7 papers
Behavior Pack Optimization for Video MLLM Post-Training
Zhaolu Kang, Shiyu Liu, Tailong Luo +12
Video multimodal large language models (MLLMs) keep climbing video question answering benchmarks, yet shuffling the frames, masking the segment that supports the answer, or occludi…
SAVOR: Self-Aware Visual Grounding via Confidence-Calibrated Reinforcement Learning for Multimodal Hallucination Mitigation
Zixiu Ding, Zilin Zhao, Yingjie He +3
Multimodal large language models (MLLMs) have made strong progress on visual question answering and image captioning, yet they still produce fluent claims about objects, attributes…
Disentangling Language Roles in Multilingual LLM Task Execution
Qishi Zhan, Minxuan Hu, Seoyeon Jang +7
Multilingual LLMs are increasingly used when instruction, source content, and required response languages do not coincide. Existing benchmarks have expanded multilingual instructio…
A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning
Qishi Zhan, Minxuan Hu, Liang He +2
In limited-data settings, a single endpoint mean of an evaluation metric such as the Continuous Ranked Probability Score (CRPS) is itself a random variable, yet it is routinely rep…
Unstable Rankings in Bayesian Deep Learning Evaluation
Qishi Zhan, Minxuan Hu, Guansu Wang +2
Standard evaluations of Bayesian deep learning methods assume that metric estimates are reliable, but we show this assumption fails under data scarcity. Method rankings are not onl…
How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction
Zhaolu Kang, Yingjie He, Kehan Jiang +18
Large language models (LLMs) excel at semantic understanding, yet their ability to reconstruct internal structure from scrambled inputs remains underexplored. Sentence-level restor…
Speech Recognition Model Improves Text-to-Speech Synthesis using Fine-Grained Reward
Guansu Wang, Peijie Sun
Recent advances in text-to-speech (TTS) have enabled models to clone arbitrary unseen speakers and synthesize high-quality, natural-sounding speech. However, evaluation methods lag…