AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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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