20 papers
DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning
Chao Huang, Zeliang Zhang, Jiang Liu +7
Multimodal large language models (MLLMs) have made rapid progress, yet their reasoning ability often lags behind strong text-only LLMs. Bridging this gap typically requires large-s…
XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models
Xingrui Wang, Jiang Liu, Chao Huang +7
Omni-modal large language models (OLLMs) aim to unify audio, vision, and text understanding within a single framework. While existing benchmarks primarily evaluate general cross-mo…
VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking
Jingyang Lin, Jialian Wu, Jiang Liu +6
Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely sampled video frames, result…
Reliable Use of Lemmas via Eligibility Reasoning and SectionAware Reinforcement Learning
Zhikun Xu, Xiaodong Yu, Ben Zhou +6
Recent large language models (LLMs) perform strongly on mathematical benchmarks yet often misapply lemmas, importing conclusions without validating assumptions. We formalize lemma$…
CD4LM: Consistency Distillation and aDaptive Decoding for Diffusion Language Models
Yihao Liang, Ze Wang, Hao Chen +7
Autoregressive large language models achieve strong results on many benchmarks, but decoding remains fundamentally latency-limited by sequential dependence on previously generated…
Unleashing Hour-Scale Video Training for Long Video-Language Understanding
Jingyang Lin, Jialian Wu, Ximeng Sun +8
Recent long-form video-language understanding benchmarks have driven progress in video large multimodal models (Video-LMMs). However, the scarcity of well-annotated long videos has…