Research pathway
Native ERAIS Model Research
Test whether one native ERAIS profile can clear a bounded domain gate without turning an engineering run into a general model claim.
Your starting point
A problem worth resolving.
This is a fit when
- A bounded domain-learning or architecture question with a qualified baseline
- A partner who values a decisive negative result and rigorous run/recovery evidence
When another approach is a better fit
- A request for an Australian ChatGPT, frontier pretraining, or a production assistant
- A sponsor requiring a positive quality or efficiency outcome
The useful output
What you take away.
- 01Hypothesis, protocol, baseline, and promotion contract
- 02Environment, data, rights, model, and compute manifests
- 03Run, failure, pause, resume, and recovery ledger
- 04Quality, failure, generation, and resource analysis
- 05Independent review and promote, redesign, retain-as-tooling, or stop record
Where this stands
The basis. The question to test.
Run only through a qualified NF-C protocol with lawful rights, isolated holdout, useful-quality threshold, bounded compute, repeated result plan, and independent review.
Existing basis
- Two governed native-byte training arms each completed 262,400 updates and 536.3M valid byte-level training targets on an ordinary four-thread CPU
- Exact checkpoint continuation, causal execution, fixed-panel evaluation and adverse-result retention
- The richer cognitive stack followed a materially different learning trajectory but did not earn quality promotion on the preregistered fixed panel
Evidence still needed
- Useful held-out or open-ended quality against qualified dense controls
- Stored-capacity and active-path matched comparisons with repeated independent evaluation
- Quality-per-compute, sample-efficiency and matched-quality resource evidence
- Safe measured production training and serving envelopes
Working together / 8–16 weeks
From your question to a decision.
- 01Qualify through NF-C.
- 02Freeze the native profile, baseline, holdout, compute, and all-outcome decision tree.
- 03Execute bounded scouts and confirmatory runs as declared.
- 04Review quality, failures, recovery, resources, and uncertainty.
- 05Promote only exact supported wording—or redesign/stop.
What we need to begin
- One falsifiable domain question and qualified baseline
- Lawful data/model/software rights and isolated holdout
- Frozen compute, rerun, stopping, and publication rules
- Independent method/result review
Scope and handover
A decision you can act on.
Customer work is controlled by a signed statement of work. Public pages provide information and do not create a service commitment.
Acceptance conditions
- The frozen protocol, profile, data rights, holdout, baseline, and compute ceiling are preserved.
- Required runs, stops, failures, recovery, and resource evidence are complete.
- Independent review records unresolved objections.
- Delivery acceptance remains separate from any positive claim promotion.
Scope and limits
One native profile, domain question, baseline, rights set, holdout, and compute ceiling. No operational checkpoint or general-model claim follows.
Completed native training and a changed learning trajectory do not establish dense-quality parity, useful open-ended generation, sample efficiency, quality-per-compute superiority, local full-weight viability, or production readiness.
Offer source and review record
Next step
Bring a question worth investigating.
Tell us what is getting in the way, the outcome you want and your timeframe. A short description is enough to begin; leave sensitive material out of the first message.