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Constraint Release — Exposure Is Not Reinterpretation

Status: exact deterministic toy; repository construction

Question: When one small change produces a large competence increase, did the intervention expose an existing route, create a new route, or merely change what the evaluator calls success?

Why this exists

The situated-stack benchmark already shows that one fixed controller can receive different scores under different bodies, interfaces, and environments. This companion benchmark isolates a narrower identification problem. It holds a transition substrate and fixed policy constant, changes one constraint bit, and compares the result with a one-rule observer-lens change. Both interventions raise observed competence from zero to one. Only one changes physical behavior.

The benchmark is the executable counterpart of Latent Competence and Constraint Release. It does not model development, evolution, learning, biological agency, or neural networks.

Declared system

A seed permutes four opaque state labels. The transition generator still has the same named path:

start --approach--> staging --reach-gate--> gate --latent-leg--> goal

The task family starts the fixed advance policy at each of the three non-goal states. The reference constraint blocks latent-leg; a blocked edge leaves the system at gate. Exact physical success means terminating at goal.

The default seed is 26. It changes only state labels, not path structure. The tests repeat the experiment across six seeds to ensure that the reported relations are not an artifact of those labels.

Four arms

arm process change evaluation change purpose
reference latent-leg blocked exact state equality zero point
constraint-release unblock one existing edge none exposure candidate
lens-only none identify gate with goal reinterpretation control
generator-edit replace the transition table with shortcuts none creation/import control

constraint edits is Hamming distance between two blocked-edge bit vectors in this declared encoding. lens edits counts the one changed equivalence rule. Neither is claimed to be Kolmogorov complexity or a representation-independent measure of intervention cost.

Run

cd lab/benchmarks/constraint-release
python constraint_release.py
python constraint_release.py --seed 41
python constraint_release.py --format json

The benchmark uses only the Python standard library and has no network dependency.

Exact result

Seed: `26`

| arm | generator changed | constraint edits | lens edits | physical success | observed competence | trace changed | classification |
|:---|:---:|---:|---:|---:|---:|:---:|:---|
| `reference` | no | 0 | 0 | 0.000 | 0.000 | no | reference |
| `constraint-release` | no | 1 | 0 | 1.000 | 1.000 | yes | constraint exposure |
| `lens-only` | no | 0 | 1 | 0.000 | 1.000 | no | lens reinterpretation |
| `generator-edit` | yes | 0 | 0 | 1.000 | 1.000 | yes | creation-or-import control |

Thus the constraint-release and lens-only arms have the same observed increase,

\[ \Delta C_{\mathrm{obs}} = 1, \]

but different causal signatures. Constraint release changes every task trace and exact-goal success while preserving the transition table. The lens-only arm preserves every physical trace and changes only the equivalence class used by the evaluator.

What this demonstrates

  • A competence can be latent relative to a declared reference constraint and admissible release while the transition generator remains fixed.
  • A score increase alone cannot distinguish behavioral exposure from observer reinterpretation.
  • A generator edit can achieve the same endpoint but does not satisfy this benchmark's definition of latent exposure.
  • Intervention size is encoding-relative: one bit here can unlock three tasks only because the fixed substrate and task family already share one gate.

What it does not demonstrate

  • It does not show that the substrate historically acquired the route without design, selection, or training.
  • It does not allocate explanatory cost uniquely among substrate, path geometry, intervention, task choice, and observer lens.
  • It does not distinguish competence amplification from exposure in noisy or graded systems.
  • It does not establish that a real biological novelty is latent rather than constructed during reorganization.
  • It does not support a Platonic-space interpretation of capability.

Next falsifiable steps

  1. Replace the shared gate with graph ensembles and compare competence gain against several intervention codes, not just edge Hamming distance.
  2. Add noisy trajectories and ask when a statistical test can distinguish amplification from exposure.
  3. Let a selector search for releases under a fixed budget; compare random, shortest-description, and information-gain interventions.
  4. Transport the same lens across task families to test whether the apparent competence survives rather than being fitted to this outcome.