> Collapse Index CLI.
Initializing...
8ba7c07b991f04a58dedb69254c2f7edfc503ab9216250029a725bbf0a465235
> System diagnostics just crossed the singularity.
Initializing...
8ba7c07b991f04a58dedb69254c2f7edfc503ab9216250029a725bbf0a465235
> System diagnostics just crossed the singularity.
Collapse shows up as confident errors from small changes, silent regressions on near‑identical inputs, unstable enforcement that risks compliance, and hard‑to‑reproduce failures that frustrate audit — the kind of instability standard metrics often miss. In practice, it surfaces in areas such as self‑driving, credit decisions, and large language models:
Minor changes in sensor inputs or road conditions (e.g., lighting or lane markings) can cause unsafe decisions.
Minor changes in applicant data (e.g., income rounding or address formatting) can cause silent denials or approvals.
Minor changes in prompt wording (e.g., rephrasing or added context) can cause confidently wrong or inconsistent answers.
These risks emerge under small, benign changes. Collapse Index (CI) measures stability beyond accuracy and turns risk into an auditable signal.
Standard metrics often overlook collapse from minor changes. Collapse Index (CI) highlights instability under benign variation, surfacing confidently wrong behaviors that impact real-world deployment.
By probing with subtle perturbations, CI exposes vulnerabilities traditional validation misses. This enables teams to identify brittle regions, strengthen reliability, and ensure robust performance before production rollout.
| Method | Stress-based | Lightweight | Audit-aligned | Modality-agnostic | Notes |
|---|---|---|---|---|---|
| Collapse Index (CI) | ✓ | ✓ | ✓ | ✓ | Defines collapse as structured instability; integrates reproducibility into the diagnostic itself |
| HELM | ✗ | ✗ | ✓ | ✗ | Large-scale, multi-metric evaluation; not collapse-specific |
| Calibration / Confidence | ✗ | ✓ | ✗ | ✓ | Improves probability alignment but misrepresents brittleness under stress |
| OOD Detection | - | ✓ | ✗ | ✓ | Captures distributional shift; lacks collapse diagnostics |
CI uniquely combines stress-based probing, lightweight operation, audit-aligned outputs, and modality-agnostic coverage. Benchmarks (HELM), calibration, and OOD detection are complementary but do not diagnose collapse; use them alongside CI for broader assurance.
Provide a dataset with redacted identifiers, variants, labels, and confidence scores.
CI computes collapse risk, interprets alignment, and produces a sealed bundle for integrity and audit.
A few thousand rows can be analyzed in seconds on a standard laptop.
No dependencies, no external services, no hidden calls. Only local computation.
CI plugs cleanly into evaluation pipelines and schedulers.
Each run produces standardized summaries and sealed logs that integrate easily with organizational audit systems.
CI systematically varies inputs to reveal where models break,
surfacing instability and confidently wrong outputs for actionable diagnostics.
Detect and highlight cases most susceptible to collapse for targeted analysis and review.
Guarantee integrity and reproducibility with cryptographically signed output files.
All processing is performed locally, ensuring data never leaves your environment.
Sealed outputs can be independently verified for secure, offline audits.
The Collapse Index Dataset Generator (CIDG) is a standalone CLI for producing synthetic datasets designed to stress test Collapse Index.
Define custom schemas, randomize fields, and export in compatible formats all locally and offline.
Simulate a wide range of scenarios to support thorough model diagnostics and stress testing.
Generate synthetic datasets with custom schemas tailored to your domain and use cases.
Full CLI interface for fast iteration, batch runs, and streamlined analysis.
Precisely configure fields, distributions, and perturbations to mirror real stressors.
Direct output compatibility with the Collapse Index CLI for zero-friction analysis.
Offline, privacy-preserving generation—no external services or cloud calls.
Explore curated synthetic datasets crafted to surface collapse under benign variation.
Standardized schemas, sealed manifests, and offline integrity checks included.
Analyze and compare with predictable fields and variants designed for CI‑compatible workflows.
Each pack ships with a manifest and integrity hashes for reproducibility and audit.
All datasets are created and computed with the CI Data Generator (CIDG) locally—no cloud calls.
Interested in trying Collapse Index CLI?
We offer pilot access to select organizations for evaluation and feedback.
All pilot runs are performed locally on redacted or synthetic datasets. No cloud, no external calls.
Each run produces a cryptographically sealed report for compliance and internal review.
Get started in quickly with minimal configuration. Compatible with standard tabular formats.
> Developed and based in Silicon Valley, CA.
/Request a pilot →
"Collapse isn’t failure.
It’s the first honest signal of how a system breaks."