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ENTRO-EVO (E-LAB-05) FINAL SUMMARY
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Project: ENTRO-EVO - Adaptive Entropy Weighting
Version: 1.0.0 (11 sub-versions)
Date: 2026-04-08
DOI: 10.5281/zenodo.19464489

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ACHIEVEMENTS
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✅ 11 working versions from v1.0 to v11.0
✅ Best PSI achieved: 0.075 (v11.0)
✅ Best weight balance: [0.33, 0.33, 0.34] (v7.0)
✅ Best compromise: [0.38, 0.22, 0.40], PSI=0.25 (v5.1)
✅ Zero budget violations in multiple versions
✅ Successful phase transitions (stabilize/control/explore)
✅ Temperature annealing working

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LIMITATIONS
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❌ Weight collapse in late versions
❌ K death in some versions
❌ No formal convergence proof
❌ Spike handling still heuristic

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SCIENTIFIC CONTRIBUTION
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This research demonstrates that entropy-driven adaptive systems can self-organize
under thermodynamic constraints, achieving approximate convergence despite strong
stochastic perturbations. The system behaves not as a classical optimizer, but
as a controlled chaotic annealing engine.

The fundamental trade-off discovered:
  PSI minimization ←→ Weight balance
appears to be a intrinsic property of entropy-controlled systems.

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NEXT STEPS
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1. v12.0: Combine temperature dynamics + anti-collapse
2. Formal entropy definition (Shannon/free-energy)
3. Spike prediction layer
4. Continuous phase field (not hard switches)

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REFERENCES
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ENTROPIA (E-LAB-01): 10.5281/zenodo.19416737
ENTRO-AI (E-LAB-02): 10.5281/zenodo.19284086
ENTRO-CORE (E-LAB-03): 10.5281/zenodo.19431029
ENTRO-ENGINE (E-LAB-04): 10.5281/zenodo.19441032
ENTRO-EVO (E-LAB-05): 10.5281/zenodo.19464489

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"A single engine regulates itself. A fleet of engines requires a coordinator.
But an intelligent engine learns the physics of its own environment."

— Samir Baladi, April 2026
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