B07 · Learning Architecture · Live Demo

The learning architecture exists. The missing seam is feeding it truthful pre-outcome predictions and production outcomes.

This console runs the canonical chain end to end. You seal a forecast before execution, run the mission, and watch the outcome either pair into calibration or get refused as hindsight. Only paired observations calibrate. Nothing here learns silently: every behavior change passes a promotion court and a governance decision, in the open, with receipts.

IN-BROWSER SIMULATION OF THE SPECIFIED ARCHITECTURE · NO LIVE SYSTEMS CONNECTED · STATE RESETS ON RELOAD
Economic north star · the first proof
forecast→ bounded action→ real outcome→ OutcomeRecord→ calibration pair→ learning proposal→ governed promotion→ better next mission
Console 01

Run the learning loop

One mission, full chain: neuruh.outcome-record.v1 → Outcome Calibration Ledger → Learning Update Proposal → Promotion Gate → Governance → promoted behavior. Seal the forecast first. The hindsight button is there so you can watch the starvation defect fire.

0
Calibration pairs
·
Mean Brier score
0
Unpaired outcomes (refused)
v1.0.0
Promoted behavior version

Canonical chain

IDLE

Mission control

no mission loaded
72%

Demo court rule (thresholds chosen for this console; the spec itself requires paired pre-outcome forecasts, an independent court, and a governance decision, and leaves the numbers to the implementation): a proposal passes with at least 3 calibration pairs behind it, mean Brier at or under 0.25, and no governance-boundary item inside it. Failing proposals return as evidence, not as silent edits.

Receipt log

Calibration: predicted vs observed

NO PAIRS YET
Stated confidence at seal time Observed outcome value (1 / 0.5 / 0) Perfect calibration reference

Each bar pair is one sealed forecast matched to its observed outcome. The gap between the two bars is the calibration error the ledger records. Hindsight outcomes never appear here: there is nothing truthful to plot.

What the pairs let you measure

MeasureLive value from this session
Recipe calibrationawaiting pairs
Confidence calibration (stated vs observed)awaiting pairs
False positive rate (predicted success, observed failure)awaiting pairs
False negative rate (predicted failure, observed success)awaiting pairs
Mean time-to-outcomeawaiting pairs
Mean cost vs valueawaiting pairs
Failure concentration (top archetype)awaiting pairs
Feed layer

Adapter families A0 to A7

The Sep-5 court mapped roughly 14 outcome producers and required no new outcome schema. These adapters are the bridges. Toggle A3 off and try to seal a forecast: the starvation defect is what a missing bridge feels like from inside the loop.

Sep-7 protocol

Topology-to-outcome experiment bench

Does changing the computational organization around the same mission change measured results? Run the comparison protocol: same mission class, five organizations, repeated trials. One successful run proves nothing; this bench refuses to crown a winner under 10 trials per variant.

Pre-outcome record is captured for every trial before execution: archetype, success condition, evidence, prediction, confidence, expected cost and latency, provider, recipe, topology, tools, alternatives, unknowns.
VariantQualityCalib. errorCompletionInterventionsCost / runLatencyPolicy failsEcon. value
No trials run yet. Post-outcome record per trial captures actual outcome, time-to-outcome, value, cost, errors, reversals, human interventions, FP/FN class, causal caveats, acceptance, and a promote / demote / retest recommendation.

Research maturity ladder

RUNG 1

Governance boundary

Automatic learning may propose the left column on its own. The right column stays a versioned proposal with tests and an independent court. Try forcing one:

May propose automatically
  • OKRouting weights
  • OKRecipe calibration factors
  • OKRetry / backoff policy
  • OKContext selection
  • OKNew tests
May never silently rewrite
  • NOGovernance rules
  • NOPrivileged capability
  • NODestructive authority
  • NOPublic exposure
  • NOLegal / financial commitments
  • NOCanonical mutation semantics
Second loop · Sep-7

Market-demand learning

OutcomeRecord teaches whether executed work was correct. Demand telemetry teaches what buyers repeatedly ask for that the organism cannot yet serve. Submit demand signals; unmatched demand becomes a MISSING_INGREDIENT, gets ranked by RICHIE, and repeated pull commissions a build instead of another idea backlog.

Intake signal

PRIVACY-SAFE FIELDS ONLY

No raw sensitive prospect text is retained for learning: problem class, requested outcome, buyer class, match state, missing ingredient, band, conversion state, and reason lost. That is the whole record.

Missing ingredients

REGISTRY: 2 OFFERS

Existing offer registry: Property Opportunity Scan Property Intelligence Dossier

No unserved demand yet. Submit a signal the registry cannot match.

Demand maturity ladder

Searches, questions, likes and "that would be cool" are weak demand. Productization rises with this ladder, not with applause.

Sep-7 · Reasoning compilation

Successful intelligence should get cheaper

Repeated successful reasoning compiles downward into cheaper logic. Run the same classification task repeatedly: each clean run is evidence. At the thresholds, promote the pattern down a rung. A novel or ambiguous case routes straight back to frontier reasoning. Ambiguity is never compiled away.

Task: classify parcel tax status from county evidence packet
clean runs at current rung: 0 · total saved vs frontier: $0.00

Objective: lowest reliable cost for the required evidence quality and consequence level, not minimum token spend. Independent court and outcome calibration stay necessary at every rung; cost optimization never outranks truth.

Walkthrough

Presenter script

Six moves, about four minutes. Every claim below is something the console actually does on screen.

  1. Seal a forecast."Before anything executes, the prediction is locked: class, confidence, cost, latency, recipe, topology, known unknowns. The seal hash is the receipt. This is the input the whole architecture was starving for."
  2. Execute, then hit hindsight."Run the mission and the outcome pairs. Now run one without a forecast. Same outcome quality, and calibration refuses it. Learning from hindsight is narrative, not measurement."
  3. Build three pairs and read the chart."Stated confidence against observed outcomes. The gap is calibration error, per recipe, per source, per topology. That gap is what gets governed."
  4. Draft the proposal, convene the court."Learning proposes; it does not edit. The court checks pairs, Brier score, and boundary. Governance promotes a version, or rejects it, with the reason in the log."
  5. Run the topology bench at 1 trial, then 12."At one trial the bench refuses to declare a winner. At twelve, differences become evidence. That refusal is the difference between a research program and a success story."
  6. Feed it unserved demand, then compile a pattern down."The second loop: buyers pull the build queue through MISSING_INGREDIENT and RICHIE ranking. And every repeated success gets cheaper, rung by rung, without ever compiling ambiguity away."