11 citations · 12 across the 9 of their papers we have counts for
9 papers
What Do Agents Learn from Trajectory-SFT: Semantics or Interfaces?
Weizheng Gu, Chengze Li, Zhuohao Yu +6
Large language models are increasingly evaluated as interactive agents, yet standard agent benchmarks conflate two qualitatively distinct sources of success: semantic tool-use and…
From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models
Hongrui Jia, Chaoya Jiang, Yongrui Heng +2
As Large Multimodal Models (LMMs) scale up and reinforcement learning (RL) methods mature, LMMs have made notable progress in complex reasoning and decision making. Yet training st…
OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents
Hongrui Jia, Jitong Liao, Xi Zhang +7
With advances in decision-making and reasoning capabilities, multimodal agents show strong potential in computer application scenarios. Past evaluations have mainly assessed GUI in…
Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework
Hongrui Jia, Chaoya Jiang, Shikun Zhang +1
With the continuous expansion of Large Language Models (LLMs) and advances in reinforcement learning, LLMs have demonstrated exceptional reasoning capabilities, enabling them to ad…
SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization
Hongrui Jia, Chaoya Jiang, Haiyang Xu +6
As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefi…
ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation
Yijiang Xu, Hongrui Jia, Liguo Chen +8
Fuzz testing is crucial for identifying software vulnerabilities, with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection. However, as the need for t…
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model
Chaoya Jiang, Jia Hongrui, Haiyang Xu +6
This paper presents MaVEn, an innovative Multi-granularity Visual Encoding framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image…
A Survey on Evaluating Large Language Models in Code Generation Tasks
Liguo Chen, Qi Guo, Hongrui Jia +9
This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks. With the ra…
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Chaoya Jiang, Hongrui Jia, Wei Ye +5
Large Vision Language Models exhibit remarkable capabilities but struggle with hallucinations inconsistencies between images and their descriptions. Previous hallucination evaluati…