5 citations · 12 across the 32 of their papers we have counts for
7 papers · 1 filter
Plan Canvas: Fixed Reasoning Regions for Continuous Language Flows
Miaohe Niu, Pengxiang Li, Jingbo Zhu +1
Continuous language flows generate text by denoising all positions of a target canvas together. The natural way to add reasoning to such a model is to write a trace ahead of the an…
StateRAG: Typed State Contracts for Complex Retrieval-Augmented Generation
Miaohe Niu, Xinyu Liu, Pengxiang Li +3
Multi-step retrieval-augmented generation requires tracking evolving information needs, selected evidence, and unresolved gaps. However, free-form trajectories conflate this histor…
Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
Pengxiang Li, Dilxat Muhtar, Tianlong Chen +2
Diffusion Language Models (DLMs) are often advertised as enabling parallel token generation, yet practical fast DLMs frequently converge to left-to-right, autoregressive (AR)-like…
Diffusion Language Models Know the Answer Before Decoding
Pengxiang Li, Yefan Zhou, Dilxat Muhtar +5
Diffusion language models (DLMs) have recently emerged as an alternative to autoregressive approaches, offering parallel sequence generation and flexible token orders. However, the…
Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuning
Xin Xu, Tianhao Chen, Fan Zhang +11
While slow-thinking large language models (LLMs) exhibit reflection-like reasoning, commonly referred to as the "aha moment:, their ability to generate informative critiques and re…
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
Pengxiang Li, Shilin Yan, Joey Tsai +4
Classifier-Free Guidance (CFG) significantly enhances controllability in generative models by interpolating conditional and unconditional predictions. However, standard CFG often e…
InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
Congkai Xie, Shuo Cai, Wenjun Wang +17
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as…