AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A targeted adversarial perturbation can drive a vision-language model's (VLM's) teacher-forced training loss for a fixed target caption to near zero, yet the same model, allowed to generate freely, produces the original, correct description with no trace of the target. We call this dissociation the train/inference gap,...
The model's hidden state provides a way to read, steer, and check tool choice before a call is made, suggesting that the model's hidden state provides a way to read, steer, and check tool choice before a call is made.
This work introduces OptimismBench, which detects directional bias with inverted pairs: each scenario elicits both P(success) and P(failure), and asymmetry between the two framings yields a signed bias score without ground truth.
This work analyzesparse autoencoder features across six models and three SAE families and zero-ablate at full layer depth, finding cross-family claims are sensitive to training methodology, not just activation function or scale.
Behavioral topology is shaped more by the deployment harness than by the LLM, providing a model-agnostic structural primitive for safety auditing and runtime monitoring, and addresses both prediction goals.
This work tracks quantization across 16 models from 8 families under round-to-nearest, seven under AWQ, two under GPTQ and one under GGUF, at 8 down to 2 bits, and measures the margin, the picked option's score minus its best alternative's, which removes the protection a large margin affords.
Web agents observe a browser through text, pixels, or both, and the choice is usually fixed once for all tasks, so a stronger agent can overturn the result, and the rerun noise bands and the full measurement protocol are reported.
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