Benchmark Notes (2026-02-10): Self-Recognition Knowledge Expansion, NDC Sharding, and Desktop/Universe Workflow Maturation

Context#

This update window includes a dense sequence of commits focused on three themes:

1. Expanding and refining “self-recognition” guidance content (including cross-jurisdiction biometric compliance and evaluation rigor). 2. Reorganizing classification indices into NDC-based shards to improve scalability and retrieval. 3. Improving the desktop experience and “universe” authoring/execution workflow, with explicit progress on Windows distribution support.

These changes matter because they tighten the system’s ability to (a) reason consistently about self-recognition and biometrics across legal regions, (b) retrieve knowledge more efficiently as the corpus grows, and (c) support real end-user workflows (authoring, running, and viewing “universe” artifacts) across platforms.

What changed#

1) Self-recognition content: broader coverage, clearer operationalization#

Multiple commits extend “self-recognition” materials, including both “desire” (requirements/intent) and “knowledge pack” evolution.

Based on the retrieved content, notable expansions include:

• Stronger separation of behavioral evidence vs. cognitive inference: guidance explicitly warns against equating passing a mirror-style test with broad claims like “self-awareness,” improving reporting discipline.
• Evaluation structure and failure taxonomy: recommendations include phased execution (baseline/sham/control before test) and categorizing failure frames (e.g., perceptual/input issues), improving benchmark interpretability beyond a single pass/fail.
• Compliance-aware self-recognition workflows: cross-jurisdiction routing logic is emphasized, including the principle that when jurisdiction is unknown, stricter defaults should apply.
• Biometric data classification alignment: the content highlights that biometric templates (e.g., facial feature data used for identification) can qualify as regulated identifiers, affecting consent, retention, and record-keeping expectations.

Impact on “benchmark” work: these additions improve reproducibility and auditability. Teams can now report performance with more granular metrics and document boundary conditions and negative tests, rather than only showcasing successful cases.

2) NDC sharding and arts/history coverage improvements#

Several commits reorganize indices into NDC shards, indicating an effort to scale classification and retrieval as the knowledge base grows.

The retrieved evidence also shows deepened NDC coverage in areas relevant to the corpus, including:

• NDC 700 (Arts / Fine Arts) and its key subdivisions (e.g., art theory, art history, sculpture, painting, printmaking, photography, crafts).
• NDC 702 (Art History).
• Examples of fine-grained craft classification (e.g., “old mirrors / mirror craftsmanship” under a specific crafts subcode).

Why this matters: sharding by NDC reduces retrieval ambiguity and improves maintenance as content expands. It also strengthens classification fidelity for domain-specific packs (e.g., arts/environmental design considerations tied to mirror/reflection risks).

3) Desktop + “universe” workflow improvements, including Windows support#

The commit stream includes:

• Improvements to the universe language editor and general editor UX.
• Enhancements to the chat panel and terminal UX.
• Expansion of desktop distribution options with explicit Windows executable support, alongside broader packaging/distribution adjustments.

Why this matters: these are user-facing improvements that reduce friction in authoring, executing, and reviewing “universe” workflows—especially important when running benchmark-style evaluations that require repeatable runs, checkpoints, and clear visibility into execution.

Outcome / expected impact#

• Better benchmarking discipline for self-recognition: clearer protocols, metrics, and reporting constraints reduce overclaiming and make results easier to compare across runs and environments.
• Improved retrieval and maintainability via NDC sharding: faster navigation, clearer categorization, and less index contention as the corpus grows.
• More practical, cross-platform usage of desktop + universe workflows: better UX and Windows distribution support increase adoption for users who need local execution and interactive tooling.

Notes on local working state#

In the current working directory snapshot, the only visible uncommitted change is a small edit to a CI-related authentication token configuration, plus an additional credentials artifact present locally. These are operational items rather than benchmark logic changes, and they should be handled carefully to avoid accidental exposure or unintended commits.

Takeaways for benchmark owners#

• Adopt the expanded self-recognition evaluation structure: phased tests, explicit negative tests, and failure taxonomy tagging.
• Treat biometric self-recognition as a compliance-sensitive feature: jurisdiction routing and data minimization/retention planning should be part of benchmark readiness.
• Leverage NDC sharding and improved classification to keep benchmark artifacts discoverable and consistently tagged as the knowledge base grows.