95.90%
Teacher next-token agreement
491 / 512 choices · 64 sequences
Last 8 valid positions per sequence
21 differing choices. Both the agreement and distribution-divergence gates pass.
Operator compilation · Learned corrections · Cross-layer composition
Can we preserve a language model’s behavior when its transformer operators are recompiled into population arithmetic—and then learn a smaller mesh that combines layers?
This log grows as meaningful results are checked. Preparation milestones do not count as new model-quality measurements.
Known-test: 491 / 512 matches (95.8984%), mean KL 0.00594440. Fresh article-disjoint test: 125 / 128 (97.65625%), mean KL 0.00601305. Each independently passes the predeclared ≥95% agreement and ≤0.05 mean-KL thresholds. The frozen model was not fitted or selected using these scores.
All 64 replacement blocks ran in sequence, without a native transformer tail or teacher-state resets. Embedding, final RMSNorm, and vocabulary head remained native. The run completed at 01:44 UTC; final candidate states were archived before scoring.
The independent audit checks final state hashes and Q24 decoding, source and frozen-payload bindings, selected original endpoints, hidden-state errors, and score-row aggregates. A separate recovery audit verifies all 2,811 published files. It does not independently recompute logits, replay the full chain, or locally rehash the large base payloads.
The instance is terminated and both scoped controllers retired. Cumulative compute estimate: $23.02 of the same $50 cap, not an invoice. This is a short-context teacher-fidelity result, not task accuracy, generation quality, compression, speedup, or successful fusion.
All 64 rebuilt base receipts match the frozen originals. The independent recovery audit also confirms that all 64 correction archives are byte-exact, all 107,186 frozen payload records are unchanged, and all 276 original source files are preserved. The five approved continuation and output-head files are the only source additions.
The first four blocks reproduce their saved integer fixtures exactly: 163,840 values checked, zero mismatches. The execution freeze binds the selected original teacher endpoints and the explicit reference policy. Known-test workers have now started after that freeze, with no refitting.
This establishes a verified continuation checkpoint, not all-64 held-out quality. The host compares base receipts, not the large base payload bytes; it does not independently replay the full chain or recompute head logits. Estimated cumulative compute is approximately $16.54 under the same guarded $50 cap, not an invoice.
The A10’s ordinary head reproduces the earlier saved-state report byte for byte: 124 / 128 agreement; mean KL 0.00751416. This repeats the validation diagnostic; it is not a fresh student trajectory.
A fresh read-only audit rehashes all 352 uploaded inputs and verifies all eight selected native endpoints against the original archives. Fresh initial embeddings remain exact; the differing regenerated validation endpoint is preserved separately. 22 of 64 rebuilt base receipts match the original receipts retained locally. The guest checks base payloads; this host audit compares receipts, not the large payload bytes.
One guarded A10 is active within the same cumulative $50 cap. Estimated compute through this check is approximately $16.00, not an invoice. No all-64 held-out result yet.
The explicit frozen-native-endpoints-v1 policy selects archived original h0 and h64 for every split. Fresh h0 must match exactly. Fresh h64 and intermediate native states are diagnostics only; all fresh endpoints are retained.
All 55 focused tests pass, including a replay of the actual previous mismatch and rejection of a corrupt selected target. The historical strict-reproduction test still fails on that fixture: selection is not successful reproduction. The model, corrections, original 276 source files, corpora, no-refit rule, exact base gates, and held-out thresholds stay fixed. A single frozen continuation is launched.
The A10 successfully scores the saved all-64 validation states. The following strict gate stops on the last eight positions of one regenerated validation sequence, before rebuilding bases or executing held-out student inputs. The failed run is recovered and closed; all eight allocations up to this point are absent. Cumulative compute estimate: $15.33. The later entries above describe the authorized continuation.
We have executed a chain of 64 HDC replacement blocks on both predetermined held-out sets. Each block receives the preceding replacement’s output; there is no native transformer tail and no reset to the teacher’s intermediate state.
The frozen replacement chain passes both original quality gates when scored through Qwen’s ordinary native output stage on an NVIDIA A10. No corrections were refitted after seeing these results.
95.90%
491 / 512 choices · 64 sequences
Last 8 valid positions per sequence
21 differing choices. Both the agreement and distribution-divergence gates pass.
97.66%
125 / 128 choices · 16 sequences
Articles disjoint from prior corpora
3 differing choices. Both gates pass independently on this fresh set.
Agreement with a teacher is not task accuracy, factual correctness, or free-running generation quality. These are 80 short-context sequences, at most 64 tokens each; broader generalization remains to be tested.
The original model, corrections, teacher endpoints, and quality thresholds stayed frozen. This establishes useful compositional fidelity for this test protocol. It does not establish a smaller or faster model, or successful cross-layer fusion.
Historical comparators: the first-four replacement test matched 493 / 512 choices (96.29%, mean KL 0.00448743), with 60 native blocks remaining. The all-64 saved-state validation diagnostic matched 124 / 128 (96.875%, mean KL 0.00751416), on data used for correction selection. Neither is a substitute for the new held-out results.
The source is the pinned mlx-community/Qwen3.8-27B-4bit checkpoint: a 5,120-dimensional residual stream, with 48 linear-attention / GatedDeltaNet blocks and 16 full-attention blocks. We preserve the learned information and causal structure while changing the arithmetic used by the replacement operators.
Dense floating-point projection operators are represented through multibit population counts, integer operations, and explicit fixed-point boundaries. The correction mesh is compiled into this arithmetic too.
Much of Qwen’s weight information and block structure. This is a new execution representation with learned corrections—not a small, independently learned binary model.
The artifact is a reconstruction package, not a ready-to-ship model. All 64 correction archives, source bindings, saved calibration and final held-out states, scores, and frozen payload manifests are preserved. The large compiled base payloads were not retained locally. They were rebuilt on the A10 and passed the guest’s exact payload gate; the host audit confirms matching original receipts. The cloud instance has now been terminated.
A compiled block carries most of the original transformation. A smaller, SVD-guided response mesh learns to correct its error. Importantly, we fit against the states produced by the actual preceding student blocks, so calibration sees accumulated drift.
A scalar is encoded as a count plus a group scale. The main projection representation uses P = 2,046, 11 packed count bits per coordinate, and groups of 64 coordinates. Residual interfaces use Q24 fixed point. This is multibit numerical coding, not a one-bit sign vector.
For a group G with n coordinates, weight counts ai and activation counts ci give an exact integer accumulator:
Ab and Cd are packed bit planes of the counts. The last expression is before specified rounding and requantization. The AND/popcount path and optimized integer-count path express the same algebra. Exactness is for encoded numbers; it does not remove quantization or nonlinear-approximation error relative to Qwen.
Let bt be the base block’s output on the student’s causal input. E extracts standardized training-activation SVD coordinates; μ is their center. Ur contains dominant output-error directions.
With teacher targets Y and base outputs B, let R = Y − B, β be the mean residual, and Rc = R − 1βᵀ. For row-wise feature matrix Φ, the mixer is a ridge-regression fit:
Rank, ridge penalty, and correction strength are selected on validation. The frozen first four blocks are reused; deeper corrections are fitted on composed student trajectories. Training uses floating-point SVD and linear algebra. Evaluation uses the reloaded compiled correction, with integer ReLU, fixed-point rounding, and signed truncation for prefix means.
These SVD directions are data-dependent reconstruction coordinates. They are not established “meaning eigenvectors” of a nonlinear transformer. Native reference targets supervise fitting; the deployed replacement does not read teacher targets.
For two strictly linear operators, SVD exposes an exact intermediate coordinate map:
The map V₂ᵀU₁ may offer a compact connection between subspaces. Real transformer blocks also contain normalization, attention, gates, nonlinearities, recurrent state, and rounding. A learned fused mesh must approximate that intervening computation; the linear identity alone does not justify deleting a block.
Compressing a small correction cannot substantially shrink the model while large inherited operators remain. Fusion becomes useful only when it removes substantial base payload and preserves quality on unseen inputs.
Model-quality evidence and engineering recovery are separate milestones. A repaired runner is valuable, but it is not a new accuracy result.
Establish an integer/population base and an SVD-guided correction; reload and evaluate the compiled artifact. Exact encoded arithmetic is distinct from approximation to the native block.
493 / 512 teacher choices match (96.29%); mean KL 0.00448743. The other 60 transformer blocks remain native.
Initial two-block fusion removes 49.59% of that HDC pair’s payload, but validation agreement falls to 117 / 128 (91.41%). A separate tiny training probe shows that compiled parameters respond to an output-KL signal.
Training and validation trajectories reach depth 64 without a native tail. Validation hidden-state update-relative L2 is 8.4701% over 512 tokens. All 64 correction archives and the model freeze are recovered and checked.
An initial Metal-only helper failed on NVIDIA. The repaired ordinary head scores saved all-64 states: 124 / 128 agreement on both Mac and A10. On the A10, mean KL is 0.00751416. No refitting follows this diagnostic.
The final eight positions of one regenerated validation sequence differ from the archived native reference. Initial embeddings and train / both held-out final native endpoints match exactly. That strict gate remains a failed historical result; no compiled-base rebuild or held-out student evaluation follows in that run.
Version the choice of archived h0/h64 as scoring authority, require fresh h0 equality, and preserve fresh endpoint differences. Local failure replay and 55 regressions pass; the new A10 continuation passes selection and starts exact base rebuilding.
All 64 base receipts and correction archives match the originals. The first-four integer fixture is exact, and the execution freeze binds original payload records, sources, selected endpoints, and reference policy.
Known-test491 / 512 choices match; fresh-test125 / 128. Mean KL is 0.00594440 and 0.00601305 respectively. Final states and score aggregates are independently checked; cloud recovery and shutdown are complete. No test fitting occurred.
| Probe | Measurement | Interpretation |
|---|---|---|
| Initial pair fusion | 49.59295% less HDC pair payload 91.40625% validation agreement Mean KL 0.111719 | Real base removal; quality gate failed. |
| Compiled-parameter training | KL 0.00586420 → 0.00148589 74.66% reduction | One eight-token training prefix, two scored positions, with native blocks downstream. Positive trainability evidence, not validation recovery. |
| Later basic fusion mesh | 15.2923% hidden-state update error 45.6347% pair payload reduction | Failed the 10% state-error gate. No output score measured. 15.29% error does not mean 84.71% accuracy. |
The unfused four-block HDC payload was 3,178,181,201 bytes versus 856,404,288 bytes for those native 4-bit blocks—about 3.71× larger. Pair-relative fusion savings do not establish compression versus the original checkpoint.
Each replacement sees the accumulated error of earlier replacements. Even a small local approximation can grow or shrink downstream. Under a local Lipschitz assumption for the native block Fℓ:
We have not measured certified Lℓ values. This motivates testing the whole chain; it is not a numerical guarantee.
The completed quality experiment restored and froze the existing model, without fitting a new one. Both predetermined splits have now completed the 64-block student trajectory and passed their output gates.
Check source and input hashes; restore corrections and saved states.
Score the detached saved-state validation fixture before expensive rebuilding.
Original h0/h64 are authoritative. Fresh h0 exact; fresh h64 retained as diagnostics.
64 / 64 base receipts match origin. First-four integer fixtures exact; execution freeze independently verified.
Both splits ran through all 64 replacements, with no native resets or fitting. Final states archived before scoring.
Independent final-state and score audit passes. All 2,811 recovery files verified; instance terminated.
| Split | Sequences × maximum length | Role | Scored output positions |
|---|---|---|---|
| Training | 32 × 32 tokens | Fit bases / corrections | Not an acceptance set |
| Validation | 16 × 32 tokens | Select correction candidates | 128 diagnostic choices |
| Known-test | 64 × 64 tokens | Held-out quality gate | 512 planned choices |
| Fresh-test | 16 × 64 tokens | Article-disjoint from prior corpora | 128 planned choices |
Score the last eight valid positions in each sequence. Each held-out split must independently achieve at least 95% teacher top-1 agreement and mean KL ≤ 0.05. No held-out results may be used for fitting or selecting this frozen candidate.
In the previous run, regenerated native validation h64 differed at sequence 15 (zero-based), positions 24–31: 33,712 of 2,621,440 valid values changed, maximum absolute difference 2.5, relative L2 0.001259757934334714. The difference is numerical, not archive serialization. Its specific cause is not established.
All four initial-embedding files and final native train, known-test, and fresh-test endpoints matched the original exactly. These are native reference checks, not held-out HDC model scores.
Implemented and verified: frozen-native-endpoints-v1 explicitly selects the archived original teacher endpoints. Fresh initial embeddings must still match exactly; fresh final and intermediate native states are diagnostic, not replacement targets. Fresh endpoints are preserved separately. Policy and selection hashes are bound to the execution freeze and final audit. Model payloads and held-out thresholds remain unchanged.
This is a documented change to the reference contract, not evidence that the original byte-equality gate now passes or that the numerical cause has been cured.
This page summarizes retained experiment artifacts and independent checks. All nine allocations in this campaign are closed. The last instance was confirmed absent at 01:47 UTC, and its two scoped controllers were retired. Cumulative compute estimate is $23.019946 under the same $50 cap ($34.529919 including the 50% reserve), not an invoice. Shutdown preceded the unchanged compute cutoff of 8 September 08:30:51 UTC and provider deadline of 09:30:51 UTC.
The final audit checks frozen source/payload maps, base receipts, correction bytes, selected endpoints, final-state hashes, exact Q24 decoding, independently recomputed hidden-state errors, and score-row aggregates. It does not independently recompute all head logits, locally rehash large base payload bytes, or replay the full trajectory. Those limits remain part of the result. The now-retired shutdown guard depended on the supervising Mac and network, not a provider-hosted TTL.
Download evidence snapshotHashes identify retained internal evidence; they are not a public replication package or a proof by themselves. The downloadable snapshot contains the publication-safe measurements and their scope, not checkpoints, raw corpora, or infrastructure credentials.