May 2026

AI-enabled infrastructure systems increasingly govern access to emergency services, dis-aster relief, and utility restoration, yet they routinely produce inequitable outcomes even when allocation algorithms apply procedurally neutral rules. The standard explanation locates the cause inside the algorithm. This paper argues instead that inequity arises from the interaction between the algorithm and the physical environment in which it operates: network topology, resource locations, and demand distribution jointly constrain what any policy can achieve, and when those constraints are sufficiently […]

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You shipped your first AI agent to production last quarter. It works. Customers love it. Then your finance lead pings you on Slack: “Our Bedrock bill went up 4x last week. Which feature is burning the tokens?” You stare at CloudWatch. You stare at your code. You realize you have no idea, because you never instrumented per-request token usage. A week later, a customer complaint lands on your desk: “the assistant gave me wrong information at 2:47 PM […]

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Autoregressive LLM world models factorize next-state generation left-to-right, preventing them from conditioning on globally interdependent anchors (tool schemas, trailing status fields, expected outcomes) and yielding prefix-consistent but globally incoherent rollouts. MDLMs’ any-order denoising objective sidesteps this by learning every conditional direction from the same training signal. Empirically, fine-tuned MDLMs (SDAR-8B, WeDLM-8B) surpass AR baselines up to 4x their total parameter count on BLEU-1, ROUGE-L, and MAUVE across in- and out-of-domain splits, with lower Self-BLEU and higher Distinct-N confirming […]

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arXiv:2605.20552v1 Announce Type: new Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each recommended item is a node and its expected rating is similar to its neighbors. The goal is to recommend items that have high […]

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arXiv:2605.20545v1 Announce Type: new Abstract: Transfer learning is an essential technique for many machine learning/AI models of complex structures such as large language models and generative AI. The essence of transfer learning is to leverage knowledge from resolved source tasks for a new target task, especially when the sample size $m$ of the training data for the latter is low. In this work, we rigorously analyze the potential benefit of transfer learning in terms of sample efficiency. Specifically, […]

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arXiv:2605.20434v1 Announce Type: new Abstract: We study the contradiction graphs associated with binary concept classes. For a class $H subseteq {0,1}^X$, the order-$m$ contradiction graph $G_m(H)$ has as vertices the $H$-realizable labeled sequences of length $m$, with two vertices adjacent when the two sequences assign opposite labels to some common domain point. Our main result is that the single graph $G_m(H)$ determines the threshold predicate $mathrm{VCdim}(H)ge m$. Consequently, the full sequence $(G_m(H))_{m ge 1}$ determines the exact VC […]

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