AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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TimeNet provides the data and systems foundation needed to move beyond task- and dataset-specific TFMs toward models that can learn jointly across heterogeneous domains, modalities, temporal scales, and forms of supervision from a common data model.
This work evaluates 25 model and agent systems under a controlled tool-using protocol, then uses controlled studies to locate failures in evidence access, tool selection, memory, reasoning, interaction, and scientific execution.
TimeSage-EV is introduced, a live benchmark for agentic time series analysis in evolving environments that evaluates state identification, data summarization, and outlook reasoning, and is released as a research resource with monthly updates, code, a leaderboard, and failure-mode analyses.
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