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20242026
most citedQCBench: Evaluating Large Language Models on Domain-Specific Quantitative Chemistry

3 citations · 8 across the 23 of their papers we have counts for

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13 papers · 1 filter

cs.CL2026

Molecular LLM Agents: From Architectural Design to Scientific Autonomy

Jiatong Li, Wengyu Zhang, Weida Wang +8

Molecular science represents an important frontier for LLM-based agents. Unlike general agents that mainly operate over natural language, code, or web environments, molecular LLM a…

cs.AI2026

Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning

Kai Chen, Jifeng Ding, Ning Ding +44

We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve comp…

cs.CE2026

Chemical Chain-of-Thought Functions as a Hallucination-Prone Molecular Scratchpad

Jiatong Li, Yuxuan Ren, Weida Wang +2

Chemical reasoning language models are expected to derive molecular answers through faithful chain-of-thought (CoT). However, across four reasoning model families and twelve chemis…

cs.CR2026

AMRM-Pure: Semantic-Preserving Adversarial Purification

Zhihao Dou, Zhiqiang Gao, Dongfei Cui +6

Adversarial purification is a defense technique that employs generative models to remove adversarial perturbations. Current methods often rely on powerful generators, typically dif…

cs.LG2026

Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis

Jiatong Li, Weida Wang, Changmeng Zheng +4

Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete sequential tokens and the rigid…

physics.comp-ph2026

Deep Research in Physical Sciences: A Multi-Agent Framework and Comprehensive Benchmark

Yigeng Jiang, Tengchao Yang, Taoyong Cui +25

Deep research agents are Large Language Model (LLM)-based systems designed for autonomous, multi-step scientific reasoning, and they hold immense potential for accelerating researc…