activity
20192026
most citedIf LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents

14 citations · 67 across the 13 of their papers we have counts for

collaborators

22 papers

cs.CL2026

AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints

Jiayu Liu, Cheng Qian, Zhenhailong Wang +10

Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are progressively disclosed through…

cs.CL2025

Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning

Qi He, Cheng Qian, Xiusi Chen +3

Claim verification with large language models (LLMs) has recently attracted growing attention, due to their strong reasoning capabilities and transparent verification processes com…

cs.CV2025

EMCompress: Video-LLMs with Endomorphic Multimodal Compression

Zheyu Fan, Jiateng Liu, Yuji Zhang +4

Video-LLMs face a fundamental tension in long-video reasoning: static, sparse frame sampling either dilutes evidence across task-irrelevant segments at significant cost or misses f…

cs.CL20251 cited

The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination

Yuji Zhang, Sha Li, Cheng Qian +8

Hallucination is a persistent challenge in large language models (LLMs), where even with rigorous quality control, models often generate distorted facts. This paradox, in which err…

cs.CL2024

Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks

Jiayi He, Hehai Lin, Qingyun Wang +2

While Vision-Language Models (VLMs) have shown remarkable abilities in visual and language reasoning tasks, they invariably generate flawed responses. Self-correction that instruct…

cs.CL20247 cited

Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models

Yuji Zhang, Sha Li, Jiateng Liu +5

Hallucination is often regarded as a major impediment for using large language models (LLMs), especially for knowledge-intensive tasks. Even when the training corpus consists solel…