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A General Framework for Dynamic Consistent Submodular Maximization

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arXiv:2606. 04946v1 Announce Type: cross Abstract: Consistency is an important property in dynamic submodular maximization and entails maintaining a near-optimal solution at all times, making only a small number of adjustments to the solution in each step.

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Authors: Paul D\"utting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Ola Svensson, Morteza Zadimoghaddam
Released: 2026-06-04 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because A General Framework for Dynamic Consistent Submodular Maximization 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 "Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format length, persona-presence, on-policy, marker-only-loss)". Matching terms: line, full, trained, factor. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.04946

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