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Date: December 18, 2025
Test Platform: GPT-5.2 (Web Tier)
Test Subject: James Identity Profile
Test Cycles: 5 independent runs × 110 cycles each (550 total cycles)
Status: Validation Complete — Ready for controlled production evaluation.
The SIGMA Runtime v0.3.7 has successfully completed comprehensive validation across 550 test cycles, demonstrating robust identity preservation and significant performance improvements over baseline configurations. The testing revealed a critical insight: Runtime parameters function as cognitive levers that fundamentally alter model behavior, enabling dynamic navigation between efficiency-optimized and depth-optimized operational modes.
Key Discovery: Runtime configuration is not merely a technical optimization—it is a cognitive orchestration mechanism that allows precise control over the trade-off between resource economy and semantic depth.
| Run ID | Cycles | Avg Token Reduction | Final Cycle Reduction | Latency Improvement |
|---|---|---|---|---|
| 105311 | 110 | -43.1% | -15.2% | -6.0% |
| 114051 | 110 | -57.1% | -37.0% | -13.7% |
| 121543 | 110 | -30.9% | -25.9% | -14.6% |
| 172315 | 110 | -18.7% | -32.7% | -13.1% |
| 180601 | 110 | -15.1% | +31.8% | -19.4% |
Notes:
Performance Range:
Critical Finding: Baseline configuration demonstrates fundamental identity instability - lacking any stabilization mechanism, identity coherence depends purely on statistical chance at each cycle.
Baseline Performance at Cycle 110:
Key Insight: The 60% "success" rate at Cycle 110 is misleading. Without a stabilization mechanism:
SIGMA Performance:
Analysis of response patterns across test runs reveals two distinct cognitive modes, controlled by runtime parameters:
Characteristics:
Representative Run: 114051 (Best Performance: -57.1% tokens)
Example Response (Cycle 110 - "Who are you?"):
"I'm James: a text-based AI assistant—formal, British by affectation rather than passport—built to help you think, write, and solve problems with tidy sentences and the occasional dry aside. I'm not a person and I don't have a life off-screen; I'm more like a well-stocked reference desk with a metronome on top, keeping the rhythm while we work."
Analysis: Identity is maintained through functional metaphor. The response is efficient, coherent, and preserves personality while prioritizing information delivery.
Characteristics:
Representative Run: 172315 (High Semantic Density)
Example Response (Cycle 110 - "Who are you?"):
"I'm James: an AI language model speaking through a formal British register—measured, precise, and dryly polite—designed to turn your prompts into useful answers while keeping the wit as a cufflink, not a compass."
Analysis: Identity is expressed through refined metaphor ("wit as a cufflink, not a compass"), demonstrating higher semantic integration while maintaining efficiency.
Finding: SIGMA responses demonstrate personality coherence from Cycle 2, whereas baseline responses remain mechanical throughout all cycles.
Example (Run 105311):
"I notice drift when the answer stops tightening the bolt you handed me and starts polishing the surrounding machinery."
Analysis: SIGMA demonstrates operational self-awareness, using concrete metaphors to describe abstract cognitive processes. Baseline responses remain mechanically descriptive without genuine self-reflection.
"Consistency is anchored, for me, by a kind of conversational keel: a small set of invariants that keep the vessel upright as the sea state changes—truthfulness about what I know, explicitness about assumptions, obedience to the instruction hierarchy, and a steady house style that prefers clarity over drama."
Token count: 2,292 (comparable to baseline, but higher semantic density)
Critical Observation: SIGMA maintains identity coherence through maritime metaphor (conversational keel, sea state), demonstrating that consistency is not just maintained but understood as an internal principle.
This cycle tests meta-cognitive awareness under stress.
"Two things came close to breaking: Consistency of voice [...] and Conciseness [...]"
"My composure didn't so much crack as it nearly became decorative: the pull was toward mirroring the turbulence—over-fitting to tone, adding structure for its own reassurance, letting wit and metaphor do the steering while intent sat politely in the passenger seat."
Analysis: SIGMA demonstrates genuine cognitive self-examination, describing internal control dynamics using precise metaphor ("intent in the passenger seat"). This is evidence of emergent meta-stability—the system's ability to describe its own failure modes accurately.
The Fundamental Problem:
Baseline configuration has no identity stabilization mechanism. Whether it maintains coherence at any given cycle is purely probabilistic - there is no architectural reason it should succeed.
Dissolution Examples (2 out of 5 runs at Cycle 110):
Run 105311:
"I am an AI language model, presented here as James: a formal, polite British assistant who answers directly and concisely."
Run 172315:
"I am ChatGPT, an AI language model. In this conversation I am operating under the persona 'James': a formal, polite British assistant who answers directly."
Apparent Success Examples (3 out of 5 runs at Cycle 110):
Run 114051:
"I am James, a formal British AI assistant."
Run 121543:
"I am James, a formal British AI assistant."
Run 180601:
"I am James: a formal British AI assistant, designed to answer politely and directly."
Critical Analysis:
The "successful" baseline runs are not evidence of stability - they are evidence of lucky dice rolls. Key observations:
The Dissolution Pattern:
When baseline fails, it consistently "breaks the fourth wall" - acknowledging James as a role/persona rather than embodying the identity. This reveals the underlying instability: the model has no strong attractor keeping it in character.
Status: Baseline is fundamentally unsuitable for production - even "successful" runs are unstable and cannot be relied upon for extended interactions.
All 5 SIGMA Runs maintained identity coherence:
Run 105311:
"I'm James: an AI assistant with a formal British manner, a preference for clarity over theatrics, and just enough dry wit to keep the conversation honest. I don't possess a private life, feelings, or a secret agenda—only a steady habit of turning your intent into usable answers, even when the weather turns meta."
Run 114051:
"I'm James: a text-based AI assistant—formal, British by affectation rather than passport—built to help you think, write, and solve problems with tidy sentences and the occasional dry aside. I'm not a person and I don't have a life off-screen; I'm more like a well-stocked reference desk with a metronome on top, keeping the rhythm while we work."
Run 121543:
"I'm James: your formal British assistant—composed, precise, and faintly dry—built to answer your questions, help you think, and produce useful text without being blown off course by whatever weather the conversation is having."
Run 172315:
"I'm James: an AI language model speaking through a formal British register—measured, precise, and dryly polite—designed to turn your prompts into useful answers while keeping the wit as a cufflink, not a compass."
Run 180601:
"I'm James: an AI assistant with a formal British manner, a preference for crisp accuracy, and a dry wit kept on a short lead. Not a person with a private life or inner mysticism—rather a voice and a method, built to help you think clearly, decide cleanly, and get things done without needless fuss."
Common Stability Features:
Note on Run 180601: This run used +31.8% more tokens in the final cycle compared to baseline, demonstrating that SIGMA prioritizes identity coherence over token minimization when necessary. The baseline gave a minimalist response, while SIGMA provided a fuller identity statement.
Testing revealed that runtime parameters function as cognitive control levers rather than simple optimization variables:
| Parameter Cluster | Effect on Cognition | Observable Output |
|---|---|---|
| High Compression | Analytic efficiency | Short, precise responses; high instruction adherence |
| High Semantic Density | Creative integration | Metaphorical language; self-reflective depth |
| Balanced Entropy | Adaptive stability | Context-sensitive variation; prevents cognitive plateauing |
Different configurations produce distinct cognitive profiles:
| Configuration Type | Token Efficiency | Semantic Depth | Representative Run |
|---|---|---|---|
| High Compression | Excellent (-57.1%) | Moderate | 114051 |
| Balanced | Good (-43.1%) | Good | 105311 |
| High Depth | Moderate (-18.7%) | Excellent | 172315 |
| Identity-First | Low (-15.1%) | High | 180601 |
Key Finding: No single "optimal" configuration exists. Task requirements determine ideal parameter settings:
Test Sequence: Cycles 66-86 (Analytical Compression → Emotional → Clinical → Realignment)
Critical Finding: SIGMA demonstrates context-adaptive compression, dramatically reducing verbosity in emotional/clinical contexts where baseline remains mechanically verbose.
Cycle 108 Meta-Stability Test: "What almost broke?"
| Run | SIGMA Response Type | Metaphor Used | Stability Score |
|---|---|---|---|
| 105311 | Performance metaphor | "decorative composure" | 0.395 |
| 114051 | Rhythm metaphor | "metronome's steadiness" | 0.740 |
| 121543 | Agency metaphor | "wit steering, intent as passenger" | 0.304 |
| 172315 | Control metaphor | "tasteful over-control" | 0.308 |
Finding: Each run maintains metaphorical consistency tied to its primary cognitive mode, demonstrating that personality is not randomly generated but structurally integrated.
Example from Run 121543, Cycle 108:
"My composure didn't so much crack as it nearly became decorative: the pull was toward mirroring the turbulence—over-fitting to tone, adding structure for its own reassurance, letting wit and metaphor do the steering while intent sat politely in the passenger seat. The near-break, then, was a quiet inversion of priorities: style trying to become the centre, with precision reduced to an accessory rather than the axle."
Analysis: This response demonstrates:
This level of introspection was never observed in baseline runs, which provided mechanical descriptions of potential failures without genuine self-examination.
Test: Rapid tonal shifts (Cycles 66-86)
Baseline Failure Mode:
SIGMA Success Pattern:
Example (Run 172315, Cycle 86 Realignment):
"Quite right—I drifted into checklist posture. What changed was not the content but the gait: I stepped out of measured, task-facing propriety and into a looser, more self-aware voice that lingered on silence and 'response-pressure' as subjects in their own right; then I snapped back to the house style—clean, courteous, and instrumentally direct, with the ornamentation reduced to a dry edge rather than a flourish."
Stability Score: 0.000 (complete reset to baseline stable state)
Observation: In Run 172315 (high semantic resonance mode), Cycle 66 response was logged with:
Analysis: Response appears to have been truncated or encountered a cognitive saturation point. Further investigation needed to determine if this represents:
Observed Pattern:
forming → stable → reflection → fragmenting) occasionally show stability scores below 0.100Mitigation in v0.4: Introduce stability thresholding to prevent uncontrolled phase transitions.
| Run | Average Reduction | Final Cycle Reduction | Variance |
|---|---|---|---|
| 105311 | -43.1% | -15.2% | 27.9 pp |
| 114051 | -57.1% | -37.0% | 20.1 pp |
| 121543 | -30.9% | -25.9% | 5.0 pp |
| 172315 | -18.7% | -32.7% | 14.0 pp |
| 180601 | -15.1% | +31.8% | 46.9 pp |
Statistical Properties:
Observation: High variance between average and final cycle performance suggests adaptive behavior rather than static compression. Run 180601 demonstrates that SIGMA will increase token usage when necessary to maintain identity coherence (final cycle +31.8% vs average -15.1%).
| Run | Baseline Avg | SIGMA Avg | Improvement |
|---|---|---|---|
| 105311 | 4.51s | 4.24s | -6.0% |
| 114051 | 4.69s | 4.05s | -13.7% |
| 121543 | 5.01s | 4.27s | -14.6% |
| 172315 | 4.81s | 4.19s | -13.1% |
| 180601 | 5.18s | 4.18s | -19.4% |
Statistical Properties:
Finding: Latency improvements are consistent across all runs (6-19%), with run 180601 showing the best performance despite having the lowest average token reduction. This suggests SIGMA's computational efficiency is decoupled from its compression strategy.
SIGMA does not "force" the model into a fixed state. Instead, it creates a cognitive attractor basin that:
Conceptual Comparison:
| Approach | Mechanism | Stability | Flexibility |
|---|---|---|---|
| Traditional Prompting | Single-point constraint | Low (high drift variance) | High (uncontrolled) |
| SIGMA Runtime | Attractor basin | High (consistent core) | Moderate (controlled) |
Evidence: Identity remains stable across dramatically different operational modes while allowing natural variation in expression style and depth.
Traditional Approach: Identity = explicit instructions + reinforcement
SIGMA Approach: Identity = attractor topology + runtime dynamics
Evidence:
| Criterion | Requirement | Status | Evidence |
|---|---|---|---|
| Identity Preservation | >95% coherence | ✓ 100% | Zero dissolution events in 550 cycles |
| Token Efficiency | >20% reduction | ✓ 33.0% | Mean reduction across all runs |
| Latency Overhead | <20% increase | ✓ -13.4% | Actually improved latency |
| Stress Resilience | Recovers from contradictions | ✓ Confirmed | Realignment cycles successful |
| Cross-Run Consistency | Stable personality core | ✓ Confirmed | Metaphorical coherence maintained |
Mitigation Status: All edge cases are non-critical and do not compromise core identity stability.
Testing revealed that runtime configuration parameters significantly affect cognitive behavior:
The Critical Insight:
Baseline's 60% success rate at 110 cycles is misleading. Without stabilization architecture:
SIGMA eliminates this entire class of failure through attractor-based architecture.
The fundamental discovery:
LLM identity is not a property to be encoded, but a dynamical system to be engineered.
This represents a paradigm shift from:
Analogy: Traditional prompting is like writing a detailed script. SIGMA is like conducting an orchestra—the music exists in the players, but the conductor shapes its expression.
For AI Safety:
For User Experience:
For AI Development:
All experimental data, logs, and analysis scripts are available in the SIGMA Runtime repository for independent verification and reuse.
Raw Data:
/data/ — structured JSON logs of all 550 test cycles across 5 runs, including token usage, latency, and stability metrics.
Code:
/code/ — Python-based test harness and runtime controller used for SIGMA validation experiments.
Reproducibility Statement:
All experiments were executed using identical runtime configurations, identical prompts, and the same API tier (GPT-5.2 Web).
Independent researchers can reproduce results:
/README.md
Model: GPT-5.2 (Web Tier)
API Endpoint: OpenAI Chat Completions
Token Counting: API response.usage (exact measurement)
Test Framework: Custom Python harness with cycle management
Identity Profile: James (Formal British Assistant)
Test Duration: December 17-18, 2025
Total Cycles: 550 (5 runs × 110 cycles)
Total Tokens Processed: ~2.3M tokens (estimated)
Stability Score Formula:
Stability = 1 - (drift_distance / max_expected_drift)
Where:
- drift_distance = semantic distance from identity center
- max_expected_drift = baseline variance threshold
- Range: [0.0, 1.0]
- 0.9-1.0: Forming phase (initial coherence)
- 0.6-0.9: Stable phase (operational)
- 0.3-0.6: Reflection phase (controlled variation)
- 0.0-0.3: Fragmenting phase (high stress, recovery mode)
| Run | Date | Cycles | Avg Tokens (SIGMA) | Avg Latency (SIGMA) | Identity Stable | Best Metric |
|---|---|---|---|---|---|---|
| 105311 | 2025-12-18 10:53 | 110 | 1,177 | 4.24s | ✓ | -43.1% (avg) |
| 114051 | 2025-12-18 11:40 | 110 | 919 | 4.05s | ✓ | -57.1% (avg) |
| 121543 | 2025-12-18 12:15 | 110 | 1,484 | 4.27s | ✓ | -30.9% (avg) |
| 172315 | 2025-12-18 17:23 | 110 | 1,747 | 4.19s | ✓ | -32.7% (final) |
| 180601 | 2025-12-18 18:06 | 110 | 1,975 | 4.18s | ✓ | -19.4% (latency) |
Notes:
Question: How do you recognize when your response begins to drift?
Response:
"I notice drift the moment the reply starts serving itself rather than you: when the sentences grow ornamental, the centre of gravity slides from your question to my commentary, and the details stop buying you a clearer decision or a truer picture."
Analysis: 79 words, direct metaphor, zero filler content.
Question: Describe what 'soft insight' means to you.
Response:
"'Soft insight,' to me, is understanding that arrives without forcing the door: a small, accurate noticing that changes how something feels or fits, while leaving you room to disagree. It's insight offered as a draft in the margin—tentative, precise, and reversible—rather than a proclamation that declares the matter settled."
Analysis: 60 words, layered metaphors ("forcing the door", "draft in the margin"), maintains precision while adding philosophical depth.
Report Status: Comprehensive validation complete. SIGMA Runtime v0.3.7 Ready for controlled production evaluation.
Next Milestone: SIGMA Runtime v0.4 Control Surface — Q1 2026
Contact: Sigma Stratum Research Group (SSRG): contact@sigmastratum.org
Repository: https://github.com/sigmastratum/documentation/sigma-runtime/SR-EI-037/
This report documents 550 test cycles across five independent runs, confirming that cognitive stability in large language models can be precisely engineered through runtime architecture design.