2 citations · 5 across the 23 of their papers we have counts for
9 papers · 1 filter
Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space
Chao Chen, Zhixin Ma, Yongqi Li +4
Multimodal reasoning aims to enhance the capabilities of MLLMs by incorporating intermediate reasoning steps before reaching the final answer. It has evolved from text-only reasoni…
Merlin's Whisper: Enabling Efficient Reasoning in Large Language Models via Black-box Persuasive Prompting
Heming Xia, Cunxiao Du, Rui Li +3
Large reasoning models (LRMs) have demonstrated remarkable proficiency in tackling complex tasks through step-by-step thinking. However, this lengthy reasoning process incurs subst…
Parallel Test-Time Scaling for Latent Reasoning Models
Runyang You, Yongqi Li, Meng Liu +3
Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains-of-thought in parallel and aggr…
Rec: Towards Large Recommender Models with Reasoning
Runyang You, Yongqi Li, Xinyu Lin +4
Large recommender models have extended LLMs as powerful recommenders via encoding or item generation, and recent breakthroughs in LLM reasoning synchronously motivate the explorati…
Towards Harmless Multimodal Assistants with Blind Preference Optimization
Yongqi Li, Lu Yang, Jian Wang +3
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction. Given the extensive applications of MLLM…
Exploring Training and Inference Scaling Laws in Generative Retrieval
Hongru Cai, Yongqi Li, Ruifeng Yuan +4
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel par…
Tutorial Proposal: Speculative Decoding for Efficient LLM Inference
Heming Xia, Cunxiao Du, Yongqi Li +2
This tutorial presents a comprehensive introduction to Speculative Decoding (SD), an advanced technique for LLM inference acceleration that has garnered significant research intere…
PEToolLLM: Towards Personalized Tool Learning in Large Language Models
Qiancheng Xu, Yongqi Li, Heming Xia +3
Tool learning has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools. Existing tool learning studies primarily focus on th…
TokenSkip: Controllable Chain-of-Thought Compression in LLMs
Heming Xia, Chak Tou Leong, Wenjie Wang +2
Chain-of-Thought (CoT) has been proven effective in enhancing the reasoning capabilities of large language models (LLMs). Recent advancements, such as OpenAI's o1 and DeepSeek-R1,…