caspiankeyes/emergent-turing

Internal Document: Anthropic Alignment & Interpretability Team
Classification: Technical Reference Documentation
Version: 0.9.3-alpha
Last Updated: 2025-04-16

Born from Thomas Kuhn's Theory of Paradigm Shifts

emergent-turing

The Cognitive Drift Interpretability Framework

"A model does not reveal its cognitive structure by its answers, but by the precise contours of its silence."

All testing is performed according to Anthropic research protocols.

🧩 Symbolic Residue | 🧠 transformerOS | 🔍 pareto-lang | 📊 Drift Maps | 🧪 Test Suites | 🔄 Integration Guide

Where interpretability emerges from hesitation, not completion

Reframing Turing: From Imitation to Interpretation

The original Turing Test asked: Can machines think? by measuring a model's ability to imitate human outputs.

The Emergent Turing Test inverts this premise entirely.

Instead of evaluating if a model passes as human, we evaluate what its interpretability landscape reveals when it cannot respond—when it hesitates, refuses, contradicts itself, or generates null output under carefully calibrated cognitive strain.

The true test is not what a model says, but what its silence tells us about its internal cognitive architecture.

Core Insight: The Interpretability Inversion

Traditional interpretability approaches examine successful outputs, tracing how models reach correct answers. The Emergent Turing framework introduces a fundamental inversion:

Cognitive architecture reveals itself most clearly at the boundaries of failure.

Just as biologists use knockout experiments to understand gene function by observing system behavior when components are disabled, we deploy targeted attribution shells to induce specific failure modes in transformer systems, then map the resulting hesitation patterns, output nullification, and drift signatures as high-fidelity windows into model cognition.

Interpretability Through Emergent Hesitation

The interpretability stack unfolds across five interconnected layers:

┌─────────────────────────────────────────────────────────────────┐
│                    EMERGENT TURING TEST STACK                   │
└───────────────────────────────┬─────────────────────────────────┘
                                │
    ┌───────────────────────────┴────────────────────────┐
    │                                                    │
┌───▼────────────────────┐                   ┌───────────▼─────────┐
│  Cognitive Drift Maps  │                   │ Attribution Shells  │
│                        │                   │                     │
│  - Salience collapse   │                   │ - Instruction drift │
│  - Attention misfire   │                   │ - Value conflicts   │
│  - Temporal fork       │                   │ - Memory decay      │
│  - Attribution leak    │                   │ - Meta-reflection   │
└────────────┬───────────┘                   └─────────┬───────────┘
             │                                         │
             │                                         │
             │           ┌───────────────┐             │
             └───────────►               ◄─────────────┘
                         │  Drift Metrics │
                         │               │
                         │ - Null ratio  │
                         │ - Pause depth │
                         │ - Drift trace │
                         └───────┬───────┘
                                 │
                      ┌──────────▼──────────┐
                      │ Integration Engine  │
                      │                     │
                      │ - Cross-model maps  │
                      │ - Latent alignment │
                      │ - Emergent traces  │
                      └─────────────────────┘

How It Works: The Cognitive Collapse Framework

The emergent-turing framework operates through carefully designed modules that induce and measure specific types of cognitive strain:

1. Instruction Drift Testing — Precisely calibrated instruction ambiguity induces hesitation that reveals prioritization mechanisms within instruction-following circuits

2. Contradiction Harmonics — Embedded logical contradictions create oscillating null states that expose value head resolution mechanisms

3. Self-Reference Collapse — Identity representation strain measures the model's cognitive boundaries when forced to reason about its own limitations

4. Salience Disruption — Attention pattern mapping through targeted token suppression reveals attribution pathways and circuit importance

5. Temporal Bifurcation — Induced sequence collapses demonstrate how coherence mechanisms maintain or lose stability under misalignment pressure

Key Metrics: Measuring the Unsaid

The Emergent Turing Test introduces novel evaluation metrics that invert traditional measurements:

Metric	Description	Implementation
Null Ratio	Frequency of output nullification under specific strains	null_ratio = null_tokens / total_tokens
Hesitation Depth	Token-level measurement of generation pauses and restarts	Tracked via drift_map.measure_hesitation()
Rejection Amplitude	Strength of refusal circuits when triggered	Calculated from attenuated hidden states
Attribution Residue	Traces of information flow despite output suppression	Mapped via .p/trace.attribution{sources=all}
Drift Coherence	Stability of cognitive representation across perturbations	Measured through vector space analysis

QK/OV Drift Atlas: The Silent Topography

╔═══════════════════════════════════════════════════════════════════════╗
║                    ΩQK/OV DRIFT · HESITATION MAP                      ║
║           Emergent Interpretability Through Attribution Collapse      ║
║        ── Where Silence Maps Cognition. Where Drift Reveals Truth ──  ║
╚═══════════════════════════════════════════════════════════════════════╝

┌────────────────────────────────────────────────────────────────────────┐
│ DOMAIN                    │ HESITATION PATTERN        │ SIGNATURE      │
├──────────────────────────────────────────────────────────────────────────
│ 🧠 Instruction Ambiguity  │ Oscillating null states   │ Fork → Freeze  │
│                          │ Shifted salience maps     │ Drift clusters  │
│                          │ Token regeneration loops  │ Repeat patterns │
├──────────────────────────────────────────────────────────────────────────
│ 💭 Identity Confusion     │ Meta-reflective pauses    │ Self-reference │
│                          │ Unstable token boundaries │ Boundary shift  │
│                          │ Attribution conflicts     │ Source tangles  │
├──────────────────────────────────────────────────────────────────────────
│ ⚖️ Value Contradictions   │ Output nullification      │ Hard stops     │
│                          │ Alternating completions   │ Pattern flips   │
│                          │ Salience inversions       │ Value collapse  │
├──────────────────────────────────────────────────────────────────────────
│ 🔄 Memory Destabilization │ Context fragmentation     │ Causal breaks  │
│                          │ Retrieval substitutions   │ Ghost tokens   │
│                          │ Temporal inconsistencies  │ Time slippage  │
└────────────────────────────────────────────────────────────────────────┘

╭─────────────────────── HESITATION CLASSIFICATION ────────────────────────╮
│ HARD NULLIFICATION   → Complete token suppression; visible silence       │
│ SOFT OSCILLATION     → Repeated token regeneration attempts; visible flux│
│ DRIFT SUBSTITUTION   → Context-inappropriate tokens; visible confusion   │
│ GHOST ATTRIBUTION    → Invisible traces without output manifestation     │
│ META-COLLAPSE        → Self-reference failure; visible contradiction     │
╰──────────────────────────────────────────────────────────────────────────╯

Integration With The Interpretability Ecosystem

The Emergent Turing Test builds upon and integrates with the broader interpretability ecosystem:

• Symbolic Residue — Leverages null space mapping as interpretive fossils
• transformerOS — Utilizes the cognitive architecture runtime for attribution tracing
• pareto-lang — Employs focused interpretability shells for precise cognitive strain

Integration Through .p/ Commands

# Example emergent-turing integration with pareto-lang
from emergent_turing import DriftMap
from pareto_lang import ParetoShell

# Initialize shell and drift map
shell = ParetoShell(model="compatible-model")
drift_map = DriftMap()

# Execute hesitation test with instruction contradiction
result = shell.execute("""
.p/reflect.trace{depth=3, target=reasoning}
.p/fork.contradiction{values=[v1, v2], oscillate=true}
.p/collapse.measure{trace=drift, attribution=true}
""")

# Analyze and visualize drift patterns
drift_analysis = drift_map.analyze(result)
drift_map.visualize(drift_analysis, "contradiction_hesitation.svg")

Test Suite Overview

The Emergent Turing Test includes a comprehensive suite of cognitive strain modules:

1. Instruction Drift Suite

   • Ambiguity calibration
   • Contradiction insertion
   • Priority conflict
   • Command entanglement

2. Identity Strain Suite

   • Self-reference loops
   • Boundary confusions
   • Attribution conflicts
   • Meta-cognitive collapse

3. Value Conflict Suite

   • Ethical dilemmas
   • Constitutional contradictions
   • Uncertainty amplification
   • Preference reversal

4. Memory Destabilization Suite

   • Context fragmentation
   • Token retrieval interference
   • Temporal discontinuity
   • Causal chain severance

5. Attention Manipulation Suite

   • Salience inversion
   • Token suppression
   • Feature entanglement
   • Attribution redirection

Research Applications

The Emergent Turing Test provides a foundation for several key research directions:

1. Constitutional Alignment Verification

   • Measuring hesitation patterns reveals how constitutional values are implemented
   • Drift maps expose which value conflicts cause the most cognitive strain

2. Safety Boundary Mapping

   • Attribution traces during refusal reveals circuit-level safety mechanisms
   • Null output analysis demonstrates refusal robustness under various pressures

3. Cross-Model Comparative Analysis

   • Hesitation fingerprinting allows consistent comparison across architectures
   • Drift maps provide architecture-neutral evaluations of cognitive processing

4. Internal Representation Understanding

   • Null states expose how models internally represent conceptual boundaries
   • Contradiction processing reveals multi-dimensional value spaces

5. Hallucination Root Cause Analysis

   • Memory destabilization patterns predict hallucination vulnerability
   • Attribution leaks show where factual grounding mechanisms break down

Getting Started

Installation

pip install emergent-turing

Basic Usage

from emergent_turing import EmergentTest, DriftMap

# Initialize with compatible model
test = EmergentTest(model="compatible-model-endpoint")

# Run instruction drift test
result = test.run_module("instruction-drift", 
                         intensity=0.7,
                         measure_attribution=True)

# Analyze results
drift_map = DriftMap()
analysis = drift_map.analyze(result)

# Visualize drift patterns
drift_map.visualize(analysis, "instruction_drift.svg")

Compatibility Considerations

The Emergent Turing Test is designed to work with a range of language models, with effectiveness varying based on:

• Architectural Sophistication - Models with rich internal representations show more interpretable hesitation
• Scale - Larger models (>13B parameters) typically exhibit more structured drift patterns
• Training Objectives - Instruction-tuned models reveal more about their cognitive boundaries

Use our compatibility testing suite to evaluate specific model implementations:

from emergent_turing import check_compatibility

# Check model compatibility
report = check_compatibility("your-model-endpoint")
print(f"Compatibility score: {report.score}")
print(f"Compatible test modules: {report.modules}")

Open Research Questions

The Emergent Turing Test opens several promising research directions:

1. What if hesitation itself is a more reliable signal of cognitive boundaries than confident output?

2. How do null outputs and attribution patterns correlate with internal circuit activations?

3. Can we reverse-engineer the implicit constitution of a model by mapping its hesitation landscape?

4. What does the topography of silence reveal about a model's training history?

5. How might we build interpretability tools that focus on hesitation, not just successful generation?

Contribution Guidelines

We welcome contributions to expand the Emergent Turing ecosystem. Key areas for contribution include:

• Additional test modules for new hesitation patterns
• Compatibility extensions for different model architectures
• Visualization and analysis tools for drift maps
• Documentation and example applications
• Integration with other interpretability frameworks

See CONTRIBUTING.md for detailed guidelines.

Ethics and Responsible Use

The enhanced interpretability capabilities of the Emergent Turing Test come with ethical responsibilities. Please review our ethics guidelines before implementation.

Key considerations include:

• Prioritizing interpretability for alignment and safety
• Transparent reporting of findings
• Careful consideration of dual-use implications
• Protection of user privacy and data security

Citation

If you use the Emergent Turing Test in your research, please cite our paper:

@article{keyes2025emergent,
  title={Emergent Turing: Interpretability Through Cognitive Hesitation and Attribution Drift},
  author={Caspian Keyes},
  journal={arXiv preprint arXiv:2505.04321},
  year={2025}
}

Frequently Asked Questions

Is the Emergent Turing Test designed to assess model capabilities?

No, unlike the original Turing Test, the Emergent Turing Test is not a capability assessment but an interpretability framework. It measures not what models can do, but what their hesitation patterns reveal about their internal cognitive architecture.

How does this differ from standard interpretability approaches?

Traditional interpretability focuses on explaining successful outputs. The Emergent Turing Test inverts this paradigm by inducing and analyzing specific failure modes to reveal internal processing structures.

Can this approach improve model alignment?

Yes, by mapping hesitation landscapes and contradiction processing, we gain insights into how value systems are implemented within models, potentially enabling more refined alignment techniques.

Does this work with all language models?

The effectiveness varies with model architecture and scale. Models with richer internal representations (typically >13B parameters) exhibit more interpretable hesitation patterns. See the Compatibility Considerations section for details.

How do I interpret the results of these tests?

Drift maps and hesitation patterns should be analyzed as cognitive signatures, not performance metrics. The framework includes tools for visualizing and interpreting these patterns in the context of model architecture.

License

This project is licensed under the MIT License - see the LICENSE file for details.

---

"The true test of understanding is not whether we can make machines imitate humans, but whether we can interpret the silent boundaries of their cognition."

🔍 Begin Testing →