16 papers
Human Cognition in Machines: A Unified Perspective of World Models
Timothy Rupprecht, Pu Zhao, Amir Taherin +20
This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To…
OmniMem: Scalable and Adaptive Memory Retrieval for Long Video Generation
Lin Zhao, Yushu Wu, Yifan Gong +2
Autoregressive (AR) video generation extends videos by producing latent chunks sequentially, but scaling to long videos requires repeated access to a growing historical KV cache. E…
Structured Agent Distillation for Large Language Model
Jun Liu, Zhenglun Kong, Peiyan Dong +10
Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-style frameworks. Yet, their practical de…
Efficient Reasoning with Hidden Thinking
Xuan Shen, Yizhou Wang, Yufa Zhou +4
Chain-of-Thought (CoT) reasoning has become a powerful framework for improving complex problem-solving capabilities in Multimodal Large Language Models (MLLMs). However, the verbos…
Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment
Jun Liu, Zhenglun Kong, Pu Zhao +9
Structured pruning for large language models (LLMs) has garnered significant academic interest due to its ability to efficiently compress and accelerate LLMs by eliminating redunda…
HIERAMP: Coarse-to-Fine Autoregressive Amplification for Generative Dataset Distillation
Lin Zhao, Xinru Jiang, Xi Xiao +7
Dataset distillation often prioritizes global semantic proximity when creating small surrogate datasets for original large-scale ones. However, object semantics are inherently hier…