AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Trajectory-Aware Evidence Coordination (TAEC), a training-free framework that coordinates evidence use around unresolved answer requirements, achieves the best overall performance against leading training-free visual RAG baselines, with the highest average accuracy across multiple proprietary vision-language models.
It is found that reporting reliability depends on the stage a task has reached, and that almost every deployed model the authors test is reliable at some stages and unreliable at others, indicating that agent frameworks should not control task flow on the strength of the model's state reports alone.
The proposed FLY-EVAL++ is an evidence-driven evaluation protocol that combines deterministic verification of protocol compliance, physical feasibility, and safety constraints with fixed rubric-guided aggregation into interpretable multi-dimensional scores.
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