6 papers
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…
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…
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…
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…
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…
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…