Alphabeta Math
LemmaStatement: AI-adaptedProof: 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.

The two filtrations identify E2 and the composite edge

Statement

For the supplied composite data AI, F(I)J under the acyclicity and comparison hypotheses of the total-composite lemma, the vertical-first filtration of Tot(GJ) collapses to Rn(GF)(A). The other filtration has E2p,q=RpG(RqF(A)). Its two edges are the canonical maps RnG(F(A))Rn(GF)(A) and Rn(GF)(A)G(RnF(A)).

Facts & Assumptions

Given: The supplied data and exact hypotheses in the statement.

[F1]

The first filtration identifies the target through the augmentation GF(I)Tot(GJ) (The total Cartan-Eilenberg complex computes the derived composite).

[F2]

The second hypercohomology sequence has E2p,q=RpG(Hq(F(I))) and finite resolution-degree filtration (Second hypercohomology spectral sequence).

[F3]

Its edges are inclusion of the bottom horizontal cycles and projection onto resolution degree zero (Hypercohomology edge maps are canonical).

Proof

1.1

The cohomology of F(I) is RqF(A), with H0(F(I))=F(A). Substitute this into F2 to obtain the displayed E2. F1 identifies the total target with Rp+q(GF)(A) through the actual augmentation quasi-isomorphism. Its proof computes the other filtration as one row, so these are two filtrations of the same total complex, not a claimed equality of their second pages.

F1F2
2.1

Set the lower bound to zero in F3. The bottom horizontal-cycle inclusion gives RnG(H0(F(I)))=RnG(F(A))Hn(Tot(GJ)); projection gives Hn(Tot(GJ))G(Hn(F(I)))=G(RnF(A)). Transport both along the augmentation isomorphism of step 1.1. This defines the canonical derived-composite edges and agrees with the finite filtration definition. In degree zero both reduce to GF(A); vanishing edge terms are permitted. The naturality/choice qualifications are exactly those of F1–F3.

F1F3step 1.1

Depends on

Used by

Dependency tree · two levels

11 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources