Papers by Zheyuan Yang
Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs (2026.findings-acl)
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| Challenge: | Existing benchmarks primarily evaluate planning and execution success, overlooking the self-reflective dimension of tool use. |
| Approach: | They propose a benchmark to assess LLMs’ self-reflective reasoning in tool-augmented multi-turn dialogues. |
| Outcome: | The proposed benchmark covers 10 domains with 88 distinct APIs and 968 annotated dialogues, systematically injecting diverse error types arising from both user and assistant behavior. |
TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity (2026.findings-acl)
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Zheyuan Yang, Liqiang Shang, Junjie Chen, Xun Yang, Chenglong Xu, Bo Yuan, Chenyuan Jiao, Yaoru Sun, Yilun Zhao
| Challenge: | TableVista evaluates multimodal table reasoning under visual and structural complexity . current models struggle to maintain reasoning consistency when structural complexity combined with visually integrated presentations. |
| Approach: | They propose a benchmark for evaluating multimodal table reasoning under visual and structural complexity. |
| Outcome: | The proposed model performs poorly on visual and structural complexity. |
Table-R1: Inference-Time Scaling for Table Reasoning Tasks (2025.emnlp-main)
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| Challenge: | In this study, we explore inference-time scaling on table reasoning tasks. |
| Approach: | They propose a large-scale dataset of reasoning traces and a reinforcement learning with verifiable rewards approach to enable inference-time scaling on table reasoning tasks. |
| Outcome: | The proposed model matches or exceeds GPT-4.1 and DeepSeek-R1 models on diverse table reasoning tasks. |
MMSciCode: Real-world Evaluation of Multilingual Multi-Discipline Scientific Research Coding (2026.acl-long)
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| Challenge: | MMSciCode is a benchmark for evaluating foundation models in scientific code generation. |
| Approach: | They propose a multilingual, multi-discipline benchmark for evaluating foundation models in scientific code generation that integrates domain-specific knowledge with algorithmic reasoning. |
| Outcome: | The new benchmark is annotated by domain experts and features rigorous quality controls to ensure dataset integrity and authenticity. |
Can LLMs Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure (2025.acl-short)
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| Challenge: | TestCase-Eval focuses on Fault Coverage and Fault Exposure tasks . authors provide insights into their strengths and limitations in generating effective test cases . correctness and robustness of algorithmic solutions hinge on quality of test suites . |
| Approach: | They introduce TestCase-Eval, a benchmark for systematic evaluation of LLMs in test-case generation. |
| Outcome: | The new benchmark measures the performance of LLMs in test-case generation. |