Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce. This distinction is important in predict-then-optimize systems: large forecast changes may leave the optimizer's action unchanged, while small changes can alter the selected decision and its realized value.
arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
By Yurui Zheng, Ying Jin
Revelation Control studies how to price interventions that reveal hidden state only when the revealed distinctions can alter a consequential decision, while separately accounting for any useful progress the intervention itself creates. The authors develop a framework for learning systems that defines decision‑sufficient revelation, revelation depth, and a cost‑adjusted factorization criterion, and they provide a target‑independent protocol for model‑specific instantiation. Experiments on Qwen2.5‑7B and Mistral‑7B‑v0.3 show that deeper future‑learning probes have positive decision value and that productive reuse yields strict equal‑compute utility advantages, supporting a structural transfer of the decision theory and evaluation protocol across architectures.
By Qinyou Wang
The paper introduces a score‑calibrated robustness framework that transforms any fixed point predictor into a decision‑relevant uncertainty representation using distribution‑free conformal calibration. By employing the conformal score as the core unit of robustness, the authors derive both reliability‑based robust optimization and target‑oriented Conformal Robust Satisficing formulations, linking them through a shared robust decision frontier and a fragility measure. Experiments on synthetic data and a real online‑grocery inventory case study demonstrate the framework’s ability to improve reliability, reduce costs, and provide interpretable uncertainty scales for black‑box predictors.
By Lingjie Zhao, Hansheng Jiang, Wei Qi
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.
By Jintao Xu, Zhengyu Chen, Ben Zhang, Yongzhi Qi, Jianshen Zhang
arXiv:2609.34284v2 Announce Type: replace
Abstract: Personalized LLMs must decide, for each stored preference, whether the current context calls for applying or suppressing it, which we call its appl...
By Haeun Jang, Yonghyun Jun, Hwanhee Lee