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20242026
most citedEvaluating Large Language Models on Multimodal Chemistry Olympiad Exams

4 citations · 6 across the 9 of their papers we have counts for

collaborators

11 papers

cs.AI2026

Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities

Shuangshuang Ying, Zheyu Wang, Yunjian Peng +16

Despite strong performance on existing benchmarks, it remains unclear whether large language models can reason over genuinely novel scientific information. Most evaluations score e…

cs.RO2025

Asynchronous Fast-Slow Vision-Language-Action Policies for Whole-Body Robotic Manipulation

Teqiang Zou, Hongliang Zeng, Yuxuan Nong +6

Most Vision-Language-Action (VLA) systems integrate a Vision-Language Model (VLM) for semantic reasoning with an action expert generating continuous action signals, yet both typica…

cs.CL20254 cited

Evaluating Large Language Models on Multimodal Chemistry Olympiad Exams

Yiming Cui, Xin Yao, Yuxuan Qin +3

Multimodal scientific reasoning remains a significant challenge for large language models (LLMs), particularly in chemistry, where problem-solving relies on symbolic diagrams, mole…

cs.LG2025

Memory-Amortized Inference: A Topological Unification of Search, Closure, and Structure

Xin Li

Contemporary ML separates the static structure of parameters from the dynamic flow of inference, yielding systems that lack the sample efficiency and thermodynamic frugality of bio…

q-bio.NC2025

The Geometry of Certainty: Recursive Topological Condensation and the Limits of Inference

Xin Li

Computation fundamentally separates time from space: nondeterministic search is exponential in time but polynomially simulable in space (Savitch's Theorem). We propose that the bra…

cs.CL2025

InfoAgent: Advancing Autonomous Information-Seeking Agents

Gongrui Zhang, Jialiang Zhu, Ruiqi Yang +15

Building Large Language Model agents that expand their capabilities by interacting with external tools represents a new frontier in AI research and applications. In this paper, we…