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Weida Wang

Shanghai AI Laboratory

22 papers hereh-index 9189 citations28 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author18

Across the 21 of 22 papers where every author was matched, so the position is known.

fields
  • cs.AI8
  • cs.LG4
  • cs.CL3
  • cs.CV3
  • cs.CE2
  • cs.CR1
affiliations
  • Shanghai AI Laboratory
Homepage
same name
  • Weida Wang — 4 papers, h 3
  • Weida Wang — 2 papers, h 3
  • Weida Wang — 1 paper
  • Weida Wang — 1 paper, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedSpeak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation

2 citations · 2 across the 11 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

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…

cs.LG2025

ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System

Dong Han, Zhehong Ai, Pengxiang Cai +16

Bayesian optimization (BO) is a powerful tool for scientific discovery in chemistry, yet its efficiency is often hampered by the sparse experimental data and vast search space. Her…

cs.LG2025

CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics

Weida Wang, Dongchen Huang, Jiatong Li +32

We introduce CMPhysBench, designed to assess the proficiency of Large Language Models (LLMs) in Condensed Matter Physics, as a novel Benchmark. CMPhysBench is composed of more than…

cs.LG2025

DSADF: Thinking Fast and Slow for Decision Making

Zhihao Dou, Dongfei Cui, Jun Yan +5

Although Reinforcement Learning (RL) agents are effective in well-defined environments, they often struggle to generalize their learned policies to dynamic settings due to their re…

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