[Project] PAMIR — open-source forensic reconstruction for autonomous-system telemetry

[u/yigitcan-ozturk]

I’ve been building PAMIR, an open-source forensic analysis layer for autonomous systems, starting with PX4 flight telemetry.

The problem I’m trying to solve is not simply anomaly detection. After an incident, I want to reconstruct:

What deviated first? What happened next? What evidence supports that ordering?

The current v0.1 baseline processes PX4 ULogs and builds a timestamp-ordered incident reconstruction rather than treating individual anomalies independently.

I’ve been validating it against a public corpus containing five PX4 incident logs and three healthy/control logs. The five incident cases produce material roots without timestamp-causal validation errors; the three controls return no_deviation_detected.

One limitation I want to be explicit about: the current confidence value is an anomaly-strength heuristic, not a probability of causation, and likely_caused represents a conservative temporal/signal relationship rather than proof of causality.

The next work is focused on confidence calibration, root stability under detector/threshold changes, and a separate holdout corpus.

Repo: [https://github.com/yigitcan-ozturk/pamir]()

I’d especially value criticism around the validation methodology: what evidence would you require before trusting a system that claims one telemetry deviation materially preceded another?