Every experiment, including the failures.

v0.57

Served

Served model

Answers the public Check, API and MCP. Corrects only the sources it passed on, keeps the reference everywhere else.

v0.74

Research candidate

Ensemble-wind correction

+7.8% against the team forecast on its sealed wind weeks — but simply removing each system's usual error at each place scored +9.1% on the same cases. The gain is local habit, not learned failure structure; it will not be promoted.

v0.75

Failed its test

One shared model for all sources

Did not beat separate per-source models, so the larger run never started.

v0.76b

Not enough evidence

Failure Atlas: who misses together

The earlier 35% → 45% readout used ranges drawn too narrow. With ranges corrected on earlier weeks, the test window holds zero group failures, so warnings cannot be judged yet.

v0.77

Exploratory

CPU tree experiment across seven sources (provisional)

Not a neural training run: a small tree model on a CPU in about 4 minutes. An outside review found two average rates computed with future answers included, so we re-ran it with every input time-valid and every model calibrated the same way (v0.77r, a diagnostic re-check of weeks already seen). Against each forecaster's own track record it cut error by about 11% on ensemble weather, 21% on single-run weather and 8% on crypto. The earlier ~25% ForecastBench gain disappeared: it now only matches the track record. The economy and Numinous sets are too small to judge.

v0.78t

Not enough evidence

Neural trust campaign: all 50 models trained, saved and scored

Reading the whole panel together beat each forecaster's track record alone, crypto gained a little, and ensemble weather looked better on few test groups. ForecastBench did worse than the tree, pair history gave no reliable edge, and 2M was no better than 0.5M. Not good enough everywhere to replace the served model.

v0.79

Failed its test

Quick rerun on older data: stopped

A 16-model rerun of the v0.78 recipe on the older data. It was stopped on 20 Oct because it skipped the new sources and the agreed simpler comparisons. Its partial numbers are diagnostic only.

v0.79e

Exploratory

13-source panel reader: scored, research only

Owner approved on 20 Oct. Data rebuilt with corrected answer timing and defect flags; the H200 measured its own speed, then trained all 134 research models (0.5M and 2M) that learn forecaster miss risk and confidently wrong consensus; all were saved and re-checked. Official verdict (v0.79f, 20 Oct): it beats four time-safe references in 7 of 10 testable sources, ties on COVID-19 and Phil. Fed ranges, and loses to a tree on crypto; pair history and the 2M size add nothing. Training is not a result: comparisons against simple references decide anything, and nothing switches the served model.

v0.80

Research candidate

Full H200 run done: two research candidates, now being repaired

Trained on 20 Oct on 454,490 cases from 16 sources (5 versions × 3 seeds, 13 minutes on an H200). Flu (+32% against the reference on confirmation) and energy (+4% against a tree model) passed both test periods; every other field kept the calibrated reference, and crypto, RSV, economy and Polymarket traders scored worse. An outside audit then found defects: forecasts dated after the run, energy missing its outcome clock, events grouped by question instead of real-world event, and two empty inputs. So both wins stay research candidates until v0.80.1 reproduces them on repaired data and a fresh period.

v0.80.2

Research candidate

Public on Hugging Face · 21 fields · 2 beat strong references

Rebuilt on Offdiagonal v6.5: 2,840,152 forecast panels with known outcomes in 21 trained fields, including air quality, urban heat, agriculture, world conditions and shared answer sources. The time audit found no forecast used after its decision time. No real-world event is scored in two periods. The approved full run trained all 21 starts on 21 Oct (7 variants × 3 seeds). The corrected judge (averaged forecasts, one test across all fields) passed two fields on both test periods: energy demand (one forecasting family) and weather vs airports (airport timing still assumed). Flu improved on development but failed confirmation. Every other field keeps the calibrated reference or has too few outcomes. It now answers as an opt-in beta (/api/public/v1/beta/check, same answers as the frozen files on 721 real cases) and powers the public Forecast Failure Watch; its weights are public on Hugging Face (ZenoDivergent/zeno-divergent-v0802) as a research beta; the default answer stays v0.57.

v0.82

Research candidate

Finished · energy, sea and crypto lead

The full v0.82 neural run finished on the H200 on 22 Oct with no failures and was judged against its locked rules; the result is public on Hugging Face. On the confirmation period it corrects forecasts best in energy (17.5% better than track records and the tree learner, 15.6% better than the calibrated reference), sea (25% better than the calibrated reference) and crypto (23%), and helps weather vs airports (10% better than track records). The two warning skills were not saved by this run, and two rivals (the market's own price and a scrambled-relations control) were not measured, so no skill passes on this run alone. v0.82.1 stopped during practice before full training. Serving does not change.

v0.82.1

Stopped during practice

Practice stopped · full training not started

Checked on Hugging Face on 22 Oct at 02:03 UTC. Nine practice checkpoints were saved before the transfer test requested ensemble weather, which has zero labelled panels in this build. The job stopped at 01:22 UTC; the full 30-round, three-seed run never started. The frozen attempt and saved checkpoints remain unchanged. No final verdict or warning-skill pass is available.