As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to assess multimodal perception, multi-step execution, tool use, and artifact delivery. However, existing benchmarks are often tied to specific task types, execution environ...
Yu Liu, Zhi-Lin Liu, Zhi-Wei Yang et al.· 0 citations
Metric reasoning is a critical and challenging task for Vision Language Models (VLMs), playing a pivotal role in embodied AI tasks such as robotic manipulation and autonomous navigation. However, current spatial reasoning remains bottlenecked by rigid pixel-level supervision; such localized optimization often compromis...
Yu-Ling Xi, Hao-Kai Zhang, Mu-Zhi Zhu et al.· 0 citations
The MaP is proposed, a unified framework that seamlessly harmonizes divergent GUI navigation tasks and effectively mitigates gradient conflicts and significantly outperforms the direct mixture training, establishing a robust paradigm for multi-task GUI navigation.
Yan Zhang, Pei Fu, Dai-Qing Wu et al.· 0 citations
This work revisits catastrophic forgetting from an interpretability perspective and introduces correctness-guided layer alignment (CLA), which allows the new model to inherit multi-layer semantic consistency from its predecessor while learning from ground-truth supervision.
Xu-Sheng Cao, Yi-Fan Meng, Jia-Ze Li et al.· International Journal of Com...· 0 citations
DAREBench (Deployment-Aware and Reliable Evaluation of Models as Agents), a benchmark designed to capture workload variation and support reliable agent evaluation, is introduced, suggesting that agent deployment and model selection should consider workload profiles, deployment mode, and accuracy--cost trade-offs rather...
Yu Liu, Zhi-Lin Liu, Zhi-Wei Yang et al.· 0 citations
This work proposes NTEP-R (NTEP Reward), a supervision mechanism ensuring that each tool invocation strictly advances the reasoning process toward the final solution, and introduces a non-repeated-goal regularizer to penalize redundant calls that revisit satisfied NTEP goals.
Xing-Ming Long, Yu Liu, Zhi-Wei Yang et al.· 0 citations
This paper revisit VLM inference and presents a new efficient guidance scheme that complements similarity-based guidance, and proposes Cross Modal Residual (CMR), a training-free visual token compression method that combines CMR, text-attention relevance, and residual-space diversity to retain task-relevant and complem...
Congyang Ou, Rui-Ke Song, Yang Zhou et al.· 1 citation
DeltaV is proposed, a ULMM that replaces full-image generation with visual updates and introduces a temporal similarity (TSIM) Router, which stops allocating tokens once the marginal reconstruction gain falls below a threshold.
Pengjie Wang, Linger Deng, Zujian Zhang et al.· 1 citation
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