Benchmark Slot Update: Reproducible Self-Recognition Evaluation Framing and NDC-Sharded Knowledge Indexing

Context#

This slot’s activity is dominated by content and indexing work around a “self-recognition” theme, alongside repeated reorganizations that partition indices into Nippon Decimal Classification (NDC) shards. In parallel, there is a small change in CI authentication token configuration, and an untracked credentials artifact appears in the working directory.

What changed#

1) Benchmark framing for self-recognition evaluation#

New/updated materials focus on evaluation methodology for self-recognition, including:

• Emphasis on making evaluations reproducible and benchmarkable, rather than ad hoc.
• Guidance to move beyond a single pass/fail outcome and track granular performance metrics (for example, time-based recognition metrics and error taxonomy tagging).
• Methodology discussions spanning different evaluation angles (e.g., “inner speech vs active inference vs baseline,” and the role of ablations), framed explicitly as a benchmark standardization gap.

While much of this content is expressed as “knowledge packs,” the user-facing intent is clear: define sharper, more repeatable evaluation standards and reporting structure so that results can be compared over time and across implementations.

2) Knowledge indexing reorganized into NDC shards#

The index content is repeatedly reorganized into NDC-based shards, and new/updated entries cover multiple NDC areas relevant to the broader self-recognition and governance narrative. Examples of covered domains present in the evidence include:

• NDC 700-series references for arts/fine arts and related subdivisions.
• NDC 800-series references for language-related classification context.
• NDC 200-series context around Japanese institutional history.
• NDC 600-series framing of identity/biometric operations as an end-to-end industry workflow, including lifecycle controls and auditability themes.

The practical effect is improved organization and retrieval: materials are grouped into smaller, classification-aligned units, which typically supports faster search, cleaner assignment, and less monolithic index churn.

3) CI authentication tokens adjusted (small but sensitive)#

There is a small, balanced edit to an authentication-token configuration used for CI. The change is limited in scope (equal number of insertions and deletions), suggesting rotation/normalization rather than expansion.

In addition, there is an untracked credentials JSON present locally. This is not incorporated into the tracked changes, but it is a security concern in day-to-day workflows and should be handled carefully (kept out of commits and cleaned up if accidental).

Why it matters#

• Benchmark quality and comparability: The evaluation-oriented updates push toward measurable, repeatable outcomes (metrics + error taxonomies) instead of one-off demos. That’s essential if “self-recognition” claims are to be assessed credibly.
• Scalable knowledge retrieval: NDC sharding reduces the operational cost of keeping a large, evolving knowledge base searchable and maintainable.
• Operational hygiene: Token/config tweaks are routine, but the appearance of a local credentials artifact is a reminder that benchmark work often spans automated pipelines where credential handling can become a hidden risk.

Outcome / impact#

• Clearer structure for discussing and designing reproducible self-recognition benchmarks, including more detailed reporting beyond pass/fail.
• Improved classification-based discoverability through NDC-sharded indexing.
• Minor CI token configuration maintenance, with a note to address local credential artifacts to avoid accidental exposure.

No changes detected?#

Changes were detected for this slot: primarily knowledge/indexing updates and a small CI token configuration edit. No code-level benchmark harness, datasets, or hardware-specific additions are evidenced here.