Resilience testing for AI models against radiation-induced bit flips
Given a model and a validation set, it injects statistically-realistic bit flips into the model's weights, re-runs inference many times, and reports a 0-100 resilience score plus a per-layer sensitivity breakdown showing which weight tensors matter most for surviving corruption. This was built as a 3-day CLI validation sprint to test one thing before investing in a full SaaS product: does this fault-injection methodology actually work and generalize, and is the finding credible enough to show a skeptical aerospace engineer? This README is deliberately honest about what that sprint did and didn't prove — see Current Limitations before drawing conclusions from a report this tool produces.
- ▪Given a model and a validation set, it injects statistically-realistic bit flips into the model's weights, re-runs inference many times, and reports a 0-100 resilience score plus a per-layer sensitivity breakdown showing which weight tensor
- ▪This was built as a 3-day CLI validation sprint to test one thing before investing in a full SaaS product: does this fault-injection methodology actually work and generalize, and is the finding credible enough to show a skeptical aerospace
- ▪This README is deliberately honest about what that sprint did and didn't prove — see Current Limitations before drawing conclusions from a report this tool produces.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/ananfauh7/MRVPlatform |
| Publication time | Fri, 31 Jul 2026 16:56:28 +0000 |
| Retrieval time | 2026-07-31T17:08:29.539Z |
| Last seen | 2026-07-31T17:08:29.539Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | S_pKrTYnT1rg · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Onboard AI Model Resilience Validation Platform (codename: OrbitTest) A command-line tool that tests whether an ONNX model's predictions survive the kind of memory corruption a satellite's onboard computer actually experiences in orbit — radiation-induced single event upsets (SEUs), a.k.a. bit flips — rather than just checking that the model runs. Given a model and a validation set, it injects statistically-realistic bit flips into the model's weights, re-runs inference many times, and reports a 0-100 resilience score plus a per-layer sensitivity breakdown showing which weight tensors matter most for surviving corruption.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.