8 papers
DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation
Zining Liu, Yunhai Hu, Tianhua Xia +4
Speculative decoding (SD) has proven to be an effective technique for accelerating autoregressive generation in large language models (LLMs) however, its application to vision-lang…
DREAM-R: Multimodal Speculative Reasoning with RL-Based Refined Drafting, Precise Verification, and Fully Parallel Execution
Yunhai Hu, Zining Liu, Xiangyang Yin +5
Speculative reasoning has recently been proposed as a means to accelerate reasoning-intensive generation in large multimodal models, but its effectiveness is often constrained by m…
DSL-R1: From SQL to DSL for Training Retrieval Agents across Structured and Unstructured Data with Reinforcement Learning
Yunhai Hu, Junwei Zhou, Yumo Cao +8
Effective retrieval in complex domains requires bridging the gap between structured metadata and unstructured content. Existing systems typically isolate these capabilities, relyin…
AutoNeural: Co-Designing Vision-Language Models for NPU Inference
Wei Chen, Liangmin Wu, Yunhai Hu +9
While Neural Processing Units (NPUs) offer high theoretical efficiency for edge AI, state-of-the-art Vision--Language Models (VLMs) tailored for GPUs often falter on these substrat…
DREAM: Drafting with Refined Target Features and Entropy-Adaptive Cross-Attention Fusion for Multimodal Speculative Decoding
Yunhai Hu, Tianhua Xia, Zining Liu +6
Speculative decoding (SD) has emerged as a powerful method for accelerating autoregressive generation in large language models (LLMs), yet its integration into vision-language mode…
MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search
Yunhai Hu, Yilun Zhao, Chen Zhao +1
We introduce MCTS-RAG, a novel approach that enhances the reasoning capabilities of small language models on knowledge-intensive tasks by leveraging retrieval-augmented generation…