arXiv:2608. 07913v1 Announce Type: cross Abstract: Selective-risk certificates promise that accepted outputs meet a declared error target.
By Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma
arXiv:2609.39123v1 Announce Type: new
Abstract: When optimizing an expensive black-box function sequentially, as in hyperparameter optimization, we may want to stop once the best evaluated value is c...
By Ami Tavory, Noa Cohen
The paper investigates the reliability of machine‑parsed statutes by developing a passive survival certificate for the Duquenne‑Guigues implication basis of extracted legal contexts. It measures inter‑extractor disagreement, runs 1,000 Monte‑Carlo trials, and certifies an implication only when a one‑sided Wilson 95% lower bound on survival reaches 0.95, providing premise spans and minimal counterexamples. Applied to 29,365 Missouri sections and 502 Indian central‑Act sections, the method passes a held‑out gate for many statute families, yet a global error model shows that 93.2% of held‑out chapters fall below the informativeness floor, attributing this to calibration‑rate transfer rather than selection bias.
By Surya Saka
arXiv:2608. 15520v1 Announce Type: new Abstract: A multimodal system may begin inference holding only some of its inputs and may acquire the rest at a cost.
By Melika Baghi
arXiv:2606. 31023v1 Announce Type: cross Abstract: Hard-constrained sequential decision systems have no certified way to spend the test-time compute of modern AI: executing the multi-step drafts of a learned policy or a frozen LLM forfeits the feasibility guarantee a trusted solver provides, while invoking the solver at every step forfeits the speed the AI offers.
By Chenyu Zhou, Qiliang Jiang, Shuning Wu, Xu Zhou
arXiv:2608. 14639v1 Announce Type: cross Abstract: Per-field accept/review with selective risk at most alpha -- accept a field only if the error rate among accepted fields is controlled -- is the trust contract document-extraction systems need, and the natural procedure silently violates it on real documents.
By Bhaskar Gurram