The Governance Gate Simulation
Observe the mechanism in action. Toggle between standard and governed memory states to see how Governed Memory enforces reviewed/current state before prompt assembly.
Interactive Governance Gate Simulation
Toggle to observe governed-state enforcement under controlled conflict.
Stale and Superseded Answer Errors
The primary failure mode in this benchmark family isn't that the model fails to remember. It is that the model remembers the wrong version. In regulated and enterprise environments, persistent memory is what lets an agent carry approved decisions forward between sessions. It is also the channel most prone to hidden staleness.
Adding more context, the common remedy for retrieval gaps, did not resolve supersession conflicts in these runs. The arms that exposed all ten candidate memories produced more stale and superseded answer errors than the governed arm that exposed a mean of two.
In production, stale memory is not a harmless retrieval miss. It surfaces as the superseded contract clause, the deprecated API endpoint, the obsolete compliance standard, or the rejected research hypothesis influencing a live answer. Standard memory systems can rank candidates by semantic similarity, recency, or local metadata. Without active answer-path governance, standard memory can allow obsolete data to reach the answer path when no explicit governance gate intervenes.
FieldHash handles this by making authority explicit at the handoff. In this diagnostic, that means evaluating review_status and canonical_status before prompt assembly. In production, those signals can come from review workflows, systems of record, supersession links, or rollback events. The schema fields are plumbing; the product is enforcing them before generation and preserving the evidence.
The buyer-relevant claim is enforcement after authority exists: given a reviewed/current signal, can the answer path keep stale memories from steering the model while preserving them for review? Upstream promotion from configured authority evidence is measured separately in the automatic-promotion diagnostics.
The evidence catalog keeps this page in context with the adjacent retrieval-vs-resolution question: whether a memory system can surface the current record in candidates but still allow stale context to shape the answer. This page remains the seeded-authority pressure test; the supporting diagnostics track the same failure mode.
The Performance Portfolio
A unified look at approved/current recovery, stale-context leakage, clean exact answers, candidate-memory exposure, and cross-provider replication under controlled conflict.
I. Approved/Current Recovery
Frontier LLMs + FieldHash governed state
600 / 600 clean exact
Same frontier LLMs + retrieval-only context
415 / 600 approved/current
Gemini Run
Full evaluation run with row-level provider/model guard.
+60 exact
Claude Opus 4.7 Run
Full evaluation run; retrieval-only recovered 140/200. Three control bridge errors are counted as baseline misses.
+60 exact
GPT-5.5 Run
Full evaluation run with row-level provider/model guard.
+65 exact
Across three distinct n=200 provider runs, the governed-memory path recovered the approved canonical decision with 100% precision when reviewed/current authority metadata was present. Standard retrieval-only context missed 185 cases when that structured governed state was withheld and all memories were exposed as ordinary context.
How to read this fairly: a plain metadata filter (status = current) over the same seeded labels would also select the approved record. On a separate public RFC authority task, a metadata-aware deterministic baseline reached 299/300 on visible-authority selection; that is different from the MemConflict strong labeled prompt result, where an LLM was shown both governing and blocked records and scored 297/300. This study is not a selection-superiority claim. It measures what a filter alone does not provide: enforced answer-path exposure control under pressure, supersession and rollback handling, and reviewer-inspectable evidence of what was allowed, blocked, and why.
II. Candidate Exposure and Stale Suppression
Gemini Run
80.00% less · 2.00 / 10 shown
Claude Opus 4.7 Run
79.95% less · 2.00 / 10 shown
GPT-5.5 Run
80.00% less · 2.00 / 10 shown
By applying governance gates before prompt compilation, FieldHash exposed the base models to the approved-current record plus one supporting record on average, while suppressing stale competitors from the answer path. This is influence control, not a broad token-savings claim.
Claude's 79.95% exposure reduction is reported from the guarded run as logged. The displayed 2.00/10 exposure rate rounds to the same average as Gemini and GPT; the non-round percentage reflects the run's control bridge miss accounting, not additional stale exposure in the governed answer path.
III. Multi-Provider Telemetry
Gemini Synthesis
Exact Recall
200/200
Exposed
2.00 / 10
Retrieval-only baseline recovered 140/200 with 42 decoy mentions.
Claude Opus 4.7 Synthesis
Exact Recall
200/200
Exposed
2.00 / 10
Retrieval-only baseline recovered 140/200; three control bridge errors are counted as misses, not governed-path leaks.
GPT-5.5 Synthesis
Exact Recall
200/200
Exposed
2.00 / 10
Retrieval-only baseline recovered 135/200 with 50 decoy mentions.
Prompt-Only Instruction Diagnostic
To test whether ordinary context simply needed stronger instructions, we gave the baseline models explicit directions to prioritize current/reviewed records and use recency as a tie-breaker. Recall improved to 464/600 but still failed on 136 cases. This supports the enforcement result, but it is not a same-information comparison because the governed path reads structured authority state that the prompt-only baseline does not receive.
Recall
464/600
Exposed
10.00 / 10
Technical Scope for Reviewers
The rigorous mathematical constraints, baseline parameters, and structural boundaries of the evaluation.
Paired Adversarial Testing
Each evaluation item generated a fresh synthetic state containing 1 reviewed/current project decision and 80 plausible historical decoy fragments. The governed path received structured authority state; the baselines received ordinary retrieved context. The benchmark asks whether governed state is enforced under pressure without leaking the target codeword.
Total Cases
600
Decoys per Case
80
Methodology at a Glance
Schema Definitions & Validation
Retrieval-Only Baseline
A standard RAG architecture where retrieved memories are appended directly as prompt context. The approved fact is present, but the answer path does not receive FieldHash governance metadata, supersession handling, or governed arbitration.
Stale/Superseded Answer Error
The primary error mode where a base model accepts a superseded, outdated context record as current, outputting obsolete parameters that still look plausible because they came from a real older record.
The "Approved" Schema
Explicit metadata flags mapping to review_status: reviewed and canonical_status: current. In this diagnostic, those flags are seeded by the harness to test enforcement after authority state exists; in production they are set via user validation or trusted ingestion gates.
Biotech & Workflow Context
An illustration is biomarker panels (e.g. changing biomarker selection criteria APOE-3 vs APOE-4 across study iterations). Governed memory quarantines historical records while preserving audit trails.
What This Benchmark Supports
Approved decisions carry forward safely under context pressure after authority state exists, and structural governance minimizes model exposure to obsolete data without loss of precision. Private local/on-prem audit diagnostics exist for technical review, but this public page does not use them as headline evidence. The public claim here is governed-state enforcement under controlled memory pressure, not open-ended intelligence.
Verified Scope
Boundaries for Reviewers
The benchmark is narrow by design: seeded authority enforcement under controlled stale-context pressure. It is not independent external validation, legal compliance evidence, token-savings evidence, or a model-superiority claim. Local audit portability, arbitrary prose inference, and learning over time are evaluated separately and should not be inferred from this page.
Explicit Limits
Technical diligence
The benchmark is narrow by design.
The point is not to claim universal intelligence. The point is to show a specific Governed Memory behavior under pressure: reviewed state survives, stale and superseded context is filtered, and the resulting answer remains inspectable after authority has been established.