7 papers
DWM: Separating World Effects from Actions in Latent World Models
Yi-Ge Zhang, Tianqi Du, Qi Zhang +1
Latent world models underpin much of modern model-based control, yet current action-conditioned formulations supervise the next-latent transition with a single, undifferentiated ta…
Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning
Tianqi Du, Qi Zhang, Yifei Wang +1
Recently, world models have emerged as a promising paradigm for building intelligent agents by learning predictive models that estimate future environment states conditioned on obs…
Autoregressive Models Rival Diffusion Models at ANY-ORDER Generation
Tianqi Du, Lizhe Fang, Weijie Yang +4
Diffusion language models enable any-order generation and bidirectional conditioning, offering appealing flexibility for tasks such as infilling, rewriting, and self-correction. Ho…
Language Ranker: A Lightweight Ranking framework for LLM Decoding
Chenheng Zhang, Tianqi Du, Jizhe Zhang +4
Conventional research on large language models (LLMs) has primarily focused on refining output distributions, while paying less attention to the decoding process that transforms th…
Long-Short Alignment for Effective Long-Context Modeling in LLMs
Tianqi Du, Haotian Huang, Yifei Wang +1
Large language models (LLMs) have exhibited impressive performance and surprising emergent properties. However, their effectiveness remains limited by the fixed context window of t…
When More is Less: Understanding Chain-of-Thought Length in LLMs
Yuyang Wu, Yifei Wang, Ziyu Ye +3
Large Language Models (LLMs) employ Chain-of-Thought (CoT) reasoning to deconstruct complex problems. While longer CoTs are often presumed superior, this paper challenges that noti…