AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Spotter is proposed, which reverses the roles: the embodied model leads and executes continuously, while the VLM runs in parallel, monitors through a lightweight local screener, intervenes only when an error is detected, reflects on and corrects it, and returns control.
Denoising-Aware Visual Evidence Trajectory Allocation (DAVET), a training-free framework that allocates visual evidence according to the evolving generation state, achieves an average speedup of 1.55 times with an average relative performance drop of 1.86%, showing that denoising-aware visual evidence allocation can re...
A moment-based perspective on policy optimization for LLM reasoning is introduced by treating the failure probability of a randomly sampled problem as a random variable and characterizing optimization objectives through its moments, leading to MMPO, a novel policy optimization framework that jointly minimizes multiple...
This work proposes PeFuse (for pseudo-fusion), a training-free framework that leverages pretrained Diffusion Models and Multimodal Large Language Models to bridge modalities via generative conversion, and introduces two novel strategies: uni-directional and bi-directional conversion, which convert CIR into four single-...
The causes of modal divergence are probed, offering insights into fostering culturally robust MLLMs, and a Multilingual, Multimodal Alignment framework for Cultural grounding evaluation is proposed.
Information Gain-based Rollout Policy Optimization (IGRPO) is proposed, a policy optimization framework that treats intermediate-state informativeness as the organizing principle of rollout collection and performs budget-aware tree-structured rollouts by allocating expansion budget according to node-level informativene...
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