2 citations · 2 across the 9 of their papers we have counts for
10 papers
More Is Not Always Better: Cross-Component Interference in LLM Agent Scaffolding
Ming Liu
LLM agent systems are built by stacking scaffolding components (planning, tools, memory, self-reflection, retrieval) assuming more is better. We study cross-component interference…
Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
Ming Liu
Can linearly decodable failure signals in LLM hidden states be leveraged to correct those failures? We investigate this classification-correction gap via Overthinking (OT)--a stabl…
Wan-R1: Verifiable-Reinforcement Learning for Video Reasoning
Ming Liu, Yunbei Zhang, Shilong Liu +2
Video generation models produce visually coherent content but struggle with tasks requiring spatial reasoning and multi-step planning. Reinforcement learning (RL) offers a path to…
MOSAIC: A Universal Agent-Level Interface for Cross-Paradigm Agent Mixing and Human-AI Collaboration
Abdulhamid M. Mousa, Jinhui Pang, Rakhmonberdi Khajiev +5
Existing infrastructure cannot deploy agents from different decision-making paradigms within the same environment, making fair cross-paradigm comparison under identical conditions…
Reasoning Multimodal Large Language Model: Data Contamination and Dynamic Evaluation
Ming Liu, Wensheng Zhang
Multimodal Large Language Models (MLLMs) show impressive vision-language benchmark performance, yet growing concerns about data contamination (test set exposure during training) ri…
Is your multimodal large language model a good science tutor?
Ming Liu, Liwen Wang, Wensheng Zhang
Multimodal large language models (MLLMs) demonstrate impressive performance on scientific reasoning tasks (e.g., ScienceQA). However, most existing benchmarks focus narrowly on the…
Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving
Ming Liu, Siyuan Liang, Koushik Howlader +3
Vision-Language Models (VLMs) have been integrated into autonomous driving systems to enhance reasoning capabilities through tasks such as Visual Question Answering (VQA). However,…
Is Your Video Language Model a Reliable Judge?
Ming Liu, Wensheng Zhang
As video language models (VLMs) gain more applications in various scenarios, the need for robust and scalable evaluation of their performance becomes increasingly critical. The tra…
On the robustness of multimodal language model towards distractions
Ming Liu, Hao Chen, Jindong Wang +1
Although vision-language models (VLMs) have achieved significant success in various applications such as visual question answering, their resilience to prompt variations remains an…
On Fairness of Unified Multimodal Large Language Model for Image Generation
Ming Liu, Hao Chen, Jindong Wang +3
Unified multimodal large language models (U-MLLMs) have demonstrated impressive performance in visual understanding and generation in an end-to-end pipeline. Compared with generati…