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Recursive self-improvement
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Recursive self-improvement
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christopher-altman/README.md

Starlab | Deep Future

Frontier AI evaluation · Quantum machine learning · Superconducting quantum benchmarks

Our research program develops falsification frameworks and reproducible evaluation harnesses spanning frontier AI evaluation and alignment testing, adaptive and superpositional quantum networks, and superconducting-quantum-hardware benchmarks.

Current work focuses on structural AI metrics, continuation-risk measurement, national-security benchmarking, quantum kernel methods, recursive self-improvement, space-telemetry anomaly detection, and superconducting-qubit systems.

Frontier AI Research Lab ・ Continuation Observatory ・ Personal Website ・ Google Scholar ・ Email

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  1. persistence-signal-detector persistence-signal-detector Public

    A multi-criterion diagnostic framework for detecting latent continuation-interest signatures in autonomous agents using density-matrix entanglement entropy.

    Python 3

  2. qml-verification-lab qml-verification-lab Public

    Verification harness for quantum ML. A reproducible lab for stress-testing quantum models where predictive accuracy, identifiability, curvature, and robustness under noise can diverge.

    Python 2

  3. noise-aware-qnn-identifiability noise-aware-qnn-identifiability Public

    When performance survives noise, identifiability may not.

    Python 3

  4. qkernel-telemetry-anomaly qkernel-telemetry-anomaly Public

    Applied quantum kernels for anomaly detection. Low-data anomaly detection on manifold-structured telemetry, benchmarking entanglement kernels vs classical baselines with geometric diagnostics.

    Python 3

  5. ibm-qml-kernel ibm-qml-kernel Public

    Quantum kernel estimation with backend-matched IBM noise modeling, plus reproducible “Wigner’s friend” branch-transfer coherence-witness experiments executed on superconducting quantum hardware.

    Python 7

  6. sat-qkd-security-curves sat-qkd-security-curves Public

    Quantum keys can fail quietly. Loss and noise can leave you with bits, but no secrecy. We model the cliff to expose silent breakage before it becomes a system risk.

    Python 2