2.1k citations · 6k across the 29 of their papers we have counts for
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EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights
Jiayi Geng, Zhengxuan Wu, Kevin S. Chen +12
When Isaac Newton discovered the law of gravitation, he did so through an iterative process of analyzing observed data such as planetary patterns, finding the underlying mechanisms…
MolecularCanvas: LLM-assisted Small-Molecule Drug Discovery via Structure-Guided Constraints
Haoyu Dong, Rui Sheng, Shuhao Zhang +7
Small-molecule drug discovery relies on iterative molecular optimization, where chemists repeatedly modify candidate compounds to balance multiple competing properties such as effi…
KARA: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression
Shen Han, Yuyang Wu, Junpu Yu +1
Reasoning language models often generate long chain-of-thought (CoT), which accumulates a massive KV cache during the decoding phase and incurs high decoding latency and limited th…
CLORE: Content-Level Optimization for Reasoning Efficiency
Yuyang Wu, Qiyao Xue, Guanxing Lu +4
Reinforcement learning post-training has improved the reasoning ability of large language models, but often produces unnecessarily long, repetitive, or semantically opaque reasonin…
Can Agents Price a Reaction? Evaluating LLMs on Chemical Cost Reasoning
Yuyang Wu, Yue Huang, Shuaike Shen +8
Large Language Models (LLMs) have become increasingly capable as tool-using agents, with benchmarks spanning diverse general agentic tasks. Yet rigorous evaluation of scientific to…
Knowing when to trust machine-learned interatomic potentials
Shams Mehdi, Ilkwon Cho, Olexandr Isayev
Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These methods scale unfavorably wit…