AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much of this degradation stems from a failure to stop rather than to reason. As sparsity increases, pruned LRMs increasingly fail to terminate within the token budget, often...
This work formalizes the Safety Paradox, analytically showing that monotonic improvements in safety alignment naturally amplify posterior vulnerability, and establishes a causal link via reinforcement learning interventions, exemplifying that artificially degrading a model's safety judgment immunizes it against the att...
Bucket-Level MOO is introduced, a scalable distributed framework that applies gradient-based MOO algorithms locally, within the parameter buckets of distributed training, that enables conflict-aware updates without ever materializing a full gradient vector for any objective, and improves multilingual performance over s...
DT-Guard is presented, a content safety guardrail model based on a Reasoning-Active Training, Reasoning-Free Inference paradigm, which demonstrates that reasoning supervision can be effectively internalized into low-latency safety discrimination.
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