1 citations · 1 across the 8 of their papers we have counts for
10 papers
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces
Haotian Liu, Yingjian Chen, Xiaohang Xu +4
Large language models increasingly tackle tasks that require multi-step reasoning, where errors in intermediate claims can affect the final answer. Self-consistency (SC) improves r…
POLAR: Ontology-Guided Risk Prevention for Tool-Calling LLM Agents
Yunju Kang, Seonghyeon Cho, Irene Li +2
LLM tool-use agents operate in dynamic environments where many actions carry operational risk. However, most safety mechanisms react only after errors manifest. Existing pre-emptiv…
SMC-ITA: Sequential Monte Carlo Inference-Time Alignment for Video-to-Audio Generation
Haoyu Zhang, Yuta Oshima, Xingjian Du +4
Video-to-audio (V2A) generation must jointly satisfy audiovisual alignment, semantic consistency, temporal synchronization, and perceptual quality. While prior work has mainly focu…
JMed48k: A Multi-Profession Japanese Medical Licensing Benchmark for Vision-Language Model Evaluation
Yue Xun, Junyu Liu, Qian Niu +10
We introduce JMed48k, a multi-profession Japanese healthcare licensing benchmark for evaluating vision-language models. Built from official PDF materials released by the Japanese M…
Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models
Xiaojie Gu, Sherry T. Tong, Aosong Feng +8
Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, especially in multi-hop QA benchmarks witho…
From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
Yingjian Chen, Haoran Liu, Yinhong Liu +7
Large Language Models (LLMs) show strong reasoning ability in open-domain question answering, yet their reasoning processes are typically linear and often logically inconsistent. I…
Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning
Jerry Huang, Peng Lu, Qiuhao Zeng +5
Ensuring that deep learning models are well-calibrated in terms of their predictive uncertainty is essential in maintaining their trustworthiness and reliability, yet despite incre…
Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning
Fan Gao, Sherry T. Tong, Jiwoong Sohn +11
While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially weaker reasoning in local langua…
MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement
Weitao Jia, Jinghui Lu, Haiyang Yu +17
Recent advances demonstrate that reinforcement learning with verifiable rewards (RLVR) significantly enhances the reasoning capabilities of large language models (LLMs). However, s…
Better Explain Transformers by Illuminating Important Information
Linxin Song, Yan Cui, Ao Luo +2
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings. Prior methods explain Transformers…