The paper investigates how to combine multiple calibrated predictors into a single refined predictor that preserves all available information and cannot be further refined. It models calibrated predictors as signaling schemes and defines refinement via feature‑independent garblings, showing that a new predictor is constructible exactly when its signal vector lies in the linear span of the input signal vectors. The authors provide algorithmic results: deterministic refinement is polynomial‑time solvable for two predictors but NP‑hard for many, while randomized refinement admits a polynomial‑time algorithm for any number of inputs by decomposing signal vectors into extreme rays of a polyhedral cone.
By Wei Tang, Hanrui Zhang
arXiv:2607. 18910v1 Announce Type: new Abstract: Sequential decision making in non-stationary and partially observable environments requires rapid adaptation to latent regime changes.
By Yuyang Shen, Shan Dai, Daimin Chen
arXiv:2606. 30068v1 Announce Type: new Abstract: Joint-embedding predictive (JEPA-style) objectives learn representations by predicting future latents.
By Ayan Pendharkar
arXiv:2610.27364v1 Announce Type: cross
Abstract: In joint-embedding predictive architectures (JEPA), an encoder and a predictor are trained together to produce a representation that minimizes the er...
By Mosh Levy, Aly Lidayan, Yoav Goldberg
arXiv:2606. 29878v1 Announce Type: new Abstract: Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce.
By Konstantinos Ziliaskopoulos, Alexander Vinel, Alice E. Smith
arXiv:2507. 01414v2 Announce Type: replace Abstract: We introduce a new family of toy problems that combine features of linear-regression-style continuous in-context learning (ICL) with discrete associative recall.
By Sultan Daniels, Dylan Davis, Dhruv Gautam, Wentinn Liao, Gireeja Ranade, Anant Sahai
arXiv:2606. 01092v1 Announce Type: cross Abstract: Supervised learning evaluates predictors through their input-output behavior.
By Vasileios Sevetlidis
The paper identifies an informational curse of horizon in goal-conditioned policy learning, showing that longer goal relabeling horizons during training can hurt policy generalization and performance. Controlled experiments with oracle planners separate training horizons from test horizons, revealing that behavioral cloning policies degrade with longer horizons while reinforcement learning mitigates this effect. The authors attribute this to reduced conditional mutual information between actions and hindsight-relabeled goals, and demonstrate that distilling short-horizon policy Jacobians into long-horizon policies improves performance, especially in combinatorial manipulation tasks.
By John L. Zhou, Yuxuan Dong, Jonathan C. Kao
arXiv:2507. 00260v3 Announce Type: replace-cross Abstract: When predictors are statistically dependent, the appropriate definition of feature importance depends on the operational goal.
By Jin-Hong Du, Kathryn Roeder, Larry Wasserman
arXiv:2608. 11658v1 Announce Type: cross Abstract: Many reinforcement learning systems, from fleet management to traffic signal control, must serve an objective that changes dynamically after deployment, and retraining a policy for each new objective is prohibitively expensive.
By Zijian Zhao, Sen Li
arXiv:2607. 11958v1 Announce Type: new Abstract: Under the free energy principle, a predictive system does not observe reality directly; it maintains a generative model of the world and experiences that model's best current hypothesis.
By MD Ibrahim Hossain Ridoy
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.