1 citations · 2 across the 15 of their papers we have counts for
17 papers
Coding Agent Memory Post-training: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via Reinforcement Learning
Lirui Luo, Kelong Mao, Heming Xia +10
Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. Recent work has begun to use reinforcement learning to m…
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…
ToolSpec: Accelerating Tool Calling via Schema-Aware and Retrieval-Augmented Speculative Decoding
Heming Xia, Yongqi Li, Cunxiao Du +2
Tool calling has greatly expanded the practical utility of large language models (LLMs) by enabling them to interact with external applications. As LLM capabilities advance, effect…
Finding RELIEF: Shaping Reasoning Behavior without Reasoning Supervision via Belief Engineering
Chak Tou Leong, Dingwei Chen, Heming Xia +4
Large reasoning models (LRMs) have achieved remarkable success in complex problem-solving, yet they often suffer from computational redundancy or reasoning unfaithfulness. Current…
LLM-REVal: Can We Trust LLM Reviewers Yet?
Rui Li, Jia-Chen Gu, Po-Nien Kung +5
The rapid advancement of large language models (LLMs) has inspired researchers to integrate them extensively into the academic workflow, potentially reshaping how research is pract…
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…
From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs
Haonan Bian, Yutao Qi, Rui Yang +4
Large Language Models (LLMs), despite their success in question answering, exhibit limitations in complex multi-hop question answering (MQA) tasks that necessitate non-linear, stru…
Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
Xinghao Chen, Anhao Zhao, Heming Xia +7
Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT relies on explicitly verbalized…
KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization
Mingbo Song, Heming Xia, Jun Zhang +4
Speculative Decoding (SD) has emerged as a widely used paradigm to accelerate the inference of large language models (LLMs) without compromising generation quality. It works by eff…
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…
How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation
Rui Li, Heming Xia, Xinfeng Yuan +4
Recently, LLMs have garnered increasing attention across academic disciplines for their potential as human digital twins, virtual proxies designed to replicate individuals and auto…
Beyond Single Frames: Can LMMs Comprehend Temporal and Contextual Narratives in Image Sequences?
Xiaochen Wang, Heming Xia, Jialin Song +9
Large Multimodal Models (LMMs) have achieved remarkable success across various visual-language tasks. However, existing benchmarks predominantly focus on single-image understanding…
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,…
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction
Hongru Wang, Rui Wang, Boyang Xue +5
Large Language Models (LLMs) can interact with the real world by connecting with versatile external APIs, resulting in better problem-solving and task automation capabilities. Prev…
SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration
Heming Xia, Yongqi Li, Jun Zhang +2
Speculative decoding (SD) has emerged as a widely used paradigm to accelerate LLM inference without compromising quality. It works by first employing a compact model to draft multi…
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…