activity
20242026
most citedMT-Mol:Multi Agent System with Tool-based Reasoning for Molecular Optimization

1 citations · 2 across the 5 of their papers we have counts for

collaborators

9 papers

cs.AI2026

Progressive Multi-Agent Reasoning for Biological Perturbation Prediction

Hyomin Kim, Sang-Yeon Hwang, Jaechang Lim +6

Predicting gene regulation responses to biological perturbations requires reasoning about underlying biological causalities. While large language models (LLMs) show promise for suc…

cs.CL2025

PARROT: An Open Multilingual Radiology Reports Dataset

Bastien Le Guellec, Kokou Adambounou, Lisa C Adams +85

Rationale and Objectives: To develop and validate PARROT (Polyglottal Annotated Radiology Reports for Open Testing), a large, multicentric, open-access dataset of fictional radiolo…

cs.LG20251 cited

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region

Abhiram Bhupatiraju, Sung Bum Ahn

Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics,…

cs.AI2025

Enhancing LLM Agent Safety via Causal Influence Prompting

Dongyoon Hahm, Woogyeol Jin, June Suk Choi +2

As autonomous agents powered by large language models (LLMs) continue to demonstrate potential across various assistive tasks, ensuring their safe and reliable behavior is crucial…

cs.AI20251 cited

MT-Mol:Multi Agent System with Tool-based Reasoning for Molecular Optimization

Hyomin Kim, Yunhui Jang, Sungsoo Ahn

Large language models (LLMs) have large potential for molecular optimization, as they can gather external chemistry tools and enable collaborative interactions to iteratively refin…

cs.LG2025

Self-Training Large Language Models with Confident Reasoning

Hyosoon Jang, Yunhui Jang, Sungjae Lee +2

Large language models (LLMs) have shown impressive performance by generating reasoning paths before final answers, but learning such a reasoning path requires costly human supervis…