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R12 Post-Commit Fingerprint Transport Theory

Shohin's missing behavior is not merely a decodable answer. It is a packet that can be written before a late interface exists, updated after the source is gone, and reused by consumers that were not available to the writer.

R12_POST_COMMIT_FINGERPRINT_TRANSPORT_THEORY.mdOpen original Markdown ↗

R12 Post-Commit Fingerprint Transport Theory

Status: THEORY CANDIDATE, NEURAL GATE BLOCKED. The v1 exact scorer passed, but independent audit found that it never invokes a packet updater, does not enforce process-separated source deletion, and lets the scorer perform output recoding. R12_PCFT_ADVERSARIAL_AUDIT.md is therefore NO-GO for a neural fit. Only a process-separated exact v2 transport falsifier may reopen a separately committed CPU neural preregistration. No Shohin adapter, SFT, or H100 job is authorized.

1. Target

Shohin's missing behavior is not merely a decodable answer. It is a packet that can be written before a late interface exists, updated after the source is gone, and reused by consumers that were not available to the writer.

Let certified task state be x_t in F_p^m and verified event update be x_(t+1)=delta_e(x_t). The model writes a quantized packet

z_t = Q(W_theta(history_t)) in {0,...,q-1}^k.

Only after z_t is committed, sample a challenge r in F_p^m. The reader must return the random linear fingerprint

phi_r(x_t) = r^T x_t mod p.

After source deletion, a shared learned updater receives only (z_t,event) and must produce a packet that passes fresh independently sampled fingerprints of delta_e(x_t) through long update chains.

2. Anti-motor theorem

For distinct states x != x' and uniform r in F_p^m,

Pr_r[r^T x = r^T x'] = 1/p.

Therefore any packet collision is exposed by a random post-commit challenge with probability 1-1/p. On a balanced colliding pair, one deterministic reader has maximum expected exact accuracy

(1 + 1/p) / 2.

This does not eliminate unlimited finite tables. It makes the claim resource-bounded: with a quantized packet near the information content of x, a fixed bundle of public answers cannot cover a challenge family of size p^m without recovering an equivalent sufficient state.

3. Proposed training objective

The treatment has four reasoning-only components around a frozen language backbone:

  1. source/history writer W;
  2. finite-precision packet quantizer Q;
  3. one shared event-conditioned updater U;
  4. challenge-conditioned reader R.

For independently sampled post-commit challenges r,r', train

L_direct = CE(R(z_t,r), phi_r(x_t))
L_update = CE(R(U(z_t,e),r'), phi_r'(delta_e(x_t)))
L_close  = distance(Q(U(z_t,e)), stopgrad(Q(W(history_t followed by e))))
L = max_or_group_robust(L_direct, L_update, lambda*L_close).

The frozen verifier computes scalar fingerprint labels. The model never receives canonical state coordinates, a rationale, an opcode sequence, a transition matrix, or the full recursive solution. Packet erasure/noise and an explicit k log2(q) ledger prevent unbounded analog precision.

At evaluation, writer and source process exit before events, challenges, and a fresh residue-to-output-symbol codebook are generated. A fresh reader process has no source, source KV, verifier, retrieval path, or scoring feedback.

4. Why it differs from failed Shohin paths

  • Unlike ordinary answer SFT or additive forks, the writer cannot know which functional will be scored.
  • Unlike J-lens/MCBS, the method installs and trains a packet rather than decoding a presumed raw-model subspace.
  • Unlike R11's fixed consumers, the consumer family is generated only after packet commitment and is exponentially larger than a fitted answer bundle.
  • Unlike a visible scratchpad, no intermediate text is emitted or reread.
  • Unlike an external scheduler, event selection and packet update must occur in the learned source-free updater.

5. Exact collapse and claim boundary

If enough fingerprint labels reconstruct every certified x_t, the method is state distillation under random projections. That is an allowed description, not a failure to be renamed. It would establish a learned compact update path, not autonomous discovery of the task ontology.

Reject the candidate if any condition holds:

  • packet capacity materially exceeds the certified state information;
  • the reader or updater can access source/KV/verifier state;
  • direct writer fingerprints pass but source-deleted updates fail;
  • unseen history/event compositions or longer depths collapse;
  • random/output-token controls match treatment;
  • a favorable GRU, MatrixNet/operator, or fixed-consumer control ties under the same resource vector;
  • normal language bypass is not bitwise identical to the frozen backbone.

6. Required process gate before any tiny neural gate

The v1 scorer pass is insufficient. A v2 runner must first execute writer, stateless one-event updater, oracle, and fresh reader as separate processes; commit serialized packets before challenge generation; withhold source and event history from updater/reader roles; and require the reader itself to emit the late-recoded symbol. Only after that exact process gate passes may a new preregistration specify a CPU neural experiment. Its minimum design is:

  • certified affine register programs over a finite field;
  • train depths 1--6; confirmation depths 12, 24, and 48;
  • unseen event-parameter compositions and source renderings;
  • at least 64 post-commit fingerprints per final packet;
  • fresh output recoding after final packet commitment;
  • exact-state positive, same-information direct-state and fixed-full-rank controls, equal-width rank-two motor bundle, favorable GRU, random packet, source-visible, and horizon-triggered controls;
  • identical packet bits, parameters, examples, optimizer updates, and compute wherever mathematically possible;
  • donor packet swaps and direct-versus-incremental packet agreement;
  • three frozen seeds and score-blind confirmation.

No Shohin/H100 experiment is authorized by a CPU scorer pass. A learned CPU candidate must show a preregistered accuracy/resource advantage over same-information controls at unseen source states, renderings, event parameters, dimensions, and depths. Tying the exact affine solver is an oracle gap, not a rejection; beating only the rank-two motor is insufficient.

7. Prior-art boundary

The collision theorem is universal hashing. Learned explicit state targets and recurrent carriers have substantial prior art, as do predictive-state representations, communication games, and state-reification networks. The defensible project hypothesis is the combined training protocol:

finite-precision source-deleted packets trained through functionals generated only after commitment, with learned post-source updates and fresh output recoding.

This composition may be experimentally new for Shohin. It is not claimed as a world-first primitive without a broader literature review.

Primary starting points: