30 papers
On Asymmetric Optimization of Reasoning and Perception in Vision-Language Model Post-Training
Xueqing Wu, Yu-Chi Lin, Kai-Wei Chang +1
Post-training has greatly improved reasoning in frontier vision-language models, yet its gains for perception remain comparatively limited, creating a bottleneck for end-to-end vis…
MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks
Hyeonjeong Ha, Qiusi Zhan, Jeonghwan Kim +6
Retrieval-augmented generation (RAG) has become a common practice in multimodal large language models (MLLM) to enhance factual grounding and reduce hallucination. Yet, its relianc…
BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning
Jia-Chen Gu, Junyi Zhang, Di Wu +3
As retrieval-augmented generation (RAG) tackles complex tasks, increasingly expanded contexts offer richer information, but at the cost of higher latency and increased cognitive lo…
OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks
Wenbo Hu, Xin Chen, Yan Gao-Tian +3
Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal Large Language Models. However…
Beyond Facts: Benchmarking Distributional Reading Comprehension in Large Language Models
Pei-Fu Guo, Ya-An Tsai, Chun-Chia Hsu +6
While most reading comprehension benchmarks for LLMs focus on factual information that can be answered by localizing specific textual evidence, many real-world tasks require unders…
LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs
Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu +6
Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior ex…