AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Alignment does not eliminate behavioral errors in language models. Models may still refuse benign requests, call unnecessary tools, or yield to false user claims. Current methods mitigate such errors as a computation problem, and rarely explore if the desired behavior is already encoded in the model's representation. M...
This work proposes GUIDER (Generative User Interest Discovery & Explicit Reasoning), a framework that fundamentally decouples intent planning from item matching by reformulating sequential modeling within a rigorous closed-set semantic interest space.
The emergence of Large Language Models (LLMs) has driven a paradigm shift in sequential recommendation from discriminative ranking to generative modeling. However, existing generative approaches predominantly rely on semantic IDs (SIDs)—discrete identifiers derived from hierarchical quantization that function as semant...
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