The study reproduces the BG-CFQS risk‑controlled throughput forecasting method on three Starlink traces and an additional dataset, finding that its risk guarantees hold only under random data splits and fail under contiguous temporal splits. It also shows the method cannot support risk budgets below 0.15, that static conformal calibration does not improve ranking, and that group‑conditional online recalibration often performs worse than no conditioning. These empirical results question the practical reliability of the technique for real‑world satellite link admission control.
Risk-budgeted throughput forecasting converts a point forecast of LEO satellite capacity into a conservative bound whose overestimation rate is held at a user-specified budget, so that admission contr...
The paper introduces loss‑conditioned state execution, a model‑agnostic technique that decides whether to apply a world model’s proposed state change or keep the current state based on whether the change reduces downstream loss. It formalizes state movability as the existence of a loss‑reducing feasible correction and constructs loss‑specific proposals from predictive distributions, executing them only when a groupwise lower confidence bound on loss improvement is positive. Experiments on forecasting and dynamics benchmarks show that the method accepts updates for a subset of cases, achieving lower bounded loss than persistence or always executing the proposal, and highlights that event predictability and loss‑based decisions must be evaluated separately.
arXiv:2606. 10187v1 Announce Type: cross Abstract: We develop a decision-calibrated conformal framework for pacing decisions in streaming advertising.
The paper introduces a method to certify selective prediction in machine learning systems by computing the availability of safety gates through exact-binomial inversion and dynamic programming. It demonstrates that a truth-informed planner can significantly improve mean coverage over naive approaches, and that reallocating error budgets further enhances coverage across diverse applications such as LLM tool‑calling, content moderation, lesion classification, and recommendation. The study highlights the importance of planning and finite‑sample estimation in ensuring reliable, granular deployment of selective predictors.
arXiv:2608. 14903v1 Announce Type: new Abstract: Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or expert belief.
arXiv:2609.38917v1 Announce Type: new Abstract: A classifier's conditional accuracy can change while its confidence distribution stays exactly the same. We study the worst-case movement of the reliab...
arXiv:2609.24559v1 Announce Type: new Abstract: We present $t_0$, a family of open-weights foundation models for forecasting with multivariate context. We release its first two members: $\texttt{t0-a...
arXiv:2608.21262v1 Announce Type: cross Abstract: Many machine-learning systems set a threshold at a quantile of a calibration set: conformal predictors that promise 90% coverage by drawing their cut...
arXiv:2610.09208v1 Announce Type: cross Abstract: Weighted conformal prediction corrects for covariate shift by reweighting calibration scores with the likelihood ratio between target and source cova...
The paper introduces a method for deciding whether to adapt a frozen segmentation model at test time, arguing that a fixed adaptation horizon conflates two distinct decisions: how far to adapt and whether to adapt at all. By measuring disagreement geometry—called prediction fragmentation—between the source model and the adapted mask, the authors predict harmful accepted area (HA) without extra labels or backward passes, achieving strong correlation across three medical benchmarks. A case‑level router built on this metric reduces HA significantly while maintaining or improving Dice scores, and the approach generalizes across architectures and domains.
arXiv:2610. 12232v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods for time-series forecasting update a deployed model, or a small adapter around it, from incoming ground truth.
arXiv:2606. 29654v1 Announce Type: new Abstract: Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and when it should be escalated to human review.