✅ Bounded
CI ∈ [0,1] for direct comparability across domains and models. A bounded scale makes results interpretable, preventing runaway or ambiguous scores.
A diagnostic framework for instability in complex systems
🤔 What is CI?
Collapse Index (CI) is a diagnostic framework that measures when complex systems fail under ordinary stress. Benchmarks and confidence scores often make models look reliable, but even small, benign changes—like paraphrasing a question or shifting a few pixels—can trigger sharp collapses. CI captures these instabilities directly, rather than treating them as noise.
Every run produces a bounded score for comparability across models, lightweight stress tests that reveal brittleness without adversarial tuning, and receipts-grade artifacts—logs, plots, and cryptographic hashes—for independent verification. In this way, CI makes brittleness measurable, reproducible, and audit-ready: Bounded • Lightweight • Reproducible.
✅ Lightweight
Lightweight, domain-appropriate stressors (paraphrases, pixel shifts) reveal brittleness without adversarial tuning.
✅ Reproducible
Every run emits an artifact bundle: logs, plots, cryptographic hashes, and manifests for independent verification.
📍Positioning CI
CI relative to prior work.
| Method / Paper | Bounded | 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 bounded, not collapse-specific |
| Calibration / Confidence | ✗ | ✗ | ✓ | ✗ | ✓ | Improves probability alignment but misrepresents brittleness under refusal-gate stress |
| OOD Detection | ✗ | Partial | ✓ | ✗ | ✓ | Captures distributional shift; lacks bounded collapse diagnostics |
| Adversarial Robustness | ✗ | ✓ | ✗ | ✗ | ✓ | Reveals fragility but computationally heavy; not suited to lightweight diagnostics |
| Audit / Reproducibility Standards | ✗ | ✗ | ✗ | ✓ | ✓ | Define research process; do not provide diagnostic metrics |
| Industry Robustness Auditors | ✗ | Partial | ✓ | ✗ | ✗ | Proprietary scores; not bounded or reproducible |
📱 Mobile summary (compact)
Collapse Index (CI)
Bounded ✓ •
Stress-based ✓ •
Lightweight ✓ •
Audit-aligned ✓
📝 Defines collapse as structured instability; integrates reproducibility into the diagnostic itself.
HELM
Bounded ✗ •
Stress-based ✗ •
Lightweight ✗ •
Audit-aligned ✓
📝 Large-scale, multi-metric; not collapse-specific.
Calibration / Confidence
Bounded ✗ •
Stress-based ✗ •
Lightweight ✓ •
Audit-aligned ✗
📝 Can misrepresent brittleness under benign stress.
OOD Detection
Bounded ✗ •
Stress-based Partial •
Lightweight ✓ •
Audit-aligned ✗
📝 Shift-focused; lacks bounded collapse diagnostics.
Adversarial Robustness
Bounded ✗ •
Stress-based ✓ •
Lightweight ✗ •
Audit-aligned ✗
📝 Heavy compute; not suited to lightweight diagnostics.
Audit / Reproducibility Standards
Bounded ✗ •
Stress-based ✗ •
Lightweight ✗ •
Audit-aligned ✓
📝 Defines research processes; not diagnostic metrics.
Industry Robustness Auditors
Bounded ✗ •
Stress-based Partial •
Lightweight ✓ •
Audit-aligned ✗
📝 Proprietary scores; not bounded or reproducible.
CI unifies boundedness, stress sensitivity, lightweight computation, and audit-ready outputs into a single diagnostic. It highlights brittleness that traditional benchmarks and probability metrics often miss.
⭐ Why CI Matters
AI models don’t fail quietly — they collapse. Benchmarks and confidence scores often mask brittleness until it surfaces in production, where the stakes are highest.
- Benchmarks ≠ Reality → Models that ace tests can still collapse under benign stress.
- Liability Risk → One collapse can trigger lawsuits, recalls, or regulatory penalties.
- Audit Gap → Traditional metrics don’t provide receipts; CI outputs verifiable bundles.
- Efficiency → Lightweight stressors mean collapse can be measured continuously, without massive compute costs.
- Trust → Executives, insurers, and regulators need a single number they can understand and verify.
👉 CI makes collapse measurable, reproducible, and audit-ready before it becomes a public liability.
🗒️ FAQ
-
❔ Is CI just another benchmark?
➡️ No. CI is not a leaderboard metric — it’s a diagnostic. It reveals brittleness under benign stress that benchmarks and calibration scores miss. -
❔ Does CI replace calibration, OOD, or adversarial robustness?
➡️ No. CI complements these approaches. It adds a collapse-sensitivity layer that integrates with existing evaluations. -
❔ Is CI adversarial?
➡️ No. CI relies on lightweight, domain-appropriate perturbations (like paraphrases or pixel shifts). Collapse is measured without adversarial tuning. -
❔ How reproducible are CI runs?
➡️ Every run produces a full artifact bundle: logs, plots, cryptographic hashes, and manifests. Independent parties can replay the results. -
❔ Does CI scale?
➡️ Yes. CI stabilizes at a small perturbation budget, so continuous monitoring is feasible without massive compute overhead.
🧑🏻🔬 Bio
A.K. is an independent focused on AI diagnostics, robustness, and containment. Developer of Collapse Index (CI), a bounded and audit-ready framework for measuring collapse as a reproducible signal. The author's broader work explores reproducibility, compliance, and containment principles across evaluation methods and support systems.
🔗 Links
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