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Constrained Bayesian Experimental Design via Online Planning

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arXiv:2605. 26990v1 Announce Type: new Abstract: Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments.

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Authors: Yujia Guo, Daolang Huang, Xinyu Zhang, Sammie Katt, Samuel Kaski, Ayush Bharti
Released: 2026-05-27 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Constrained Bayesian Experimental Design via Online Planning overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Follow-up to #354: cascading chunk-binding — does A→B, B→C, C→D propagate the full chain on a recipient trained only to emit A?", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: line, rate, trained. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.26990

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