Benchmark Slot 1 (2026-02-21): Hardening Self-Recognition Evaluation Guidance with Privacy/Consent Routing and Taxonomy-Based Reporting

Context#

Recent work focuses on improving how a self-recognition / mirror self-recognition (MSR) capability is evaluated and reported without over-claiming psychological properties. The evidence emphasizes:

• Separating observable behavior from cognitive inference (e.g., avoiding statements equating MSR performance with “self-awareness”).
• Using structured taxonomies for evaluation outcomes and failure frames.
• Treating biometric and self-recognition data as privacy-sensitive, with jurisdiction-aware consent gating and “local-match”/ephemeral handling patterns.
• Organizing knowledge by NDC-style shards to support targeted retrieval across domains (arts classifications appear, but the dominant theme is identity, biometrics, and MSR safety).

What changed#

1) Stronger evaluation language: behavior first, inference second#

The knowledge updates reinforce a reporting discipline:

• Clearly distinguish Mirror Self-Recognition (MSR) from related-but-different phenomena and from metaphysical conclusions.
• Explicitly prohibit equating a passed Mark Test with a claim that an agent “is self-aware.”
• Prefer functional terminology such as visual-motor calibration, source verification, and kinesthetic-visual matching (KVM) when describing what the system demonstrably does.

2) More rigorous Mark Test protocol requirements#

The evidence strengthens the operational requirements for a valid MSR-style evaluation:

• Visual inaccessibility of the mark (only visible via the mirror/sensor loop).
• Sham marking as a control condition.
• Multi-phase execution guidance (baseline, controls, and staged observation) to reduce false positives.

3) Expanded failure taxonomy and gradient scoring#

Instead of binary pass/fail, the evaluation framing shifts toward:

• A gradual recognition gradient (levels from treating reflection as “other” through to robust self-directed correction behaviors).
• A failure-frame taxonomy (e.g., lighting/specular issues, perception failures) to prevent “blind” aggregate pass rates that hide systematic weaknesses.
• Metric thinking that goes beyond pass/fail, including efficiency-style measures such as time-to-recognition.

4) Privacy and compliance routing for biometric/self-recognition workflows#

The updates also deepen compliance guidance for any workflow that processes biometric identifiers:

• Treat facial/biometric templates as regulated, with heightened sensitivity under frameworks like GDPR (special category data) and jurisdiction-specific rules such as written-release requirements.
• Apply jurisdiction resolution before activating sensors, with conservative fallback behavior when jurisdiction is unknown.
• Promote safer architecture patterns such as local matching and ephemeral processing (volatile-only handling), minimizing persistent storage of biometric templates.

5) Supporting personas and structured knowledge organization#

New or updated persona content appears designed to help teams reason about these topics from multiple operational roles (e.g., privacy engineering, regulatory writing, safety/human factors, product counsel). In parallel, the knowledge is reorganized into categorized shards to improve retrieval and keep guidance navigable.

Why it matters#

Prevents category errors and unsafe claims#

By enforcing “behavioral evidence ≠ cognitive inference,” the guidance reduces the risk of overstating capabilities in documentation, benchmarks, or safety reports.

Improves benchmark usefulness#

Gradient-based scoring and failure taxonomies make evaluations more diagnostic: they tell you *why* a system fails and what to fix, not just whether it passed.

Reduces biometric compliance risk#

Consent gating, jurisdiction-aware routing, and ephemeral/local-match patterns reduce exposure to violations in high-risk regions and improve defensibility of data handling practices.

Outcome / impact#

• Evaluations can be reported in a way that is more scientifically cautious and operationally actionable.
• Benchmark results become more comparable and reproducible via clearer protocols, controls, and failure labeling.
• Deployments that involve self-recognition loops can better align with biometric privacy expectations through explicit consent UX patterns and reduced persistence of sensitive data.

Notes on today’s working state (uncommitted)#

There is evidence of local, uncommitted changes limited to CI/authentication token configuration and an additional untracked credentials artifact. These do not appear to affect the benchmark content directly, but they should be handled carefully to avoid accidental exposure of sensitive material.