AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Motivated by the human ability to integrate experience across language, perception, and action, we introduce UniWALM, a unified world-action-language model that learns video, action, and language in a single shared representation, where each modality is both a conditioning signal and a supervision target. We ask: can j...
Evidence-Gated Regularization (EGR) is proposed, a modality-agnostic training objective that introduces zero inference-time overhead and derives a per-frame and per-sensor task-relevance signal to gate two state-conditional consistency objectives: invariance on low-evidence sensors, and single-sensor sufficiency on hig...
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