This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model, which formalizes the value of trying a candidate design action as its expected reduction of epistemic uncertainty about action--outcome relations. The model addresses one part of the Uncertainty Driven Action (UDA) model's open question concerning how changes in uncertainty perception determine action selection.
arXiv:2608. 05642v1 Announce Type: new Abstract: This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model, which formalizes the value of trying a candidate design action as its expected reduction of epistemic uncertainty about action--outcome relations.
The paper introduces ACTION-BED, a Bayesian experimental design framework that replaces traditional uncertainty‑reduction objectives with expected future loss (EFL) on downstream actions. By reformulating EFLs into singly intractable objectives, ACTION-BED enables joint optimisation of design and action policies using stochastic gradients without explicit posterior or marginal likelihood estimation. This approach simplifies learning of design policies and allows easy customization to various downstream tasks and loss functions.
arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.
arXiv:2606. 19328v1 Announce Type: cross Abstract: Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward design.
arXiv:2607. 14817v1 Announce Type: cross Abstract: Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning.
arXiv:2608. 16428v1 Announce Type: cross Abstract: Artificial intelligence systems often disclose uncertainty, yet they rarely make clear what response that uncertainty should trigger.
The paper introduces Generalised Bayesian Optimal Experimental Design (GBOED), extending generalised Bayesian inference to experimental design. It replaces the likelihood in Bayesian updates with a loss function, deriving a new acquisition function called Gibbs expected information gain. Empirical results show that GBOED improves robustness against outliers and mis-specified outcome noise distributions.
arXiv:2608.16466v2 Announce Type: replace-cross Abstract: Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an exp...
arXiv:2610.32514v1 Announce Type: new Abstract: Thompson sampling is a widely used strategy for sequential decision-making under uncertainty, offering a principled way to balance exploration and expl...
The paper introduces ProSE, a framework for AI assistants that generate proposals while considering users’ bounded rationality and evaluability constraints. It proposes a KL‑regularised bounded‑rational binary response model and a depth‑2 Bayes‑adaptive planner, “ProSE‑Plan,” which scores proposals by expected responses and resulting belief updates. Experiments on graph simulations show that “ProSE‑Plan” outperforms evaluability‑unaware and myopic baselines, especially when evaluation cost is high, and that informative probes are crucial for effective assistance.
arXiv:2607. 28894v1 Announce Type: new Abstract: Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior.
The paper introduces OHCAM, an online method for learning action models that include conditional and quantified effects from limited interactions. It maintains a belief over possible models and actively chooses actions that maximize disagreement among hypotheses to reduce uncertainty, while handling noisy observations. Starting with simple hypotheses, OHCAM expands complexity only when necessary, achieving sample‑efficient learning that outperforms baselines on benchmark domains and is validated on a Kinova Gen3 robot.