AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large language model watermarking embeds detectable statistical signals during decoding, but the resulting changes to token probabilities can degrade generation quality. This trade-off is particularly important for code, where small changes in token selection can break syntax or alter program behavior. Existing code wa...
Code watermarking supports provenance tracking for code generated by LLMs. Modifying token selection to embed watermarks as an LLM generates code can create a trade-off between detectability and functional correctness. Other methods instead watermark completed code using predefined transformations or trained neural mod...
Activation-based tools are usually tied to one model's native hidden space, requiring probes, sparse autoencoders, and natural-language interpreters to be rebuilt or rediscovered for each new language model. We present a Universal Activation Bus, a framework that provides a common activation interface across compatible...
LUNA is a linguistically adaptive watermark that combines model-free detection with single-token non-distortion under the standard random-key model, and is the only method that simultaneously achieves AUROC>0.99 and an absolute median perplexity shift below 0.1.
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