1 citations · 1 across the 8 of their papers we have counts for
8 papers
Benchmark Everything Everywhere All at Once
Shiyun Xiong, Dongming Wu, Peiwen Sun +5
Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their construction is labor-intensiv…
Memorize Theorems, Not Instances: Probing SFT Generalization through Mathematical Reasoning
Ruiying Peng, Mengyu Yang, Jing Lei +3
Supervised Fine-Tuning (SFT) is widely used for task-specific adaptation, yet recent work shows it systematically undermines reasoning generalization. We argue the root cause is no…
BitLM: Unlocking Multi-Token Language Generation with Bitwise Continuous Diffusion
Shaobin Zhuang, Yuang Ai, Jiaming Han +7
Autoregressive language models generate text one token at a time, yet natural language is inherently structured in multi-token units, including phrases, n-grams, and collocations t…
Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM
Wenqian Cui, Xiao-Hui Li, Daxin Tan +2
Speech large language models (SLMs) are typically built from text large language model (TLM) checkpoints, yet they still suffer from a substantial modality gap. Prior work has main…
Deeper Thought, Weaker Aim: Understanding and Mitigating Perceptual Impairment during Reasoning in Multimodal Large Language Models
Ruiying Peng, Xueyu Wu, Jing Lei +3
Multimodal large language models (MLLMs) often suffer from perceptual impairments under extended reasoning modes, particularly in visual question answering (VQA) tasks. We identify…
MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models
He Zhang, Wenqian Cui, Haoning Xu +5
Full-Duplex Speech Language Models (FD-SLMs) enable real-time, overlapping conversational interactions, offering a more dynamic user experience compared to traditional half-duplex…
TurnGuide: Enhancing Meaningful Full Duplex Spoken Interactions via Dynamic Turn-Level Text-Speech Interleaving
Wenqian Cui, Lei Zhu, Xiaohui Li +4
Full-Duplex Speech Language Models (FD-SLMs) are specialized foundation models designed to enable natural, real-time spoken interactions by modeling complex conversational turn-tak…
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs
Jierun Chen, Tiezheng Yu, Haoli Bai +11
Large vision-language models (VLMs) increasingly adopt post-training techniques such as long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL) to…