Observed train-loss reduction
Within the published full-stack run; generation remains held.
Measured evidence
Full-stack training
Bounded throughput, loss, safety-counter, warning, and claim-boundary evidence from the full training path.
Recorded finding: The integrated training path runs with observable safety and quality signals.
On this page
Currency and next evidence gate
Decision signals
Key metrics and controls
Each value is mapped directly from the recorded summary artifact and retains its experimental, historical, or control context.
Mean compute throughput
Public run aggregate, not production serving throughput.
Mean pair-work reduction
Within the approved run geometry.
Release decision
Current evidence does not approve external distribution.
Source-linked visual evidence
Readable at a glance. Inspectable in detail.
Charts are generated from normalized source fields. Every visualization includes its exact values in an accessible table.
Method
How to interpret this pack
- The full-stack pack combines loss, throughput, pair-work, warning, checkpoint, generation, and provenance signals.
- The release decision remains held because favorable training signals do not close generation-quality and operational risks.
Claim boundaries
Current evidence does not approve commercial product readiness or unrestricted generation claims.
- Full-stack local integration-training aggregate evidence only.
- Loss movement is a trainability and integration signal, not model-quality proof.
- Throughput is published with warning status because stream irregularities were detected.
- Checkpoint write pressure is published only as aggregate diagnostic evidence.
- Control-plane pressure is published only as aggregate diagnostic evidence.
- Eval trend claims are blocked because only one eval record is present.
- Generation sample quality remains held for manual review; no mature generation-quality claim is made.
- Source provenance is reduced to a clean-stamp status; private commits, paths and command details are withheld.
- Parameter and token counts are normalized to M where shown.
- No row-level metrics, raw paths, hashes, invocation payloads, raw prompts, raw datasets, raw outputs, model weights, or source internals are bundled.
- No dense-superiority, benchmark-score, production-readiness, service-level, unrestricted reproduction, or proven-breakthrough claim is made.
Inspection and reuse
Downloads and provenance
Source data
Machine-readable summary
The recorded JSON artifact is staged from the governed public evidence pack during the build. Its currency notice determines whether it may be read as current.
Open source JSONProvenance
Build and evidence receipt
Use the public build receipt to compare the deployed site source, evidence digest, claim posture, and inference release state.
Open build receiptContinue reviewing
Related evidence
measuredpartial
Historical- Observed
- The training continuation path is instrumented and produces governed stability evidence.
- Supports
- Loss signals, stability controls, generation gates, reload behavior, and token-scale receipts.
- Does not support
- Generation quality and broad model capability remain under review and are not mature public claims.
- Next decisive evidence
- Run and seal a current exact-resume continuation with frozen evaluation and generation review.
Decision relevance: research
Review evidencecontrolreviewed
Refresh required- Observed
- Diligence artifacts and boundary controls are explicitly inventoried rather than implied.
- Supports
- Artifact coverage, claim posture, clean-room controls, security review, and controlled gates.
- Does not support
- Readiness for diligence is not a declaration that all product, legal, or release risks are closed.
- Next decisive evidence
- Rebuild diligence coverage from the next clean approved evidence release and retain every unresolved gate.
Decision relevance: diligence · investment · governance
Review evidence