Reproducibility breaks down fast
When code, model state, environment details, and output commitments are separated, reviewers inherit narrative claims instead of computation evidence.
Reproducibility and Provenance
Scientific Research
Scientific AI workflows need more than model accuracy. They need reproducibility, provenance, and a way to share results across institutions without asking every reviewer to trust a private runtime. Aethelred gives research teams TEE, zkML, and hybrid verification plus Digital Seals that can anchor peer review, sealed result exchange, and auditable experiment history.
CURRENT REFERENCE ARCHITECTURE
Sealed experiment outputs
Digital Seals bind model hash, input hash, output hash, timestamp, and consensus evidence into one verifiable record.
Confidential dataset handling
TEE-backed blind compute keeps sensitive research inputs inside an attested execution boundary.
Lab-to-lab portability
Seals are relayable across IBC-compatible chains and queryable through SDKs and APIs.
5 SystemsOptional zkML proof coverage
4 TEEsConfidential compute platforms
10 ServicesLocal devnet for rehearsal
Workload Pressure
Why this workload is hard
Research teams need verifiable results that survive peer review, institutional handoffs, and restricted-data constraints.
Restricted data cannot move freely
Cross-lab studies and clinical or regulated datasets need blind-compute boundaries instead of broad dataset sharing.
Collaboration needs shared proof
Institutions need a portable record that another team can verify without re-trusting the original runtime or operator.
Why Aethelred Fits
Map the workload to the current protocol surface
The protocol fits research when the goal is to prove what ran, preserve confidential inputs, and expose result provenance as a first-class artifact.
FIT
Current Protocol Fit
Digital Seals for experiment provenance
A seal can act as the canonical record of an experiment outcome, including the model hash, output commitment, block height, and validator evidence.
FIT
Current Protocol Fit
TEE or hybrid execution for sensitive studies
Confidential workloads can run inside SGX, Nitro, SEV-SNP, or H100 Confidential Computing, with zkML added when mathematical proof is also required.
FIT
Current Protocol Fit
Research does not need a separate stack
The same SDKs, APIs, local devnet, and seal verification tools used for product workloads can back reproducible research pipelines.
Reference Workflow
A current-state flow for research workloads
A practical research workflow looks like sealed execution, not just a paper appendix.
Package the experiment boundary
Bind the model version, input dataset reference, and execution assumptions before submission.
Run with the right verification mode
Use TEE for confidential data, zkML for mathematical proof, or hybrid mode when both must agree.
Promote the result into a Digital Seal
Once supermajority agreement is reached, the result becomes a verifiable computation artifact rather than an internal log line.
Share the seal instead of a trust me claim
Reviewers, collaborators, and downstream systems can verify the output through APIs, SDKs, or on-chain interfaces.
Protocol Mapping
Which Aethelred surfaces matter most
These are the protocol surfaces that matter most in research-oriented deployments.
| Requirement | Protocol Surface | Why It Matters |
|---|---|---|
| Reproducible output record | Digital Seals | Seal records preserve hashes, provenance, agreement power, and validator evidence in one portable object. |
| Confidential dataset handling | TEE attestation | Blind compute prevents validators from seeing plaintext inputs while still producing auditable evidence. |
| Mathematical proof of execution | zkML / hybrid verification | Five proof systems are available when reviewers need proof-backed result reproduction. |
| Lab-to-lab verification path | SDKs, APIs, and IBC relay | Results can be checked programmatically and consumed outside the originating environment. |
Move from research prototype to a sealed verification flow.
Start in the developer docs, rehearse the workload in Infinite Sandbox, and use the current verification surfaces instead of carrying the old standalone research mock forward.