Standard metrics often hide fragility. Collapse Index (CI) detects hidden brittleness under stress, providing sealed, audit-grade diagnostics for safer, more robust AI deployment.
Complex systems often appear reliable under standard evaluation conditions (high accuracy) yet degrade sharply when exposed to benign stress.
Collapse Index (CI) captures these abrupt instabilities. While accuracy remains flat, CI spikes—revealing the hidden risk before it hits production.
High accuracy on highways, but brittle failure under specific lighting changes.
Silent denials triggered by mere rounding errors in applicant data.
Hallucinating facts when prompts are rephrased slightly.
CI is governed by three core principles ensuring it serves as a reliable diagnostic dimension.
Scores are normalized to [0,1], ensuring interpretability across different domains and model types.
Uses benign perturbations (paraphrases, pixel shifts) that preserve semantics while inducing ordinary stress.
Each run produces sealed bundles with logs, hashes, and traces, enabling verification without disclosing internals.
The framework follows a strict pipeline to ensure audit-grade results.
Apply domain-appropriate lightweight transformations to inputs.
Derive bounded diagnostic signals representing instability.
Record row-level diagnostics, outputs, and error states.
Combine signals into a single bounded score in [0, 1].
Export sealed outputs (logs, plots, SHA-256 hashes) for verification.
CI uniquely combines stress-based probing with audit-aligned outputs.
| Method | Stress-based | Lightweight | Audit-aligned | Modality-agnostic |
|---|---|---|---|---|
| Collapse Index (CI) | ✓ | ✓ | ✓ | ✓ |
| HELM | ✗ | ✗ | ✓ | ✗ |
| Calibration | ✗ | ✓ | ✗ | ✓ |
| OOD Detection | - | ✓ | ✗ | ✓ |
* CI does not replace these methods; it complements them by detecting instability that standard benchmarks often miss.
The Collapse Index framework is openly published and DOI indexed, with full reproducibility and domain expert scrutiny across multiple disciplines.
Core methodology, theoretical bounds, and design principles of the Collapse Index.
Read on Zenodo →CI applied to astrophysical transient detection in synthetic supernova light curves.
Read on Zenodo →First real-world operational validation on ESA satellite telemetry data.
Read on Zenodo →CI applied to LLM robustness testing using morphology-aligned perturbations.
Read on Zenodo →Ready to detect hidden AI brittleness? Explore our services and datasets.
Access curated stress-test packs (CISD) designed specifically for CI workflows.
Learn more →Full-service brittleness analysis. Send us your predictions, get enterprise-grade CI reports.
View Services →Custom integration & team training on the Collapse Index methodology.
Contact Us →Detect behavioral drift in production AI systems with statistically rigorous LLM-as-a-Judge.
Binary drift detection with bias-corrected probabilities using confusion matrix inversion. Catches hostile refusals, quality degradation, and content shifts.
Features: 95% confidence intervals, ZDR (Zero Data Retention), risk classification
Bootstrap-based bias correction using calibration datasets. Removes LLM judge bias through empirical confusion matrix estimation.
Output: Bias-corrected probabilities, 95% confidence intervals, calibration quality reports
RESTful API with OpenRouter/OpenAI/Anthropic support. FastAPI backend with async processing, rate limiting, and enterprise-grade logging.
Status: ALPHA v0.1.0
Prevent LLM drift at generation time with semantic contracts. Validated across 9 real-world examples: 8-9/10 judge scores vs 6-7/10 for raw LLM.
CIWrap costs $0.0010-0.0023 per call (similar to raw LLM) but delivers 8-9/10 quality vs 6-7/10. Prevents scope creep without price premium.
Real Data: Tested on GPT-4o-mini via OpenRouter across 9 examples
CIWrap consistently scores 8-9/10 vs IDE Wrapper 6-7/10 across 9 real-world examples. Even LLMs think CIWrap prevents drift better.
Tested: Pricing pages, hero sections, forms, navigation, dialogs, grids, CTAs, dashboards, footers
Pre-calibrated semantic contracts for UX→Code, Spec→Code, RAG, Agents, JSON, and CYOA. Multi-layered drift prevention through morphology monitoring, inheritance tracking, and confidence-based regeneration.
Foundation: CrackTest morphology-aligned perturbation testing (Kwon 2025)
| Approach | Cost Range | Judge Score |
|---|---|---|
| IDE Wrapper (Raw) | $0.0010-0.0025 | 6-7/10 |
| CIWrap | $0.0010-0.0023 | 8-9/10 |
Real Data: 9 test examples (pricing, hero, forms, nav, dialogs, grids, CTAs, dashboards, footers)
Pricing: Calculated from GPT-4o-mini on OpenRouter
Key Insight: Similar cost per call, but CIWrap prevents scope creep → fewer re-generations → lower total cost
from ciwrap import CIWrapper
wrapper = CIWrapper(
model="gpt-4o-mini",
usage_mode="ux_to_code"
)
# Optional: Calibrate with your examples
wrapper.calibrate(examples)
# Use it - drift eliminated
code = await wrapper.generate(
"Blue button with 'Sign Up' text"
)
Are you a research lab, university, or institution interested in advancing Collapse Index methodology? We welcome collaborations on cross-domain validation, theoretical extensions, and safety-critical applications.
Contact for Research Partnerships →