Paper

A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation

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arXiv:2606. 20955v1 Announce Type: new Abstract: Dynamic average estimation is a critical problem in multi-agent systems, enabling agents to collaboratively estimate time-varying signals using only local information exchange.

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Authors: Antonio Marino, Claudio Pacchierotti, Paolo Robuffo Giordano
Released: 2026-06-23 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation 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 "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe", experiment "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone". Matching terms: rate, compare, without, full, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.20955

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