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Trust & Evidence

Trust is a property of the evidence chain.

LargeQuant uses deterministic identity, cryptographic integrity, bounded execution and evidence-linked quantitative workflows so important results remain inspectable.

IDENTITY

Versioned model and record identity

Evidence-bearing records can bind exact model, protocol, artifact and software identities.

HASHING

SHA-256 content integrity

Content hashes are used across datasets, artifacts, evidence and release provenance.

SIGNING

Ed25519 signatures

Signed evidence protects exact payload integrity and binds the record to a signing key.

EXECUTION

Trusted adapter boundaries

External solver and HPC interfaces use bounded, approved execution paths instead of arbitrary user shell commands.

RELIABILITY

Quantitative gates

Policy-bound autonomy can pause when candidate evidence does not satisfy deterministic and probabilistic criteria.

PROVENANCE

Institution-linked evidence

Registered institutional keys, signed external submissions and provenance records preserve where quantitative evidence came from.

Cryptographic integrity

Know exactly what a signature establishes.

A valid signature proves that a specific key signed an exact payload and that the signed payload has not changed. Scientific adequacy still depends on the underlying methodology, evidence and domain review.

01
Integrity
Detect whether an evidence payload has changed since signing.
02
Provenance
Link evidence to the key that produced or registered it.
03
Reproducibility
Preserve the data/model/execution context required to reconstruct a quantitative result.
Evidence in operation

Trace a result backwards.

A quantitative claim can be linked to dataset lineage, model protocol, execution artifacts, uncertainty assumptions, decision rules, benchmark evidence and institutional provenance.

Explore Evidence & Provenance
Responsible disclosure

Found a security issue?

Use the LargeQuant contact channel and include enough technical detail for reproduction. Do not include live credentials, secrets or unnecessary personal data.

Contact LargeQuant