Deterministic AI Knowledge Infrastructure

DigiEmu Core defines a reconstructible knowledge substrate. It enables snapshot-verifiable state reconstruction, deterministic replay, and audit evidence — independent of any specific AI model.

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Invariant (normative): Same inputs → same reconstructed state → same SHA-256 hash.

Snapshot-verifiable stateDeterministic replaySHA-256 snapshotsClaims + Uncertainty

Position

Not model-level. Not prompt-level. A deterministic substrate for governed knowledge state.

Entry points

Read the Spec Verify in 60 seconds GitHub — Core

Problem

AI systems are typically evaluated by output behavior. However, for governance and verification, the relevant object is the underlying knowledge state — and that state is usually not deterministic or reconstructible.

Claim 1

AI systems are not state-deterministic

Modern AI pipelines accumulate implicit state (retrieval layers, tool chains, model updates, caches). Re-running the same system often does not reproduce the same knowledge state.

Claim 2

Knowledge state is not reconstructible

The underlying knowledge state of a system is typically not available as a verifiable artifact. Inputs, transformations, and intermediate states are not captured in a deterministic, replayable form.

Claim 3

Auditability therefore fails

If state cannot be reconstructed, independent verification is not possible. This blocks regulatory assessment, incident forensics, reproducibility, and standardization.

Consequence

Without reconstructible state, there is no reliable audit surface. Verification becomes dependent on operator trust rather than evidence.

Solution

DigiEmu Core makes the knowledge state of an AI system reconstructible and verifiable. It introduces an explicit knowledge model (Units, Versions, Claims, Uncertainty) and defines deterministic reconstruction with cryptographic snapshots.

Versioned UnitsClaimsUncertaintySHA-256 SnapshotDeterministic Replay

Deterministic knowledge layer

DigiEmu Core defines a reconstructible knowledge substrate below AI applications. Knowledge state is treated as a deterministic object derived from admissible inputs.

Versioned Units

Knowledge is represented as Units with immutable Versions. Only versioned inputs are admissible for replay.

Explicit Claims

Assertions are represented explicitly as Claims and become part of the auditable state, rather than being implicit in model behavior.

First-class Uncertainty

Uncertainty is represented as data (bounds, confidence, incompleteness). It is explicit and audit-visible.

SHA-256 snapshots

A canonical encoding of the reconstructed state is hashed (SHA-256). The resulting snapshot hash is a verifiable state identifier.

Tool-agnostic verification

Independent implementations can replay the referenced inputs, reconstruct the state, compute the hash, and verify equality.

Invariant

Same inputs → same reconstructed state → same hash.

Architecture

The core is designed around a simple proof obligation: state must be reconstructible, and verification must be independent.

Write → Snapshot → Replay → Verify

Step

Write path

Units, versions, claims and uncertainty are appended deterministically. Inputs are explicit.

Step

Snapshot

A SHA-256 state identifier is computed from the reconstructed knowledge state.

Step

Replay

Given the referenced inputs, any independent implementation can rebuild the same state.

Step

Verify

Compute the hash and compare to the snapshot. Produce a PASS/FAIL evidence report.

Invariant

Same inputs → same reconstructed state → same SHA-256 hash.

Deterministic replay

Evidence

Verification emits a minimal report: snapshot id, computed hash, comparison result, and referenced inputs.

PASS / FAIL

Tool-agnostic

Any independent implementation can reproduce the computation and verify the hash, without trusting the original system.

Independent replay

Snapshot Verification

Verification is defined as deterministic reconstruction plus cryptographic hashing. The snapshot hash acts as a verifiable identifier of reconstructed knowledge state.

ReplayCanonical EncodingSHA-256Evidence

Step 1

Select snapshot

Choose a snapshot hash and its referenced inputs (Units, Versions, Claims, Uncertainty).

Step 2

Deterministic replay

Reconstruct the canonical knowledge state from the referenced inputs using deterministic replay.

Step 3

Compute SHA-256

Serialize the reconstructed state using canonical encoding and compute the SHA-256 hash.

Step 4

Compare + report

Compare computed hash vs expected snapshot hash and generate a verification report (PASS/FAIL).

Output

The verification result is binary: PASS if hashes match, FAIL otherwise. A report links snapshot, inputs, and computed evidence.

Compliance & Governance

This is not a policy claim. It is a verification surface: what can be reconstructed, hashed, replayed, and evidenced.

Evidence-first

Reconstructible state

State replay

Given a snapshot and its referenced inputs, the knowledge state can be rebuilt deterministically.

Cryptographic identifier

Hash verification

The rebuilt state yields a SHA-256 hash that can be compared to the expected snapshot id.

Evidence report

Audit evidence

Verification emits a minimal PASS/FAIL report with referenced inputs and computed hash.

Audit-relevant controls

Traceability
Snapshots reference explicit inputs (units, versions, claims). No hidden state is required for verification.
Reproducibility
Independent replay can be executed by third parties to confirm the same state identifier.
Change governance
Decision logs (DECs) and versioning allow auditors to review what changed, when, and why.

Limits

Model behavior is not proven
A verified knowledge state does not prove a model’s internal reasoning or outputs are correct.
Truth is not guaranteed
Claims can be audited for provenance and structure, but factual correctness remains an epistemic task.
Operational controls remain external
Access control, incident response, and human oversight are required in addition to deterministic replay.

Regulated AI contexts

Deterministic replay and snapshot evidence support audit workflows in regulated settings: incident forensics, reproducible research, governed knowledge bases, and risk documentation.

incident reconstruction

evidence trails

change governance

Reference Implementation

A public reference implementation demonstrates deterministic reconstruction, snapshot hashing, and evidence-based verification. The goal is to enable independent replay and audit review.

Verify in 60 seconds

1. Select snapshot hash
2. Deterministically replay referenced inputs
3. Compute canonical SHA-256 state hash
4. Compare + generate report (PASS/FAIL)

Open Reference Page

Artifacts

Core Specification v1.0 (PDF)Determinism — Formal Definition v1.0 (PDF)Conformance Requirements v1.0 (PDF)

Additional governance and audit documents are available under the Reference page.

Repositories

GitHub — Core GitHub — Reference

Use Cases

DigiEmu Core is intended as an infrastructure substrate. The following use cases follow from reconstructible state, deterministic replay, and snapshot-verifiable evidence.

Regulated AI systems

Provide a reconstructible audit surface for systems operating under regulatory scrutiny. Snapshots enable evidence-based review of knowledge state.

Incident forensics

Reconstruct the knowledge state associated with an incident. Deterministic replay reduces reliance on operator memory and ad-hoc logs.

Reproducible research

Enable reproducible knowledge contexts for experiments. Snapshots and deterministic replay support verifiable replication of results.

Governed knowledge bases

Introduce explicit claims and uncertainty as governance objects. Versioned units support controlled change and review processes.

Certifiable standards

Define measurable conformance criteria: canonical encoding, hashing procedure, replay determinism, and verification reports.

Enterprise AI substrate

Separate the knowledge substrate from application logic. Use a deterministic, hash-identifiable state for internal assurance and external audits.

Engage

DigiEmu Core is a deterministic knowledge standard intended for verification and governance. Engagement paths differ by context.

Researchers

Evaluate the model, replay procedure, and evidence format. Use snapshots for reproducible experiments.

Read the Spec Determinism Formal Definition (PDF)

Institutions

Adopt the governance surface: versioned units, decision logs, audit procedures, and conformance requirements.

Reference Page Governance Framework (PDF)

Enterprise partners

Evaluate deterministic replay and snapshot verification as a compliance substrate for regulated deployments and incident reconstruction.

Validate a Snapshot Compliance & Governance Contact

Note

The standard is designed to be independently replayable. Verification does not require privileged access beyond the referenced inputs and the published procedure. Evidence is designed to be audit-ready (replayable inputs, deterministic hash comparison, and human-reviewable reports).

Snapshot-verifiable state • Deterministic replay • Audit evidence