AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Recursive self-improvement requires models not only to acquire useful training experience, but also to decide when their current experience has ceased to be useful. Existing methods often focus on what experience to generate next, while paying less attention to when fresh experience should be introduced. Many also cons...
This work presents the first music-specific, layer-wise, multi-paradigm empirical study of hallucination in audio-language models and forms it as a hierarchical perceptual grounding failure across five layers: sound events, temporal properties, tonal attributes, style, and emotion.
Findings show that historical evidence use should be evaluated under state variation, rather than inferred from outcome-only correctness, and that open-weight models rely heavily on context continuity and are reluctant to spontaneously switch to tool-mediated retrieval when provenance fails.
Self-improving agents accumulate capability by repeatedly rewriting procedural policies, controllers, or heuristic rules. They typically rely on self-authored tests or metrics to decide whether to accept subsequent edits. The agent controls both the optimized object and its verifier. As a result, self-assigned scores c...
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