most citedSpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data

3 citations · 3 across the 1 of their papers we have counts for

collaborators

7 papers

q-bio.QM20263 cited

SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data

Yunyue Su, Jiahui Chen, Zao Jiang +4

Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict themselves to single spectroscopic modaliti…

cs.CL2026

RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation

Jiajun Zhang, Yuying Li, Zhixun Li +13

Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualiz…

cs.CL2026

Gumbel Distillation for Parallel Text Generation

Chi Zhang, Xixi Hu, Bo Liu +1

The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods. However, these non-AR models often sacrifice generation qua…

cs.CV2026

MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation

Wenqing Tian, Hanyi Mao, Zhaocheng Liu +4

Subject-driven image generation is increasingly expected to support fine-grained control over multiple entities within a single image. In multi-reference workflows, users may provi…

cs.CL2026

PlotCraft: Pushing the Limits of LLMs for Complex and Interactive Data Visualization

Jiajun Zhang, Jianke Zhang, Zeyu Cui +7

Recent Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation. However, their ability to create complex visualizations for scaled and structured d…

cs.CV2025

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation

Wen Ye, Zhaocheng Liu, Yuwei Gui +6

Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and m…