1 citations · 2 across the 11 of their papers we have counts for
11 papers
Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages
Chih-Hsuan Yang, Anjir Ahmed Chowdhury, Cheng-Hau Yang +7
Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer. Such filtering assumes that a message likel…
AEGIS: Assay-Aware Protocol Validation and Runtime Monitoring for Open-Source Liquid Handling Robots
Priyanka V. Setty, Arvind Ramanathan, Ian Foster +1
Self-driving laboratories increasingly rely on low-cost liquid handlers such as the Opentrons OT-2, which ship without the pressure-based aspiration monitoring of Hamilton or Tecan…
Precise but Uncoupled: Reviewer Precision Does Not Guarantee Critique Uptake in Multi-Agent Math Reasoning
Chih-Hsuan Yang, Jingyan Jiang, Vikram Vasudevan +7
Many math- and science-oriented agent systems use hierarchical designs with specialized reviewer roles, assuming that a dedicated review stage should help turn wrong candidates int…
BioAlchemy: Distilling Biological Literature into Reasoning-Ready Reinforcement Learning Training Data
Brian Hsu, Ozan Gökdemir, Carlo Siebenschuh +7
Despite the large corpus of biology training text, the impact of reasoning models on biological research generally lags behind math and coding. In this work, we show that biology q…
PRISM: Protocol Refinement through Intelligent Simulation Modeling
Brian Hsu, Priyanka V Setty, Rory M Butler +7
Automating experimental protocol design and execution remains as a fundamental bottleneck in realizing self-driving laboratories. We introduce PRISM (Protocol Refinement through In…
FragmentGPT: A Unified GPT Model for Fragment Growing, Linking, and Merging in Molecular Design
Xuefeng Liu, Songhao Jiang, Qinan Huang +5
Fragment-Based Drug Discovery (FBDD) is a popular approach in early drug development, but designing effective linkers to combine disconnected molecular fragments into chemically an…
AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine
Carlo Siebenschuh, Kyle Hippe, Ozan Gokdemir +10
Language models for scientific tasks are trained on text from scientific publications, most distributed as PDFs that require parsing. PDF parsing approaches range from inexpensive…
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
Franck Cappello, Sandeep Madireddy, Robert Underwood +23
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that req…
DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization
Xuefeng Liu, Songhao Jiang, Siyu Chen +4
Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives. This research delves into the realm of drug optimization and introduce a nove…
ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization
Xuefeng Liu, Songhao Jiang, Ian Foster +2
Drug optimization has become increasingly crucial in light of fast-mutating virus strains and drug-resistant cancer cells. Nevertheless, it remains challenging as it necessitates r…
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
Ashka Shah, Adela DePavia, Nathaniel Hudson +2
The aim in many sciences is to understand the mechanisms that underlie the observed distribution of variables, starting from a set of initial hypotheses. Causal discovery allows us…