1 citations · 1 across the 8 of their papers we have counts for
8 papers · 1 filter
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…