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Hypothesis-Disciplined Multi-Agent Automated Formalization of Asymptotic Statistical Theory

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arXiv:2606. 20642v1 Announce Type: new Abstract: Asymptotic statistical theory is a challenging domain for AI-assisted formalization: its central results mix convergence statements, asymptotic expansions, functional analysis, and regularity conditions that have a large gap from existing infrastructure in Lean 4 formalization.

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Authors: Tingzhou Wei, Zeyu Zheng, Ethan X. Fang, Junwei Lu
Released: 2026-06-23 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Hypothesis-Disciplined Multi-Agent Automated Formalization of Asymptotic Statistical Theory overlaps with clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", 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". Matching terms: source, line, rate, implement, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.20642

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