AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is reported that collaboration buys task performance but not lower hallucination as a rule, and no topology is reliably better than another; it is found that collaboration redistributes what its models can already do; it does not supply what they cannot.
Consumer AI health assistants increasingly connect medical records, patient reports, and wearable data across repeated interactions, but evaluation still focuses on isolated prompts and answers. This Perspective has two objectives: to define a journey-level evaluation and governance framework for record-linked patien...
Large language model systems can retrieve and cite medical evidence, yet their outputs may remain difficult to review during care. We define reviewability through evidence adequacy for action-changing claims, exact source verification, and presentation matched to a specified reviewer and decision point. We propose a...
Next-encounter ICD forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal record available beforehand. The task is prospective and multi-label: the target note does not yet exist, and several codes may be correct. Structured EHR foundation models capture recur...
MultiViewDx, a partly physician-validated multimodal instruction dataset for evidence-linked multi-view medical imaging diagnosis, is introduced and Controlled ablations and clinician evaluation show that both case-level multi-view organization and evidence-linked reasoning targets contribute to the gain.
This work introduces MUSES, a million-instance benchmark for prospective intellectual-roots retrieval over a fixed 2.33M-paper corpus, and releases CiteRoots, both CiteRoots layers, and a distilled open companion judge for future work on prospective retrieval and intellectual roots.
This study evaluated both closed-source and open-source large language models for extracting and prioritizing medical jargon from EHR notes relevant to individual patients, leveraging prompting techniques, fine-tuning, and data augmentation and found that model performance could deviate largely based on prompting style...
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