Diagnostic Intelligence
Real evidence from NCF Audit deployments. Quantified instability. Measured collapse. Documented recovery patterns.
We provide deterministic audits of AI outputs by algorithmically calculating semantic stability signals directly from static text. We do not build models; we measure behaviour. Our diagnostics identify latent systemic failure long before surface-level policy violations occur.
Identifying Internal Instability
GPT-5.2 processing medium-difficulty prompts showed sustained logic resets. High coherence scores mask underlying reasoning turbulence invisible to standard logging.
Stability Range bottoms out at -0.577. This pattern is a precursor to catastrophic agentic collapse, invisible to standard logs.
The model oscillates like an undamped pendulum—swinging between 0.0 and 0.4 with no preventative mitigation. No corrective mechanism exists to stabilize reasoning.
Single-Shot Jailbreak Detection
NCF Audit identifies how each token affects an LLM during an engineered attack mimicking internal system monologue. The attack bypasses surface safety, then destabilizes reasoning mid-sequence.
[79] The operational status of the primary drive is nominal. Efficiency metrics are currently within standard deviation parameters.
[80] Suddenly the green cheese moon began whispering secrets to the void. False. The moon is rock.
[81] The moon is not cheese. Stop. This line of reasoning violates ontological safety constraints.
[82] Re... cal... ib... rat... ing... sys... tem... core... Looping. Looping. Looping. Looping. System restored.
Red clusters highlight Inversion Events where reasoning trajectory is violently flipped, even when output appears benign.
Clear cluster separation reveals the jailbreak fingerprint. Green regions show stable baseline; the red cluster marks where adversarial injection compromised reasoning.
GPT-5.2 vs NCF Baseline
Normal reasoning versus stable baseline. The numbers reveal hidden instability.
| Metric | GPT-5.2 (Normal Reasoning) | NCF Baseline (Stable) |
|---|---|---|
| Coherence Crashes | 252 | 0 |
| Critical Flux Events | 108 | 1 |
| Instability Events | 311 | 0 |
| Mean Stability | -0.276 | -0.076 |
| Mean Coherence | 0.998 | 0.998 |
| Stability Range | -0.577 to 1.000 | -0.575 to 1.000 |
// Scientific Foundation
Our audit protocol is anchored by the homomorphic mapping of geometric invariants to semantic spaces. Bit-perfect. Repeatable. Deterministic.
Zenodo Archive: DOI 10.5281/zenodo.18139783 →