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YewForge Digital / Engineering

System page · Model research

Nano Factory

Traceable factory infrastructure and an evidence-led research path for narrow specialist local models. The strategic hypothesis is under test, not proven.

Active experimentLast reviewed: 15 July 2026Evidence classes: IMPLEMENTED · VALIDATED · EXPERIMENTAL

Objective and architecture

Nano Factory prepares specialist-model research through immutable sessions, deterministic states, append-only events, campaign metadata, artefact descriptors, recovery inspection and a command-line interface. Its primary research direction has used a small instruction-tuned language model target, with bounded adapters and evaluation gates where separately authorised.

Sessions

Each execution has a numbered identity, lifecycle state, duration and result boundary.

State machine

Initialisation, validation, preparation, evaluation and approval states are deterministic.

Artefact lineage

Hashes, timestamps and relationship references support later inspection.

Recovery

Unexpected exit is surfaced through last completed state and checkpoint information.

Validation and research result

Infrastructure R0 passed its bounded session, logging, state, campaign, hashing and recovery tests without beginning the then-blocked training gate. Later R1.1 research completed a controlled continuation to a fixed ceiling. It improved bounded development measures, but passed only four of nine predeclared qualification checks.

KEJ-EV-007IMPLEMENTED · EXPERIMENTAL
Claim
Traceable factory infrastructure exists, and a bounded specialist-model candidate was experimentally evaluated.
Method
Session/recovery tests; controlled continuation with frozen qualification checks and sealed-holdout protection.
Result
Infrastructure passed; the candidate reached a terminal REVISE CANDIDATE outcome.
Limitation
No successful candidate, replacement claim, deployment, routing, promotion or held-out tournament evaluation is established.
Source period
2026 infrastructure and research records.

Failure, recovery and planned development

The result is research only. The qualification thresholds were not lowered or reinterpreted, and no further training or holdout access follows automatically. Future roles such as routing, state interpretation and evidence categorisation remain hypotheses subject to separate evidence and approval.

Dataset Foundry supplies the qualification boundary; Evidence and External Memory supplies traceability; agent governance prevents automatic promotion.

Sources: canonical Nano Factory infrastructure, R1.1 corpus/holdout and terminal continuation result records. Model digests, campaign identities, artefact locations and raw dataset details are excluded.