AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 6 of 51 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
CreditTrace-LLM evaluates target responsiveness, credit locality, paraphrase stability, and traceable Gold-point-level outputs as complementary diagnostic dimensions rather than as predefined criteria for acceptable grading performance.
Background: Large language models are increasingly used for rubric-based grading, but it remains unclear whether they adapt selectively when criterion weights change while the response, criterion definitions, and total score remain fixed. This study examined whether alternative point allocations produce targeted criter...
A physician-facing Clinical AI-Readiness Guide for preparing medical datasets before AI-based analysis to improve collaboration between clinical and technical teams and reduce preventable dataset-related failures in medical AI research is proposed.
ConsensusGrade provides a diagnostic framework for interpreting automated scores relative to observed human variability; inside-envelope rates should not be interpreted as stand-alone measures of grading accuracy.
Student-history metadata can influence LLM-generated grading scores despite explicit instructions to ignore it, and future LLM-based grading systems should separate answer-based scoring from learner-context-based personalization and validate score invariance under controlled learner-context variations.
SafetyJudge-LLM shows that local open-weight LLMs can support semantic safety judging, but their reliability must be evaluated across multiple dimensions.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.