AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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VerNav, a verifier-first framework for low-latency LLM-based VLN, achieves competitive navigation performance among representative LLM-based VLN agents while reducing average decision-stage LLM latency per step by more than $10\times$ compared with autoregressive methods.
This work identifies a new implicit attack surface for skill selection: even when the user prompt and skill description appear benign in isolation, their semantic relationship can still be strategically shaped to favor an attacker-chosen skill.
This work demonstrates for the first time that mode connectivity between independently trained DDPM and NanoCLIP modes is discovered, and provides a novel perspective for understanding the geometric properties of the loss landscapes in modern generative and contrastive models.
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