4 citations · 8 across the 14 of their papers we have counts for
14 papers
Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use
Zhixin Ma, Yutong Zhou, Yongqi Li +2
Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical worl…
Optical Reasoning: Rethinking Images as an Expressive Reasoning Medium Beyond Text
Yutong Bian, Dongjie Cheng, Heming Xia +2
Chain-of-Thought (CoT) improves the performance of Large Language Models (LLMs) and has been extended to Multimodal Large Language Models (MLLMs). More recent work further moves fr…
TInR: Exploring Tool-Internalized Reasoning in Large Language Models
Qiancheng Xu, Yongqi Li, Fan Liu +3
Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning. Existing TIR meth…
Agent-as-a-Judge
Runyang You, Hongru Cai, Caiqi Zhang +5
LLM-as-a-Judge has revolutionized AI evaluation by leveraging large language models for scalable assessments. However, as evaluands become increasingly complex, specialized, and mu…
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…
Scaling over Scaling: Exploring Test-Time Scaling Plateau in Large Reasoning Models
Jian Wang, Boyan Zhu, Chak Tou Leong +2
Large reasoning models (LRMs) have exhibited the capacity of enhancing reasoning performance via internal test-time scaling. Building upon this, a promising direction is to further…
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…
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…
Towards Text-Image Interleaved Retrieval
Xin Zhang, Ziqi Dai, Yongqi Li +7
Current multimodal information retrieval studies mainly focus on single-image inputs, which limits real-world applications involving multiple images and text-image interleaved cont…
Interleaved-Modal Chain-of-Thought
Jun Gao, Yongqi Li, Ziqiang Cao +1
Chain-of-Thought (CoT) prompting elicits large language models (LLMs) to produce a series of intermediate reasoning steps before arriving at the final answer. However, when transit…
Enhancing Tool Retrieval with Iterative Feedback from Large Language Models
Qiancheng Xu, Yongqi Li, Heming Xia +1
Tool learning aims to enhance and expand large language models' (LLMs) capabilities with external tools, which has gained significant attention recently. Current methods have shown…
Learning to Rank in Generative Retrieval
Yongqi Li, Nan Yang, Liang Wang +2
Generative retrieval stands out as a promising new paradigm in text retrieval that aims to generate identifier strings of relevant passages as the retrieval target. This generative…
Multiview Identifiers Enhanced Generative Retrieval
Yongqi Li, Nan Yang, Liang Wang +2
Instead of simply matching a query to pre-existing passages, generative retrieval generates identifier strings of passages as the retrieval target. At a cost, the identifier must b…