arXiv:2607. 06760v1 Announce Type: new Abstract: Autonomous systems under partial observability act on beliefs, not raw sensor events.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2507. 18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.
arXiv:2606. 12808v1 Announce Type: cross Abstract: Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices.
arXiv:2608. 07743v1 Announce Type: new Abstract: Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope.
arXiv:2607. 11317v1 Announce Type: new Abstract: Low-bit quantization makes small reasoning models inexpensive to deploy but can degrade their chains of thought.
arXiv:2607. 03193v1 Announce Type: cross Abstract: Calibrating a superconducting transmon chip is a sequential decision problem under noise, drift, and a finite budget: an expert must choose experiments, read ambiguous plots, judge fit quality, and revise stale beliefs as the chip drifts.
Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation.