AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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JUMP is proposed, an efficient MIA that exploits the ability of dLLMs to predict masked tokens in parallel that improves mean ROC-AUC over a prior multi-mask attack and is extended to the target-only setting by replacing target-reference scoring with relative token preference.
This work identifies the first token of the response as a structurally distinct target for diversification, largely overlooked in prior work, and finds that the first-token distribution is sharply concentrated and only weakly related to downstream correctness, as lower-probability candidates can yield similarly accurat...
Masked diffusion language models support arbitrary-order generation, suggesting a natural way to produce diverse outputs. However, recent work argues that this flexibility reduces diversity by delaying high-uncertainty tokens that can lead to different generation paths. We trace this diversity loss not to arbitrary-ord...
This work shows that paraphrase robustness is a core requirement for reliable VLM-based reward modeling in robotics and introduces ROBORMBENCH, a benchmark with 2,390 real-robot trajectories, ground-truth progress labels, and 21,673 verified paraphrases spanning lexical, syntactic, and action-goal rewrites.
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