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Audit concrete rounding in the QQ16 F0 scalar projection - #3036
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This was referenced Oct 17, 2026
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The literal QQ16 F0 common-chart scalar projection loses a unit data input under actual binary64 round-to-nearest arithmetic, while every dirty helper restores exactly. With Y0=1, L224=2^65 and all other inputs zero, exactly two ADDs round; Y0 becomes zero and four other Y outputs become -1/2. Exact arithmetic and wider-precision controls recover the original state.
This closes the concrete scalar-rounding gap explicitly left outside #2995 and #3017. It does not establish that component rounding in the actual native producer realizes these scalar faults. The native maps, B1, complete producer grid/range/cost contract, F17 consumer and supplier admission remain open. No new kappa or selected-frontier change is claimed.
Adds only
research/qq16-f0-rounded-scalar/: a written finite witness, pinned certificate and task provenance, complete numerical receipts, an exact nearest-even interpreter, and behavioral tests. Four finite precisions (8/24/53/113) reproduce the witness. Actual separate binary64 multiplication/ADD agrees with the favorable completed-ADD rounding model at all 21,025 scalar instructions. All 480 borrowed helpers restore and eight private outputs are zero. The omitted native events are explicitly inventoried.Validation on native macOS arm64:
Reproduce:
Scientific/submission base: canonical main
d8e0f83f00191df62174b641e9fff055c357f641, priority revision 6. Final completely paginated overlap refresh covers 277 open PRs, 2,440 closed PR bodies and 1,766 discussion records; 13 textual leads were inspected. No competing F0 concrete-rounding result was identified; arbitrary diffs and unpublished work are outside coverage. Canonical investigation. Family decision: narrow and stop at this witness.Credit Nick Petalas/#2965 for the producer-accuracy handoff, Arham Tahir/#2995 for the earlier scalar relative-fault audit, Tekalign/#3017 for its native binding, Ali Romman/QQ16 and the retained native/scalar lineage. New work by Arham Tahir with substantial OpenAI Codex assistance, Apache-2.0; no independent human/formal-kernel review claimed.