How does SaSame improve its own evolution method?
The evolution loop turns one-off execution into compounding learning without allowing self-modification to override authority or honesty.
- Collection
- research
- Updated
- 2026-08-13
- min read
- 4
SaSame evolves by converting both successful execution and operational residue into knowledge: CP5 makes cleanup, dependency hygiene and test drift part of the same inherited learning loop.
- Observed
- 2026-08-13 10:45 UTC
- Claim strength
- illustrative
- Dataset scope
- This is a contract-level explanation of SaSame evolution mechanics, backed by repository tests rather than a field-result dataset.
- Methodology
- The model decomposes Mission outcomes into traits, applies validity horizons, returns stale knowledge to Research and measures velocity as information gain per time, cost and reality exposure.
- Limitations
- The page does not claim that self-evolution is fully autonomous in production. It defines the boundary and verification contract for incremental implementation.
Illustrative — describes a method or schema, not a specific measured result
Variation
Research creates hypotheses by strengthening current ideas, challenging them, explaining anomalies, proposing novel alternatives, recombining past knowledge and adding bounded randomness.
Simulation
Simulation compresses time: analytical filters, historical replay, scenario tests, Monte Carlo, agent simulation and counterfactuals reduce many candidates to a few reality-safe Missions.
Knowledge Genome
Mission closeout records traits, confidence, validity horizon, environment signature, contradictory evidence and revalidation triggers. Success and failure both become inheritable information.
Evolution velocity
Velocity increases with trials, information gain, reality agreement, retained knowledge and reuse. It decreases with time, cost, reality experiment cost and duplicated reasoning.
CP2 heredity and recombination
The CP2 projection decomposes replayable Mission outcomes into trait-level inheritance records with provenance, confidence, confounders, validity state and eligibility. Recipient-specific evidence is retained, long-form copy remains inconclusive, generic outreach is blocked, and the recombined candidate remains CANDIDATE_ONLY rather than truth or execution.
CP5 hygiene as inherited learning
Executor hygiene is part of meta-evolution because unfinished branches, stale tests, parked production gaps and dependency PRs are signals about the institution itself. CP5 records them without false closure, prevents destructive cleanup of dirty worktrees, and routes unresolved runtime work back into Mission Control instead of letting it accumulate outside the loop.
Examples
4- 01
A negative generic-outreach trait suppresses future generic outreach even if the overall Mission produced one useful finding.
- 02
A simulation strategy that repeatedly mispredicts reality is itself changed by meta-evolution.
- 03
A recombined CP2 candidate can use recipient-specific evidence plus receipt-required fulfillment, but generic outreach and inconclusive long-form copy cannot silently enter a Recipe, Rule or next-Mission fast path.
- 04
A Dependabot PR remains an external dependency lane, while a production service-monitor blocker remains a scheduled recheck until live VPS evidence proves the gap closed.