αAGI ALPHATHE META-AGENTIC FRONTIER v1.22.0 / RESEARCH EDITION

THE COMPOUNDING LAB · FUTURE-TASK TRANSFER

Proof, carried
forward.

An invention matters when the next mission becomes better. Learn something once. Freeze it. Put it to work on a future it has never seen.

Begin the experiment ↗Read the 198-page manuscript ↗

Real local computation. Inspectable predictions. No account or API key.

FROM THE MANUSCRIPT
TO A FALSIFIABLE EXPERIMENT

What survives one mission
must earn its place in the next.

Training and future tasks are separate.
The policy makes the predictions.
Failure remains part of the record.

01 / DESIGN THE TRIAL

Make progress measurable.

Choose a future. The same learning process faces a different test. Every figure below is computed on your device.

Use your own series, inspect assumptions, or import a native run

Provide 20–64 integer training observations and 2–8 distinct future tasks. Each task has a calibration prefix and a held-out suffix. The learner only receives training data. Public fixtures are disclosed, not a secret benchmark.

Preparing your experiment…

02 / CAPABILITY PASSPORT

A policy, waiting
to be learned.

Forty observations from Mandate A. Ten candidate policies. No future-task answers.

Training commitment
Not frozen
Capability commitment
Not frozen
Prior learning cost
Charged in full to treatment

The frozen policy actually drives predictions. No expected-answer lookup.

03 / FUTURE TASKS

Does the advantage travel?

AWAITING EXECUTION
Gain before human review—Modeled units, after learning, validation and coordination
Tasks improved—Against the current-stack comparator
Actual Current stack With memory
Comparators · lower prediction error is better
Arm Total absolute error Forecast calls
Results will appear after execution.

B0: last value · B3: fixed trend · B5: calibration-only selection · B6: frozen memory. B1, B2 and B4 remain unmeasured. These are deterministic local baselines, not an LLM leaderboard.

Inspect every prediction and the cost ledger 
No run yet.

04 / REVIEW THE EVIDENCE

Your judgment.
An explicit record.

Inspect both arms. The timers record elapsed review time, not verified human attention. Review attaches to this exact run. Imported reviews stay in the docket; start fresh timers to record a new review.

Not recordedNot recorded

Acceptance cannot override a loss, excessive forecast error or a missing archive.

BOUNDED TRANSFER

HOLD

Execute and review the evidence before accepting a local claim.

Human review cost is pending.

MANUSCRIPT PROMOTION

HOLD Broader evidence remains open

Strongest-agent comparisons, independent validation, multi-agent scaling, calibrated α-WU and delayed real-world outcomes are still required.

Explore the implementation map ↗

EVIDENCE CONTACT INDEX

Awaiting execution

Local replay establishes reproducibility. Independent replay requires an independent process and reviewer.

  1. E0Simulated
  2. E1Probed
  3. E2Executed
  4. E3Independent replay
  5. E4Stressed
  6. E5External validation

05 / TAKE THE EVIDENCE WITH YOU

A complete record.
Even when it fails.

Thirteen manuscript sections. Raw tasks and predictions, baselines, cost and safety ledgers, capability, reviewer record, replay and calibration status. Each file has a SHA-256 checksum.

Replay it with the native agent 
alpha-agent transfer-verify run.json
alpha-agent transfer-verify evidence-docket.zip
alpha-agent transfer-run --spec spec.json --output run.json

The same inputs and frozen policy replay in Python and JavaScript. A matching hash is not a signature or proof of independence.

THE RESEARCH CONTINUES

Intelligence organizations.
Evidence before elevation.

This lab implements a bounded part of the 198-page AGI ALPHA: A Scalable Substrate for Intelligence Organizations manuscript. Explore the theory, the implementation map and the remaining proof obligations.

Read the latest manuscript ↗Open the field guide ↗Return to Proof Bloom ↗

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