hou, Akanksha Sarkar +4
Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to optimize entire codebases under realisti…
Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization
Xuefei, Wang, Kai A. Horstmann +9
Adapting production-level computer vision tools to bespoke scientific datasets is a critical "last mile" bottleneck. Current solutions are impractical: fine-tuning requires large a…
Self-Evolving Visual Concept Library using Vision-Language Critics
Atharva Sehgal, Patrick Yuan, Ziniu Hu +3
We study the problem of building a visual concept library for visual recognition. Building effective visual concept libraries is challenging, as manual definition is labor-intensiv…
Neurosymbolic Grounding for Compositional World Models
Atharva Sehgal, Arya Grayeli, Jennifer J. Sun +1
We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CompGen), i.e., high performance on unseen input scenes obtain…