1 citations · 1 across the 1 of their papers we have counts for
24 papers
Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition
Zhengdong Yang, Qianying Liu, Sheng Li +2
We present a novel approach centered on the decoding stage of Automatic Speech Recognition (ASR) that enhances multilingual performance, especially for low-resource languages. It u…
Mechanistic Diagnostics of Spatial Lexical Bias in Multimodal Large Language Model Spatial Reasoning
Chuang Ma, Qianying Liu, Tomoyuki Obuchi +6
Multimodal large language models (MLLMs) remain unreliable on spatial multiple-choice questions, and their failures are often attributed to poorly attended visual information. In t…
Tailoring the Curriculum: Student-Centered Reasoning Distillation via Dynamic Data-Model Compatibility
Jiahao Huang, Fei Cheng, Junfeng Jiang +1
Reasoning distillation transfers complex reasoning abilities from large language models (LLMs) to smaller ones, yet its success depends on how well the training data align with the…
BenchTrace: A Benchmark for Testing Reflection Ability and Controlled Evolution in LLM Agents
Jiahao Huang, Fei Cheng, Junfeng Jiang +2
Self-evolving agents improve over time by reflecting on past failures, but existing evaluation is limited in two ways: it measures only task scores, leaving reflection quality unkn…
Revisiting Anthropomorphic Reflection Markers in Large Language Model Reasoning
Yahan Yu, Noa Nakanishi, Fei Cheng
Large Language Models (LLMs) often produce explicit reflective traces during complex reasoning, accompanied by anthropomorphic markers such as wait, hmm, and alternatively. Althoug…
Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks
Yihua Zhu, Qianying Liu, Fei Cheng +4
Reinforcement learning with verifiable rewards (RLVR) has become central to post-training reasoning models, yet a key limitation of existing studies is their narrow view of the rea…