AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large language model (LLM) agents offer new opportunities for automated analysis in industry. However, rigorous evaluation of such agents-for example, within power system scenarios-remains hindered: real operational data are confidential, and existing public resources fail to fully capture the chained dependencies and...
Recent advancements in large language models have revolutionized the field of psychological counseling, especially in the context of Cognitive Behavioral Therapy (CBT). While the success of CBT relies heavily on dynamic decision-making informed by the client's real-time mental state, this aspect has often been overlook...
An overview of the role of machine learning in epilepsy management is provided and the high heterogeneity and bias-particularly in small sample sizes, handling of missing data, and scarcity of studies with external validation-limit its clinical applicability.
SDARE-Bench is introduced, the first scenario-based benchmark evaluating both stigma detection and open-ended response generation in LLMs, comprising 1,138 dyadic queries and 1,388 group dialogue and identifies stigma response as a recurring LLM safety vulnerability, especially in socially complex conversational contex...
VIBE-Bench is introduced, a benchmark with two psychology-grounded tasks, 3,504 personas and 12,239 dialogues, including a manually verified gold test set, and requires cross-concept preference reasoning beyond surface semantic overlap, establishing PRCM as a distinct failure regime in PLLMs and position VIBE-Bench as...
This work proposes CausalGCD, a causality-inspired framework designed to mitigate domain-shift bias in category discovery and proposes a Causal Geometric Manifold Constraint that enforces invariant manifold-level associations between known and unknown categories across domains, thereby facilitating robust discovery of...
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