1 citations · 1 across the 3 of their papers we have counts for
4 papers
CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts
Lianyu Hu, Shengqian Qin, Zeqin Liao +4
Chain-of-thought (CoT) reasoning has enabled multi-modal large language models (MLLMs) to tackle complex visual reasoning tasks by generating explicit intermediate reasoning steps…
Dual Tuning for Reasoning Efficacy-Driven Data Curation in Multimodal LLM Training
Ruobing Zheng, Tianqi Li, Jianing Li +3
Reasoning post-training improves Large Language Models (LLMs) on complex tasks such as mathematics and coding, but its benefits across diverse multimodal tasks remains uncertain. T…
Learn Like Humans: Use Meta-cognitive Reflection for Efficient Self-Improvement
Xinmeng Hou, Peiliang Gong, Bohao Qu +3
While Large Language Models (LLMs) enable complex autonomous behavior, current agents remain constrained by static, human-designed prompts that limit adaptability. Existing self-im…
Knowing When to Critique: Task-Adaptive Metacognitive Regulation for Reliable LLM Reasoning
Xinmeng Hou, Ziting Chang, Zhouquan Lu +6
Large language models (LLMs) reason fluently but do not regulate their reasoning: they apply uniform scrutiny to every input, which leaves them vulnerable to adversarial and counte…