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cs.CL2026
Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis
Zicheng Kong, Dehua Ma, Zhenbo Xu +9
Multimodal large language models (MLLMs) struggle with alignment due to the limitations of existing reward models (RMs), which are predominantly vision-centric, dependent on costly…
cs.CL2025
Select-Then-Decompose: From Empirical Analysis to Adaptive Selection Strategy for Task Decomposition in Large Language Models
Shuodi Liu, Yingzhuo Liu, Zi Wang +4
Large language models (LLMs) have demonstrated remarkable reasoning and planning capabilities, driving extensive research into task decomposition. Existing task decomposition metho…
cs.CL2025
Do MLLMs Really Understand the Charts?
Xiao Zhang, Dongyuan Li, Liuyu Xiang +3
Although Multimodal Large Language Models (MLLMs) have demonstrated increasingly impressive performance in chart understanding, most of them exhibit alarming hallucinations and sig…