AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Cross-object alignment among geometrically similar interaction regions during training enables structures on unseen objects that resemble previously observed ones to produce representations similar to those learned during training, facilitating target-region recognition.
Despite progress in vision-language models, 3D spatial reasoning from 2D images remains challenging. Text-based methods describe intermediate geometry with discrete tokens, limiting fidelity for continuous spatial relations. Continuous latents offer richer representations, but a single latent type does not explicitly s...
Latent visual reasoning equips vision--language models with continuous intermediate states that can process visual evidence without explicit textual reasoning traces or repeated image operations. However, existing methods often allow multiple latent tokens to access the same visual evidence through shared value project...
The Detection-Aware Attribution Framework is proposed, which learns the effects of valid attribute replacements and amortizes this intervention evidence into deployment-time diagnosis, and shows that intervention-grounded attribute attribution can connect failure localization to executable system repair.
Discriminative mask-guided diffusion is proposed, a medical anomaly detection framework that complements residual-based localization with reconstruction-shift discrimination and achieves the best overall performance among the state-of-the-art baseline methods.
GeoAAC is proposed, a geometry-based adaptive action chunking method for flow-based VLA policies that adjusts the action horizon according to the reliability of the current action prediction, with geometric variation across action prefixes remaining positively correlated with predictive uncertainty.
A comprehensive, domain-wide knowledge graph built with expert-level precision and professionalism is proposed, constructed through a three-stage agentic pipeline that extracts high-fidelity knowledge cores from authoritative textbooks and strengthens structural connectivity through cross-domain augmentation and genera...
This paper proposes VisualRouter, a training-free and plug-and-play framework for query-grounded visual sampling, which consistently improves multiple LVLMs over uniform sampling, and outperforming existing training-free visual sampling methods under the same setting.
This survey establishes workload-level boundaries and connects system architecture, competence acquisition, and evaluation through a seven-dimensional terminal competence profile, and provides a unified basis for studying terminal-mediated agency across software engineering and emerging application domains.
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