collaborators

5 papers

cs.CV2026

MechReason: Benchmarking Multi-Image Multi-Hop Reasoning in Mechanical Engineering

Tengyue Wang, Kang An, Chenxu Du +8

Despite significant progress in general visual question answering and cross-modal understanding, multimodal large language models still face a pronounced gap in evaluation for comp…

cs.AI2026

CircuitReason-1k: Benchmarking Long-Horizon Visual-to-Symbolic Reasoning inElectrical Circuits

Xinqi Yang, Kang An, Tengyue Wang +8

Electrical circuit analysis requires more than recognizing components in an image. A solver must ground symbols and labels, recover latent topology, select a physical model, formul…

cs.AI2026

MMArch: Benchmarking Multimodal Reasoning Grounded in Architectural Evidence

Chenxu Du, Kang An, Tengyue Wang +8

Multimodal large language models (MLLMs) perform strongly on engineering imagery, yet existing benchmarks mostly test drawing recognition, information extraction, or compliance che…

cs.AI2025

When Modalities Conflict: How Unimodal Reasoning Uncertainty Governs Preference Dynamics in MLLMs

Zhuoran Zhang, Tengyue Wang, Xilin Gong +4

Multimodal large language models (MLLMs) must resolve conflicts when different modalities provide contradictory information, a process we term modality following. Prior work measur…

cs.SE2025

CODEMENV: Benchmarking Large Language Models on Code Migration

Keyuan Cheng, Xudong Shen, Yihao Yang +6

Large language models (LLMs) have shown remarkable capabilities across various software engineering tasks; however, their effectiveness in code migration, adapting code to run in d…