AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work evaluates three commit-time guard granularities (global epoch, read-set version, semantic commit predicate), multi-level verification, and model-side gates on three locally hosted quantized model families, and investigates how precisely runtime guards distinguish invalidating races.
Agentic systems commit state-changing actions, but additional verifiers can inherit the same upstream fault. We present VP-CONTROL, a runtime-assurance design and deterministic benchmark for cost-aware commit gates. Its 48 task templates yield 2,880 scenarios across six fault regimes. A fixed-call 2 x 2 experiment sepa...
Financial AI systems often train information extractors on one textual register and deploy them across filings, news, and user-generated content, and standard F1 scores do not indicate which predictions remain safe to automate when that input distribution changes. We study confidence estimation and selective prediction...
An episode-level evaluation protocol for healthcare NLP agents is introduced, supplying a reproducible intermediate evaluation layer between static benchmarks and prospective workflow studies, with an explicit cost-sensitive treatment of missed versus unnecessary escalation.
The memory-clarification boundary is studied: whether interaction-derived information should be persisted, used only in the current context, re-verified, or clarified with the user, as well as across Claude and Qwen.
It is argued that public tutoring benchmarks can better support positive-impact evaluation by reporting solving-oriented and pedagogy-oriented scores separately and by making disclosure-sensitive, student-agency-preserving criteria more explicit.
This work describes coordinated perturbation as a budgeted subset-selection problem over pairwise observations and introduces an Adaptive Subset Selection Attack (ASSA) as a scalable search heuristic for probing high-impact perturbation sets.
JailMeter is proposed, an evidence-based evaluation framework designed to more faithfully measure jailbreak effectiveness and distill into a small language model, JailMeter\textsubscript{SLM}, which maintains comparable reliability with significantly reduced computational costs.
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