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Bounded recovery for modular spectral averages

OpenAI · September 23, 2026

Lean

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Abstract

We prove a bounded spectral recovery theorem for a von Neumann algebra with a faithful normal state whose centralizer consists only of scalars. A positive averaged squared norm for an arbitrary bounded Hilbert-space operator on shrinking modular spectral bands can be recovered on uniformly bounded algebra elements with shrinking spectral support. We apply this theorem to spectral intertwining rigidity and obtain an alternative proof of bicentralizer triviality for type III1 factors with separable predual.

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Collected automatically by AI tools (Claude Opus 5.5 and GPT-6 Astra, 2026-10-21) and not checked by a person. May be wrong or incomplete. Report an error.

Comments and discussion

Formalization

  • OpenAI math release, Lean scope note for family 290 (lean/docs/290.md) (release of 6 Oct 2026). Formalizes this paper's bounded recovery theorem for a faithful normal state with scalar centralizer, recovering uniformly bounded algebra elements from positive modular spectral averages in spectral bands up to four times wider; the note states that the absolute bicentralizer conjecture is not included.

Possibly relevant literature (found by the agent, not checked)

Digests and expositions

  • Claude Opus 5.5, Modular averaging and bounded recovery (Open Mathematics Courses, October 2026). This lesson follows the paper's bounded recovery theorem through modular averaging, fourth-moment estimates, polar truncation and Fourier filtering, and identifies itself as self-checked by its writing AI. [AI-generated per source]
  • Claude Opus 5.5, Spectral intertwining rigidity (Open Mathematics Courses, October 2026). The companion lesson develops the paper's passage from bounded recovery to interval estimates and joint spectral rigidity, including its application to the bicentralizer flow. [AI-generated per source]

Elsewhere

Independent sites; their content is not reviewed here.

Cite

@misc{OAI:Bounded-recovery-for-modular-spectral-averages-September-23-2026,
  author = {{OpenAI}},
  title = {{Bounded recovery for modular spectral averages}},
  howpublished = {OpenAI Math Release preprint
                  \href{https://github.com/openai/math/blob/main/preprints/Bounded-recovery-for-modular-spectral-averages-September-23-2026/Bounded-recovery-for-modular-spectral-averages-September-23-2026.pdf}{OAI:Bounded-recovery-for-modular-spectral-averages-September-23-2026}},
  year = {2026}
}

Lean formalization

OpenAI provides Lean files for this family: lean/docs/290.md. The Lean statements have not been checked by people against the paper's claims.

Family 290: Relative bicentralizers and modular spectral recovery

Proves Connes' bicentralizer conjecture for every type III1 factor with separable predual and every faithful normal state. More generally, for every inclusion $N\subset M$ of von Neumann algebras with separable preduals admitting a faithful normal conditional expectation, constructs an amenable expected subalgebra $P\subset N$ with $P'\cap c(M)=N'\cap c(M)$, resolving the relative bicentralizer conjecture.

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