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Implement fast primitives in the selected BIT caller - #2915
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Mister-Meeseeks merged 3 commits intoOct 16, 2026
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Bind completed B1 to leaf and owner-route calls, compile encoded high-field factors, and retain the preceding C0 bindings during nested execution. Include the source-bound cost argument and complete reduced caller checks. Author: Jose Manuel (jmbarrancoml). Substantial OpenAI Codex assistance, including analysis subagents; predecessor attribution remains in the package.
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#2915's ordered high-field ADD Answers the ordered-ADD component of PR #2982's single-cube handoff for the unchanged PR #2915 kernels.added and its pooled stream.add_pass. For odd Q>=3, canonical positive B,M,S and c in {-4,-2,-1,1,2,4}, with V=BQMQS: exactly 10+[S>1] additional scratch roles suffice and are attained in every call, sharpening PR #2809's conservative 32-role lexical allowance; six additional numerical words suffice and one fixture attains six (PR #2810's add_pass bound composed with the actual coefficient callback); and the complete local physical read/write/ unit-move bill, including encoded arithmetic and pool acquisitions, is at most 4096V, or 4102V with returned-output disposal. The exact non-arithmetic invoice extends PR #2877's and PR #2473's countdown and payload bills, zero case included, with pool acquisitions, the one-bit-suffix path and the marginal lifecycles. It matches the unchanged source on 109 complete packets (37,241 payload bits), and one fewer scratch role or numerical slot rejects at the real allocators. Route setup, B1 swaps, machine steps, physical backing and whole-stage stock remain open; no supplier, selected-frontier or kappa change is claimed. Adds research/high-add-scratch-invoice/ only. Submitted by @commdata2338 with substantial AI assistance (OpenAI Codex research and implementation; packaged with Claude Code). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01X86sCSXdoDd61SDFfC1PKG
This was referenced Oct 19, 2026
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I replace the selected physical BIT caller's leaf/remainder swaps and decorated-key owner routes with the completed native ordinary backend from PR2899. I compile PR2157's finite factor words into encoded high-field passes and preserve C1 → B1 → C0 → A0 by restoring both preceding primitive bindings during each nested call.
Under the pinned C0 source arguments, I obtain the complete local toll
O_q(V(n+w^(1-a1))), witha1=169711/10^8, and the corresponding stopped bound. I also compose the existing outer wrapper. I retain PR2399's bootstrap formula, the coarse children, stock contract and selected frontier. The preceding B1 already supplies enough ordinary speed for the current complex consumer, so I stop ordinary-stage expansion here.Evidence: 28 complete high-field cases; five complete routes with independent column maps, signed carries and spectators; all 1,123 selected owner matrices; a fitted preceding-stage handoff; complete q2 and q3 callers; and ordinary/binary wrapper endpoints with output reuse and owned-work erasure. I register eight cold replay commands with exact source pins and typed receipts.
I passed all eight registered commands on Python 3.12.15/macOS arm64. I then replayed the strengthened catalogue invoice check, source/ownership rejection controls and metadata checks after incorporating canonical main
475fec190d5ad746bd4690574dca30d6901acdaf. I verified all 37 package/input files against publication commitfecc5a87574df441ddc3dad44f99fca9bd7fdd92. Hosted CI remains unverified.The small C1 fixtures use B1's elementary branch. I force a declared reduced fitted body in the separate handoff test. I retain the written universal source arguments as dependencies; I have not executed a selected GL64 payload or completed a formal refinement. I make no new selected-exponent or full-multiplication-theorem claim.
I coordinate in Discussion2319. I retain David Arias's PR2753 caller, Nick Petalas's carry and outer-wrapper work, commdata2338's substitution algebra and the nested source attribution. Jose Manuel (
jmbarrancoml), with substantial OpenAI Codex assistance, including analysis subagents.