8 papers
Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing
Lecheng Yan, Yichong Zhang, Xiantao Xu +12
Long-form video editing over heterogeneous footage requires agents to coordinate source selection, multimodal analysis, timeline construction, narration and subtitle alignment, ren…
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech
Fei Yang, Xuanfan Ni, Renyi Yang +7
Recent advances in audio-language models have demonstrated remarkable success on short, segment-level speech tasks. However, real-world applications such as meeting transcription,…
Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs
Lecheng Yan, Ruizhe Li, Guanhua Chen +5
Reinforcement Learning with Verifiable Rewards (RLVR) is highly effective for enhancing LLM reasoning, yet recent evidence shows models like Qwen 2.5 achieve significant gains even…
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language Models
Bo Zeng, Chenyang Lyu, Sinuo Liu +14
Instruction-following capability has become a major ability to be evaluated for Large Language Models (LLMs). However, existing datasets, such as IFEval, are either predominantly m…
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Jiahui Geng, Fengyu Cai, Shaobo Cui +8
Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance…
HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs
Qing Li, Jiahui Geng, Zongxiong Chen +5
In recent years, large language models (LLMs) have made remarkable advancements, yet hallucination, where models produce inaccurate or non-factual statements, remains a significant…
SAUCE: Selective Concept Unlearning in Vision-Language Models with Sparse Autoencoders
Qing Li, Jiahui Geng, Derui Zhu +3
Unlearning methods for vision-language models (VLMs) have primarily adapted techniques from large language models (LLMs), relying on weight updates that demand extensive annotated…