AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Experiments on vision-language benchmarks show that CoRAL significantly outperforms prior diffusion-based methods and achieves competitive performance against strong ARMs, achieving both superior efficiency and quality.
Multimodal large language models (MLLMs) can act unsafely when individually benign images and texts are interpreted jointly. Beyond implicit reasoning risk, where reasoning drifts toward harm, we identify intent-level risk, where cross-modal ambiguity silently commits the model to a hazardous premise that path-level op...
AeroCopilotBench and its executable cockpit environment, ACOE, are introduced, which define state-transition rules, task goals, and trajectory-level safety constraints based on aircraft-specific Pilot's Operating Handbooks (POHs).
Causal Latent Revision (CaLR), a framework that reformulates reasoning as constrained latent optimization, performs gradient-guided ``thought revision" to enforce logical consistency, enabling dynamic self-correction of intermediate steps during parallel generation.
QEncodeBench tasks large language models with encoding classical constraint problems as phase oracles and scores the generated circuits with an adversarially self-validated verifier that decides full solution-set equivalence up to a global phase, with ancillas restored and resource budgets enforced.
A safety-gated evaluation framework in which a trajectory succeeds only when all task goals are achieved without violating any hard safety constraint, while safe goal progress and trajectory safety are measured separately is established.
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