Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-13
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

A two-row hypercohomology spectral sequence

Example

Let K be bounded below with HqK=0 except for q=0,1, and supply the data of the second hypercohomology theorem for F. Put A=H0K, B=H1K. The only E2 rows are E2p,0=RpF(A) and E2p,1=RpF(B). With τp=d2p,1:RpF(B)Rp+2F(A), the target RnF(K) fits into 0cokerτn2RnF(K)kerτn10, where negative-index terms are zero. The values of τ and these extensions need additional input for a general K.

Facts & Assumptions

Given: The functor, complex and supplied replacements, with DC or supplied comparisons for naturality.

[F1]

The second hypercohomology theorem gives this E2, finite decreasing filtration and differential (r,1r) (Second hypercohomology spectral sequence).

[F2]

A computation record must retain unknown differentials and extensions explicitly (Spectral-sequence computation record).

Verification

1.1

For r=2 the only possible nonzero arrows are τp from row one to row zero. Hence E3p,0=cokerτp2 and E3p,1=kerτp. For r3, every outgoing arrow from either row has negative second coordinate and every incoming arrow starts above row one. These positions stay zero on successive pages, so E3=E.

F1
2.1

In total degree n, the only possible filtration quotients are at p=n and p=n1. F1's zero/full endpoints identify the first as a subobject of the target and the second as its quotient, giving the displayed exact sequence. At n=0 it reduces to R0F(K)=F(A); at n=1 its subobject is R1F(A) and its quotient is kerτ0. Below zero there are no surviving quotients, so the finite target filtration forces vanishing. This proves convergence and identifies the unresolved extension, rather than assuming a splitting.

F1F2step 1.1
3.1

A fully numerical specialization takes abelian groups, F the identity, and K0=Z/2, K1=Z/3 with zero differential and supplied replacements. Exactness of identity means its positive derived objects vanish, since applying it preserves the exact resolution. Thus E20,0=Z/2, E20,1=Z/3, all other entries are zero, and every τp is zero. Each total degree has one nonzero quotient: the target is Z/2 in degree zero, Z/3 in degree one and zero otherwise. The upper edges are the identity under the augmentation identification. This specialization has no extension ambiguity and uses no choice beyond the supplied-data convention.

F1F2step 2.1

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

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Sources