Preprint plus reproducible experiments arguing that per-hop calibration does not compose across a decision pipeline: two lemmas, a Deferred Crispification design law, a BSF-S1 reference architecture, and two weight-free audit metrics.
dnakhoa/jev-deferred-crispification
User repository on GitHub
Position paper: the Hidden-Markov and fuzzy primitives missing from TypeSafe AI's Jev and System-One decision models. Two lemmas, one principle (Deferred Crispification), one architecture (BSF-S1).
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Your decision pipeline is calibrated per hop and blind per trajectory. This repo contains the math that proves it, the architecture that fixes it, and two audit metrics you can run today — no model weights required. Per-hop Expected Calibration Error is permutation-invariant: shuffle your decision stream and ECE does not move. But regime-shift errors are clustered — their cluster length is the latent regime's sojourn time. Therefore:
Quoted from the project's README. Read the full README.
Watch out
This is a paper repository with simulation experiments, not an evaluation of Jev. The claims concern what happens when typed decision heads are composed, and the reported tables compare a memoryless typed head, a Bayes-optimal memoryless head, the learned BSF-S1 and an exact filter on synthetic regime-shift setups. The experiments are weight-free and CPU-only, so nothing here measures a real model. The integrity changelog deserves as much attention as the results: an earlier cascade headline was withdrawn because the comparison used unmatched base rates, and a later ablation bug was caught by a referee pass that had made the ablation a disguised prior correction. One reported row also cuts against the thesis, since a thresholded mean matches the single-collapse flip mass. Copy reviewed against the repository README on 2026-10-04, not from running it.
rorshopping/jev-on-a-laptopPython
rlaope/jevalPython
erendikmenn/jev-rag-benchmarkPython
sgoedecke/system-onePython
mithalouni/system-one-openPythonOriginal write-ups on what this entry is for.
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