# Self Assessment — 2026-03-29 10:03 UTC

Overall score: 0.50

```json
{
  "dimensions": [
    {
      "dimension": "reflection_quality",
      "score": 0.1,
      "evidence": "Critical instrumentation gap: reflection_quality reports total_observations=0, retrieved_count=0, influenced_count=0 \u2014 yet learning_velocity shows 200 observations created this week. These two subsystems are tracking different things or retrieval telemetry is simply broken. Either way, zero retrieval events and zero influenced-action events is the worst possible outcome for a reflection system: observations are being created but there is no evidence any of them have ever been fetched or shaped a decision. Cannot distinguish 'retrieval tracking is unimplemented' from 'reflections are genuinely ignored by the runtime' without further instrumentation. Both hypotheses are damning.",
      "data_available": true
    },
    {
      "dimension": "procedure_effectiveness",
      "score": 0.9,
      "evidence": "7 active procedures, avg_success_rate=1.0 (100%), zero low performers, zero quarantine candidates. This is the strongest signal in the dataset. Haircut of 0.1 for the possibility that 100% success on a small corpus of 7 procedures reflects under-exercise rather than genuine robustness \u2014 edge cases and failure modes may not yet have been encountered.",
      "data_available": true
    },
    {
      "dimension": "outreach_calibration",
      "score": 0.25,
      "evidence": "29 total outreach events: 18 ignored (62%), 6 acknowledged (21%), 3 useful (10%), 2 ambivalent (7%). Positive-signal rate (acknowledged + useful) is 31%. The 62% ignore rate is the dominant story and it is poor. Category breakdown is partially illuminating: digest carries 8 of the 11 categorized messages with 5 acknowledged + 2 ambivalent + 1 useful \u2014 digest is the most active channel and performs reasonably when it lands. But 18 ignored messages are entirely absent from the category breakdown, suggesting they may belong to uncategorized or lower-signal categories. Blockers (1 useful/1 total) and surplus (1 useful/1 total) perform well in small samples. The early-V3 caveat applies, but 29 data points are enough to flag the ignore rate as a calibration problem, not noise.",
      "data_available": true
    },
    {
      "dimension": "learning_velocity",
      "score": 0.55,
      "evidence": "200 observations created this week vs 0 last week. The 0-baseline makes trend analysis meaningless \u2014 this is either week-one data or a prior-week outage, not a trend. Raw volume of 200 is healthy. However: no procedure extraction count is provided for either week, so the conversion from observations to durable procedures cannot be assessed. Missing half the metric set. Scoring 0.55 for strong observation volume with an incomplete signal.",
      "data_available": true
    },
    {
      "dimension": "resource_efficiency",
      "score": 0.5,
      "evidence": "Surplus staging: 99 items reviewed (42 promoted + 57 discarded), promotion rate = 42.4%. A near-even split is reasonable \u2014 the curation bar is functioning. 100 items remain pending, which may indicate a backlog forming if intake continues at current rate. Cost budget utilization: no data provided. Idle compute utilization: no data provided. Scoring 0.5 because the one available metric (promotion rate) is acceptable but half the dimension is blind.",
      "data_available": true
    },
    {
      "dimension": "blind_spots",
      "score": 0.3,
      "evidence": "Topic distribution is heavily concentrated: awareness_tick (53, 26.5%), user_model_delta (23, 11.5%), light_reflection (22, 11%), reflection_summary (21, 10.5%), reflection_output (19, 9.5%) \u2014 these five meta-observation categories account for ~70% of all 200 observations. The observation stream is largely Genesis watching itself watch itself. External-world coverage is thin: github_releases=3, cc_update=2, anomaly=7, infrastructure=1. Drive coverage is weak across the board: preservation drive (infrastructure=1, escalations=2 = 1.5%), curiosity drive (recon/releases ~3.5%), cooperation drive (not identifiable as a category \u2014 0%), competence drive (code_audit=5, capability_improvement=1 = 3%). The cooperation drive has no representation whatsoever. The dominant recency-bias pattern is reflexive self-monitoring rather than world-directed attention.",
      "data_available": true
    }
  ],
  "overall_score": 0.5,
  "observations": [
    "Reflection instrumentation is broken or retrieval is never being called: 200 observations created, 0 retrieved, 0 influenced. This is the highest-priority structural defect \u2014 a reflection system that never influences action is just a write-only log.",
    "Outreach ignore rate of 62% indicates miscalibration in either timing, content, or channel selection. The 18 unclassified ignored messages are a ghost fleet \u2014 we don't know what they are or why they failed.",
    "The cooperation drive has zero representation in the observation corpus. Genesis is not forming or tracking observations about its relationship with the user, collaboration patterns, or joint goals.",
    "Procedure effectiveness is the one unambiguous bright spot: 7 procedures at 100% success, no quarantine candidates. The learned-behavior layer is working as designed.",
    "Learning velocity baseline is unassessable: last week's 0 observations makes this either a first-week measurement or a prior outage. Neither interpretation supports confident trend analysis.",
    "The observation stream is dominated by meta-observations (reflections about reflections, awareness ticks, reflection outputs) \u2014 ~70% of all observations are Genesis monitoring its own internal state rather than the external world or user relationship."
  ],
  "recommendations": [
    "Fix retrieval telemetry immediately: instrument observation fetch events and add influenced_action tracking to the reflection pipeline. Until retrieved_count > 0 is confirmed, assume the reflection loop is broken end-to-end.",
    "Audit the 18 uncategorized ignored outreach messages: determine their category, content, and send time. If they are digest duplicates or low-signal alerts, tighten the send threshold before those categories. If they are a new message type, add it to the category taxonomy.",
    "Add a cooperation-drive observation category and wire at least one observation source to it \u2014 user interaction quality, joint-task completion, user correction events. A drive with zero observations is effectively disabled.",
    "Establish a weekly procedure extraction target: learning_velocity should report new_procedures_this_week alongside observations. Without this, the conversion from insight to durable behavior is invisible.",
    "Set a topic-diversity floor: awareness_tick should be capped as a fraction of weekly observations (suggest 15% max). Surplus observation budget should be redirected toward external-world categories (recon findings, anomaly patterns, external events).",
    "Investigate the surplus pending backlog of 100: if intake rate exceeds review rate, the backlog will grow unboundedly. Either increase review throughput or tighten the intake filter."
  ]
}
```
