4 citations · 5 across the 12 of their papers we have counts for
13 papers
Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning
Enhan Zhao, Wei Wu, Yuanrui Zhang +2
Spatial reasoning remains a persistent challenge for multimodal large language models (MLLMs). Existing approaches largely rely on large-scale, statically curated datasets, where a…
EntroRouter: Learning Efficient Model Routing via Entropy Regulation
Kaiyi Zhang, Xueliang Zhao, Zhuocheng Gong +2
Model routing balances solution accuracy and computational cost by selecting among models of varying capabilities. While recent multi-round frameworks interleave reasoning and plan…
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…
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…
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…
SubgoalXL: Subgoal-based Expert Learning for Theorem Proving
Xueliang Zhao, Lin Zheng, Haige Bo +3
Formal theorem proving, a field at the intersection of mathematics and computer science, has seen renewed interest with advancements in large language models (LLMs). This paper int…
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…
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…