7 accepted papers
Effective tool use and reasoning are essential capabilities for large reasoning models (LRMs) to address complex real-world problems. Through empirical analysis, we identify a prevalent "Lazy Reasoning" phenomenon, where LRMs frequently engage in repetitive and meaningless reflective reasoning. This…
Recent advances in online reinforcement learning (RL) for large language models (LLMs) have demonstrated promising performance in complex reasoning tasks. However, they often exhibit an imbalanced exploration–exploitation trade-off, resulting in unstable optimization and sub-optimal performance. We …
Post-training quantization (PTQ) with computational equivalence for Large Language Models (LLMs) have demonstrated remarkable advances, however, their application to Multimodal Large Language Models (MLLMs) presents substantial challenges. In this paper, we analyze SmoothQuant as a case study and id
While GUI agents have advanced rapidly, they often lack the robustness to recover from their own errors, hindering real-world deployment. To bridge this gap at both the evaluation and data levels, we introduce GUI-RobustEval and propose Robustness-driven Trajectory Synthesis. GUI-RobustEval containi…
Large language models (LLMs) excel in problem-solving but require training data with diverse reasoning processes. Existing methods mainly optimize instruction-response pairs but lack a systematic design for the underlying reasoning structure. This paper proposes RSS: a Reasoning Structure driven dat…
Activation sparsity can reduce the computational overhead and memory transfers during the forward pass of Large Language Model (LLM) inference. Existing methods face limitations, either demanding time-consuming recovery training that hinders real-world adoption, or relying on empirical magnitude-bas…
There are two main approaches to object detection: CNN-based and Transformer-based. The former views object detection as a dense local matching problem, while the latter sees it as a sparse global retrieval problem. Research in neuroscience has shown that the recognition decision in the brain is bas…