AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The concept of Data Science World Model is introduced, which model the data science execution environment by predicting environment state transitions conditioned on current workflow states and candidate operations and proposes DSWorld, a practical framework that combines structured state construction, cost-aware routin...
The key idea is to induce the adversary to solve a misspecified inverse problem, in which no plausible label sequence in the sequence space can explain the observed gradients, by inducing inconsistency across three dimensions: objective, direction, and scale.
DS-Lighting is introduced, a unified harness toolkit that makes harness design explicit for data-science automation and integrates multiple open-source data-science benchmarks into an MLE-Bench-style task format, enabling controlled comparison under a shared task interface, sandboxed runtime, and metric protocol.
SelfWAM is introduced, a unified self-grounded WAM built on a modality-specialized Mixture-of-Transformers (MoT) architecture that jointly predicts actions, action-conditioned future RGB frames, and robot self-masks, thereby grounding future prediction in the robot's visible body and its action-induced motion.
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