5 citations · 10 across the 18 of their papers we have counts for
20 papers
How Should Diffusion Language Models Edit Code?
Xijia Tao, Ziru Liu, Shansan Gong +7
Code editing requires a model to decide where to make changes, generate the new content, and preserve everything else. We study how masked diffusion language models divide these re…
SoftSkill: Behavioral Compression for Contextual Adaptation
Xijia Tao, Yihua Teng, Xinyu Fu +6
Natural-language skills let agents reuse task knowledge, yet deploying a long Markdown document makes the model interpret that knowledge anew on every call. We ask whether the beha…
DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning Models
Zirui Wu, Lin Zheng, Jiacheng Ye +5
Block diffusion language models accelerate decoding through parallel block-wise denoising, yet whether they can be reliably scaled for long chain-of-thought (CoT) reasoning remains…
Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone
Jiacheng Ye, Shansan Gong, Jiahui Gao +6
While autoregressive Large Vision-Language Models (VLMs) have achieved remarkable success, their sequential generation often limits their efficacy in complex visual planning and dy…
DynaAct: Large Language Model Reasoning with Dynamic Action Spaces
Xueliang Zhao, Wei Wu, Jian Guan +2
In modern sequential decision-making systems, the construction of an optimal candidate action space is critical to efficient inference. However, existing approaches either rely on…
PromptCoT 2.0: Scaling Prompt Synthesis for Large Language Model Reasoning
Xueliang Zhao, Wei Wu, Jian Guan +2
Large language models (LLMs) are evolving from conversational systems into strong reasoners for tasks such as Olympiad mathematics and competitive programming. While scaling parame…
Dream-Coder 7B: An Open Diffusion Language Model for Code
Zhihui Xie, Jiacheng Ye, Lin Zheng +8
We present Dream-Coder 7B, an open-source discrete diffusion language model for code generation that exhibits emergent any-order generation capabilities. Unlike traditional autoreg…
Scaling Reasoning without Attention
Xueliang Zhao, Wei Wu, Lingpeng Kong
Large language models (LLMs) have made significant advances in complex reasoning tasks, yet they remain bottlenecked by two core challenges: architectural inefficiency due to relia…
PromptCoT: Synthesizing Olympiad-level Problems for Mathematical Reasoning in Large Language Models
Xueliang Zhao, Wei Wu, Jian Guan +1
The ability of large language models to solve complex mathematical problems has progressed significantly, particularly for tasks requiring advanced reasoning. However, the scarcity…
Implicit Search via Discrete Diffusion: A Study on Chess
Jiacheng Ye, Zhenyu Wu, Jiahui Gao +4
In the post-AlphaGo era, there has been a renewed interest in search techniques such as Monte Carlo Tree Search (MCTS), particularly in their application to Large Language Models (…
Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration
Qintong Li, Jiahui Gao, Sheng Wang +6
Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data, leading to impressive performance across a range of downstream…
Scaling Diffusion Language Models via Adaptation from Autoregressive Models
Shansan Gong, Shivam Agarwal, Yizhe Zhang +9
Diffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling, potentially addressing limitations of autoregressive (AR) models. However, c…
Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning
Jiacheng Ye, Jiahui Gao, Shansan Gong +4
Autoregressive language models, despite their impressive capabilities, struggle with complex reasoning and long-term planning tasks. We introduce discrete diffusion models as a nov…
ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom
Jingqi Zhou, Sheng Wang, Jingwei Dong +6
Large vision-language models (LVLMs) have witnessed significant progress on visual understanding tasks. However, they often prioritize language knowledge over image information on…
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models
Xueliang Zhao, Xinting Huang, Tingchen Fu +5
Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs). The integration with Domain-Specific Languages (DSL), offering precise visual represen…
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers
Qintong Li, Leyang Cui, Xueliang Zhao +2
Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks. However, there are increasing debates regarding whether these mo…
Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models
Jiacheng Ye, Shansan Gong, Liheng Chen +8
Recently, diffusion models have garnered significant interest in the field of text processing due to their many potential advantages compared to conventional autoregressive models.…
G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model
Jiahui Gao, Renjie Pi, Jipeng Zhang +8
Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mat…
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving
Xueliang Zhao, Xinting Huang, Wei Bi +1
Large Language Models (LLMs) have driven substantial progress in artificial intelligence in recent years, exhibiting impressive capabilities across a wide range of tasks, including…
Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks
Qintong Li, Leyang Cui, Lingpeng Kong +1
Previous work adopts large language models (LLMs) as evaluators to evaluate natural language process (NLP) tasks. However, certain shortcomings, e.g., fairness, scope, and accuracy…