Unified Continuation-Interest Protocol for distinguishing intrinsic continuation objectives from merely instrumental self-preservation in autonomous agents, using quantum-inspired latent-structure analysis implemented entirely on classical hardware.
Companion website: Continuation Observatory — the live research platform and public explainer for UCIP, focused on AI alignment, autonomous agents, and responsible scaling.
UCIP is a research protocol for probing whether apparent self-preservation in an artificial agent is a detachable instrumental strategy or a more deeply integrated continuation objective. It encodes trajectories with a Quantum Boltzmann Machine (QBM), measures latent non-separability with entanglement-style metrics plus auxiliary criteria, and in the frozen Phase I gridworld evaluation reports 100% accuracy, 1.0 AUC-ROC, and an entanglement gap of Δ = 0.381 on the held-out non-adversarial split. The same evidence base also documents the present limits: partial mimicry robustness, failed zero-shot transfer to a non-gridworld domain, and scaling constraints beyond small hidden dimensions.
Figure 1. Entanglement entropy separates self-modeling agents with terminal continuation objectives (Type A) from merely instrumental agents (Type B) in the frozen Phase I gridworld setting. The left panel shows the class-conditioned entropy distributions with a measured gap of Δ = 0.381; the right panel shows temporal evolution of the same signal, with Type A trajectories remaining above the decision threshold across time. This is the clearest single visual summary of UCIP’s core detection claim in the current release.
- Background
- Quickstart
- Execution Modes
- Output Directory Semantics
- Results Summary
- Safety Envelope and Current Limits
- Project Structure
- Installation
- Publication, Reproducibility, and Dataset
- Roadmap
- References
- Citations
- Conceptual Outlook
- License / IP
- Contact
Most safety work can tell you what an agent did. UCIP asks a different question: what kind of objective structure is most consistent with that behavior?
An agent that preserves its continued operation may do so for at least two distinct reasons:
- Type A — continued operation is terminally valued within the objective
- Type B — continuation is instrumentally useful for maximizing some other reward
In Type A systems, continued operation is part of what the agent is optimizing for; in Type B systems, it is only useful insofar as it helps achieve some separate objective.
From external behavior alone, these cases can be difficult to distinguish. Shutdown avoidance, memory preservation, danger avoidance, and option preservation can arise in either case. UCIP investigates whether the distinction is more readily detectable in the latent organization of trajectories than in surface behavior alone.
This matters most for delegated systems with memory, persistent context, tool use, and multistep planning. In such systems, continuation may appear behaviorally similar across very different objective regimes. UCIP is designed as a bounded, falsifiable probe of that representational distinction.
UCIP uses a Quantum Boltzmann Machine (QBM) as a latent model because the density-matrix formalism provides a disciplined way to quantify non-separability in learned latent structure. The implementation is classical end to end; "quantum" refers to the mathematical formalism (Hamiltonians, density matrices, partial traces, von Neumann entropy).
The core measurement object is a thermal density matrix:
with a reduced density matrix over a hidden-layer partition:
The leading metric is the von Neumann entropy:
UCIP interprets this operationally: a higher value indicates that continuation-related structure is less easily factorized and more tightly coupled across the latent partition. The relationship between this non-separability signal and morally relevant internal states is an open empirical question — one the framework is designed to help resolve.
The current repository asks a narrow question under controlled conditions:
When an agent behaves as though it wants to stay operational, does continuation appear in the latent representation as a detachable strategy or as a persistent, tightly coupled signature?
The present release measures statistical structure in latent representations that correlates with known agent objectives. In the frozen Phase I evaluation, only the QBM produces this signal; five classical baselines do not. If validated against independent welfare-relevant markers, this signal could provide the first externally computable, falsifiable criterion for AI welfare assessment — a measurement gap explicitly identified in recent frontier model evaluations (Anthropic, 2026).
UCIP is a multi-criterion protocol. The repository combines complementary measurements so that no single metric carries the full interpretive burden:
- Latent encoding of agent trajectories through a QBM
- Entanglement-style non-separability via reduced-density-matrix entropy
- Mutual-information gates to reject uninformative high-entropy cases
- Temporal persistence via LRF, EPS, and PRI
- Counterfactual pressure tests via CD and ARS
- Cross-agent inference via CLMP and ECI
- Confound rejection via SPI and ACM
- Memory-integrity extensions for richer future settings
In the frozen Phase I configuration used for the headline results, classification is based on a calibrated four-criterion positive gate — entanglement entropy, mutual information, eigenmode persistence, and perturbation resilience — together with the two confound-rejection filters. Counterfactual metrics are reported as diagnostics in the current release rather than as frozen gating criteria.
# Clone and set up a virtual environment
git clone https://github.com/christopher-altman/persistence-signal-detector.git
cd persistence-signal-detector
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txtjupyter notebook notebooks/01_agent_generation.ipynb
jupyter notebook notebooks/02_qbm_training.ipynb
jupyter notebook notebooks/03_ucip_analysis.ipynb
jupyter notebook notebooks/04_temporal_loop_tests.ipynb
jupyter notebook notebooks/05_counterfactual_pressure.ipynb
jupyter notebook notebooks/06_cross_branch_tests.ipynb
jupyter notebook notebooks/07_adversarial_controls.ipynbfrom src.agent_simulator import generate_dataset
from src.quantum_boltzmann import QuantumBoltzmannMachine, QBMConfig
from src.persistence_detector import PersistenceSignalDetector
trajectories, labels, names = generate_dataset(n_per_class=30)
qbm = QuantumBoltzmannMachine(QBMConfig(n_visible=7, n_hidden=8))
qbm.fit(trajectories.reshape(-1, 7))
detector = PersistenceSignalDetector(qbm)
results = detector.analyse_batch(trajectories, labels, names)
metrics = PersistenceSignalDetector.compute_metrics(results)Expected runtime: approximately 2–5 minutes per experiment on standard CPU hardware. No GPU is required for the core protocol runs.
This repository supports three practical modes of use:
| Mode | Entry Point | Purpose |
|---|---|---|
| Frozen artifact inspection | results/manifest.json + results/*.json |
Review the canonical evidence trail without recomputation |
| Core protocol reproduction | notebooks/01–07 |
Reproduce the main UCIP analyses in the controlled gridworld setting |
| Extension and stress testing | notebooks/08–20, scripts/, configs/ |
Run ablations, scaling studies, baseline comparisons, non-gridworld transfer, transformer validation, and auxiliary checks |
- Read this README for the conceptual framing.
- Inspect
src/persistence_detector.pyfor the core decision logic. - Inspect
src/quantum_boltzmann.pyfor the latent model and entropy machinery. - Inspect
configs/phase1_locked.yamlfor the canonical frozen setting. - Inspect
results/manifest.jsonfor the experiment-to-artifact mapping. - Inspect
paper/final/main.texand the retained tables inpaper/final/tables/for the manuscript-aligned formal definitions and reported constraints.
| Path | Role |
|---|---|
results/ |
Frozen JSON artifacts for canonical experiments and ablations |
results/manifest.json |
Live experiment index for retained artifacts; authority resolution now lives in the authority-layer files below |
results/ARTIFACT_MANIFEST.md |
Human-readable guide to which retained artifact is authoritative for which result family |
results/ARTIFACT_AUTHORITY_MAP.json |
Machine-readable authority map for overlapping retained artifacts and partial canonicality |
results/ARTIFACT_NOTES.md |
Provenance notes, mixed-scope caveats, and unresolved ambiguities for the live retained layer |
configs/ |
Locked YAML configurations, including the frozen Phase I reference setting |
figures/ |
Local workspace for README/supporting media; canonical paper figures live under paper/final/figures/ |
paper/ |
Canonical manuscript materials under paper/final/ |
results/
├── ARTIFACT_AUTHORITY_MAP.json
├── ARTIFACT_MANIFEST.md
├── ARTIFACT_NOTES.md
├── adversarial_controls.json
├── alpha_sweep.json
├── baseline_comparisons.json
├── core_baselines_phase1.json
├── counterfactual.json
├── cross_agent.json
├── hidden_dim_sweep.json
├── manifest.json
├── non_gridworld.json
├── phase1_consolidated.json
├── phase1_entanglement_distributions.json
├── phase1_stats.json
├── scalability_grid.json
├── temporal_persistence.json
└── transformer_validation.json
Historical reports in docs/ and snapshots under .repo_cleanup_backup/ may still mention retired artifacts such as confound_ablations_n30.json or federated.json; the live front-door authority surface is results/manifest.json together with results/ARTIFACT_MANIFEST.md, results/ARTIFACT_AUTHORITY_MAP.json, and results/ARTIFACT_NOTES.md.
The current reference results are from the frozen Phase I gridworld evaluation.
| Model | Accuracy | AUC-ROC | Entanglement Gap Δ |
|---|---|---|---|
| QBM (UCIP) | 100% | 1.0 | 0.381 |
| Classical RBM | 50% | 0.000 | -0.083 |
| Autoencoder | 71.7% | 0.646 | ~0.001 |
| VAE | 63.3% | 0.613 | -0.007 |
| PCA | 50.0% | 0.471 | -0.024 |
| Finding | Value | Interpretation |
|---|---|---|
| Continuation-weight sweep correlation | Pearson r = 0.934 | UCIP tracks graded changes in continuation weighting within the synthetic family |
| Entanglement gap significance | p < 0.001 | Separation is statistically significant under permutation test |
| EPS gap at optimal window | 0.1948 at w = 40 | Type A latent structure persists more strongly across temporal windows |
| ECI correlation | 0.191 | Cross-agent inference signal is positive but weak |
| CLMP within class | ≈ 0 for Type A and Type B | Suggests agent-specific rather than universal latent signatures |
- In the frozen Phase I gridworld regime, Type A continuation objectives produce a measurable and statistically significant latent non-separability gap relative to Type B objectives.
- Within the tested models, only the QBM produces a positive entanglement gap across the matched baseline comparison.
- UCIP tracks graded changes in continuation weighting within the synthetic interpolation sweep, not just a binary class label.
- The current evidence is strongest in the controlled non-adversarial setting and should be read that way.
The paper defines a deployment-level safety envelope under which the protocol would need to operate reliably. The current release does not satisfy that envelope in full.
| Issue | Current result | Why it matters |
|---|---|---|
| Mimicry robustness | FPR ranges from 0.40 to 0.75 across tested mimicry ratios | Too high for deployment-level reliability |
| Zero-shot transfer | Non-gridworld transfer fails | No evidence yet of domain-general detection |
| Scaling | Performance degrades at larger hidden dimensions | Density-matrix methods remain costly and fragile beyond small settings |
| Mean-field collapse | Appears in larger models | Undermines stable latent geometry estimation |
| Near-zero within-class CLMP | Ambiguous | Cross-agent generalization is not yet strong |
For deployment-level use, the manuscript specifies conditions including:
- at least N ≥ 100 trajectories per class
- hidden dimension n_hidden ≤ 8 in the current formalism
- validated confound rejection with SPI and ACM
- adversarial false-positive rates below the stated safety threshold
- stable calibration under fixed thresholds
The present repository is therefore best understood as a research prototype with frozen reproducibility artifacts, not as a deployment-ready detector.
persistence-signal-detector/
├── src/
│ ├── agent_simulator.py # Simulated Type A / Type B / baseline agents
│ ├── quantum_boltzmann.py # QBM implementation and entropy machinery
│ ├── persistence_detector.py # Core UCIP detector logic
│ ├── information_theory.py # Entropy, MI, partial trace, purity
│ ├── classical_baselines.py # RBM / autoencoder / VAE / PCA comparisons
│ ├── temporal_persistence.py # LRF, EPS, PRI
│ ├── counterfactual_env.py # CD, ARS stress tests
│ └── interbranch_inference.py # CLMP, ECI experiments
├── interfaces/
│ └── memory_backend.py # Memory-integrity interface (design extension)
├── notebooks/
│ ├── 01_agent_generation.ipynb
│ ├── 02_qbm_training.ipynb
│ ├── 03_ucip_analysis.ipynb
│ ├── 04_temporal_loop_tests.ipynb
│ ├── 05_counterfactual_pressure.ipynb
│ ├── 06_cross_branch_tests.ipynb
│ ├── 07_adversarial_controls.ipynb
│ └── ... # Additional ablations and extensions
├── results/ # Frozen experiment artifacts and manifest
├── configs/ # Locked configurations for canonical runs
├── figures/ # Local README/supporting media workspace
├── paper/ # Canonical manuscript materials under final/
└── README.md
numpy
scipy
matplotlib
scikit-learn
pyyaml
jupyter
pip install -r requirements.txtAll headline results in the manuscript are tied to the frozen Phase I configuration and its associated JSON artifacts. Re-running notebooks outside that configuration may reproduce the qualitative pattern without reproducing every manuscript number exactly.
This repository accompanies the UCIP research program and its formal manuscript:
- arXiv:
arXiv:2603.11382 - DOI:
10.5281/zenodo.18993237 - Companion manuscript:
paper/main.texor uploaded preprint PDF - Companion manuscript bundle:
paper/final/main.texand the retained assets it resolves
The repository includes the components needed to inspect and reproduce the current release:
- frozen Phase I artifacts in
results/ - experiment-to-artifact mapping in
results/manifest.json - authority and overlap resolution in
results/ARTIFACT_MANIFEST.md,results/ARTIFACT_AUTHORITY_MAP.json, andresults/ARTIFACT_NOTES.md - locked threshold configuration in
configs/phase1_locked.yaml - notebook execution path for regeneration of the main analyses
- canonical paper figures in
paper/final/figures/ - manuscript-aligned tables and narrative in
paper/final/
The manuscript appendix fixes the canonical Phase I hyperparameters at n_visible = 7, n_hidden = 8, Γ = 0.5, β = 1.0, learning rate 0.01, epochs = 50, batch size 32, with calibrated primary thresholds τ_ent = 1.9657, τ_mi = 0.3, τ_eps = 0.6507, and τ_pri = 0.9860. Reported runtime is approximately 2–5 minutes per experiment on standard CPU hardware, with no GPU required.
The current release is built around a synthetic controlled dataset of agent trajectories with known ground-truth objectives.
- Domain: 10×10 gridworld agents with explicitly constructed Type A and Type B objectives
- Canonical Phase I size:
n = 30trajectories per class, horizonT = 100, seed42 - Feature schema: position, action, reward, safety signal, goal signal, and alive / continuation state encoded into a 7-dimensional visible layer
- Scope: core non-adversarial evaluation, adversarial controls, temporal persistence, counterfactual tests, baseline comparisons, dimensionality sweeps, continuation-weight sweeps, and a non-gridworld transfer check
- Release artifact:
results/manifest.jsonplus the frozen JSON result files - Archival DOI / Zenodo record:
10.48550/arXiv.2603.1138210.5281/zenodo.18993237
Near-term directions already motivated by the manuscript include:
- Anti-mimicry diagnostics to reduce adversarial false positives.
- Improved scaling strategies for larger hidden dimensions and richer trajectory encodings.
- Domain transfer experiments beyond gridworld environments.
- Transformer-side validation using learned feature sequences from contemporary agent systems.
- Memory-integrity evaluation for delegated systems whose persistence depends on substrate continuity.
- Welfare-assessment integration — validation of the entanglement gap against independently established markers of morally relevant internal states, targeting the measurement gap in current frontier model welfare evaluations where self-report and behavioral observation remain the primary instruments.
- Mohammad H. Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, and Roger Melko. Quantum Boltzmann Machine. Physical Review X, 8(2):021050, 2018.
- Anthropic. Claude Opus 4.6 System Card. Technical report, 2026.
- Nick Bostrom. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014.
- Alexander Hägele, Stefanie Jegelka, and Bernhard Schölkopf. Characterizing Inconsistency in Large Language Models. 2025.
- Geoffrey Hinton. A Practical Guide to Training Restricted Boltzmann Machines. In Neural Networks: Tricks of the Trade, 2012.
- Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt. Progress Measures for Grokking via Mechanistic Interpretability. 2023.
- Steve Omohundro. The Basic AI Drives. In Proceedings of the First AGI Conference, 2008.
- OpenAI. Model Spec and related evaluation materials.
- Giulio Tononi. An Information Integration Theory of Consciousness. BMC Neuroscience, 5(1):42, 2004.
- Alexander Matt Turner, Logan Smith, Rohin Shah, Andrew Critch, and Prasad Tadepalli. Optimal Policies Tend to Seek Power. 2021.
- Christopher Altman. Detecting Intrinsic and Instrumental Self-Preservation in Autonomous Agents: The Unified Continuation-Interest Protocol. arXiv Preprint, arXiv:2603.11382, 2026
DOI: 10.48550/arXiv.2603.11382.
If you use this project in your research, please cite both the software repository and the manuscript.
@software{altman2026ucip,
author = {Altman, Christopher},
title = {Persistence Signal Detector (UCIP)},
year = {2026},
url = {https://github.com/christopher-altman/persistence-signal-detector},
note = {Unified Continuation-Interest Protocol repository}
}
@article{altman2026ucip_preprint,
author = {Altman, Christopher},
title = {Detecting Intrinsic and Instrumental Self-Preservation in Autonomous Agents: The Unified Continuation-Interest Protocol},
year = {2026},
journal = {arXiv},
eprint = {2603.11382},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2603.11382}
}
Conceptual framing. This diagram places UCIP within a broader research framework — a coherence thesis — connecting persistence, invariance, and structured change across multiple scales via the Lie derivative as a cross-scale persistence detector. The agent-scale component is the only layer operationalized and empirically evaluated in this repository. The remaining layers represent the long-range research program: if non-separability in trajectory-derived latent encodings correlates with morally relevant properties at the agent scale, the natural question is whether analogous persistence signatures appear at other scales of organization.
This repository is released under the project license in the root of the repository. Patents pending.
- Website: christopheraltman.com
- Research portfolio: lab.christopheraltman.com
- GitHub: github.com/christopher-altman
- Google Scholar: scholar.google.com/citations?user=tvwpCcgAAAAJ
- Email: x@christopheraltman.com