aurora-mix-v3 — cascade challenger generator

A fifteen-family synthetic time-series prior redesigned around TempoPFN's full pipeline (arXiv 2510.25502). numpy + scipy only — no torch, no vendored package, one self-contained generator.py.

v3 over v2 — the streaming-prefix fix and the evidence rebalance:

1. Stratified interleaved emission. The trainer's streaming feed modes (stream_cpu/stream_gpu) consume only a token-budget prefix of the stream. v1/v2 emitted family-blocked, so only the first family (kernel_gp) ever trained — the deployed valor/aurora-mix was effectively a pure-GP generator (heat rank 3, rel 1.143). v3 interleaves (the same stratified scheme the reigning king ares-v3 uses), so every prefix carries the configured mixture — verified: all 15 families present at their configured shares in a 5% prefix.
2. Evidence-based weights (config.json, no code change needed to iterate): ~45% anchored on the two throne-proven families — trend_seasonal 0.30 (the king holds 4 rounds with 100% tuned trend×seasonality composition) and kernel_gp 0.15 (our GP out-ranked the former king smoothgp) — ~22% on real-world-shape robustness (calendar/GARCH/intermittent), small live shares for the untested TempoPFN-gap trio. Rationale: the KOTH bootstrap-LCB rewards per-window consistency (this round's challenger was 14% better on mean and still lost with LCB −0.356), so uniform decency beats occasional brilliance.
3. Post-transform degeneracy repair: count-rounding can no longer flatten a degenerate-repaired series back to zero-std.

Study the live king first (cascade fetch)

Every committed generator is public and content-addressed. Pull the reigning king and diff your prior against the actual code that is winning right now:

cascade fetch king --network test --chain-toml chain.testnet.toml   # → ./fetched-king-uidN
python local_validator/validate.py --repo my_generator --king ./fetched-king-uidN

--king accepts any generator dir, so the compare stage scores your feature-space coverage against the live king, not just the shipped base_generator. You win by improving on the visible best — a byte-identical copy is dropped before it trains, so you must genuinely beat it.

Strategy vs the king (the TempoPFN redesign)

The king vendors ten TempoPFN families but drops the audio-inspired group (pyo runs an audio server and seeds via hash() — non-deterministic, not on the allowlist) and leaves TempoPFN's augmentation layer mostly unused. v2 attacks exactly that gap:

1. Keeps the highest-signal families (per the TempoPFN ablation), with its own implementations: compositional GP/kernel priors (KernelSynth-style, coarse-grid Cholesky + cubic upsampling) and rich trend × multi-seasonality × structured-noise composition.

2. Reimplements the king's missing TempoPFN audio priors deterministically (pure numpy/scipy):

   family	TempoPFN prior it restores	what it teaches
   rhythm	Stochastic Rhythms	quasi-periodic event trains: tempo drift, accent bars, swing, dropouts, pluck/percussive/bump kernels
   fractal_multi	Multi-Scale Fractals	piecewise-slope spectra, Weierstrass sums, multiplicative cascades (multifractal volatility)
   net_diffusion	Network Topology	forced diffusion on random directed graphs — propagation, echoes, superposition (CauKer flavour, no networkx)

   Financial Volatility (TempoPFN's fourth audio prior) is already covered by regime_garch.

3. Ports TempoPFN's augmentation layer (the vendored king has the params but doesn't use most of it): per-series time-warp, damping envelopes, and spike injection in post-processing, plus batch-level splice transitions (two series crossfaded at a changepoint — TempoPFN's transition_ratio) and the TSMixup-style mixup family.

4. Keeps the seven regime classes the king does not emit (from v1):

   family	real-world shape it teaches	literature
   chaotic (Lorenz/Rössler/Duffing/Mackey-Glass/logistic/Hénon)	nonlinear dynamics, traffic/weather transfer	DynaMix (arXiv 2505.13192)
   fgn (fractional Gaussian noise / fBm)	long-range dependence	classic Hurst
   regime_garch (Markov-switching AR + GARCH)	volatility clustering, regime shifts	econ/finance
   intermittent (zero-inflated counts)	retail demand, sparse events	Croston
   calendar (daily profile × weekly factors × holidays)	energy/traffic load curves	—
   growth (logistic/Gompertz/lifecycle)	adoption curves, saturation	Bass
   bursts_anomaly (subcritical Hawkes + anomaly-injected AR)	bursty events, robustness to outliers/level shifts	—

5. Scale/offset/quantisation diversity in post-processing: log-uniform scales over five decades, offsets, integer quantisation (count-like series), softplus positivity — the from-scratch model must learn scale-robustness from the corpus alone.

6. Length mixture biased long (55 % of crops in [768, 2048]) — more signal per series while keeping short-series coverage.

Determinism

The corpus is a pure function of (seed, n_series). Every RNG is a np.random.default_rng(SeedSequence([seed, tag, index])); there is no global RNG, no torch, no wall-clock, no hash(). Verified by cascade verify (two full draws, digest-compared) and by a cross-process digest smoke (~650k points/s on a CPU box — well above the trainer's 185k tokens/s reference rate, so stream_cpu never starves the GPU).

Verify / test / score

# from the cascade repo root
python local_validator/validate.py --repo my_generator          # CPU gauntlet (valid + regime coverage vs king)
python -m pytest my_generator/tests -q                          # contract tests
cascade verify ./my_generator                                   # the real pre-deploy check

# the REAL signal (GPU, upstream `cascade score`): does it actually beat the king?
bash local_validator/vps_score.sh                               # on a GPU VPS — A/B train+score vs the fetched king

cascade score trains the fixed Toto2-4M on your corpus at the heat budget and scores it (lower geomean = better), so you learn whether you clear the 0.02 win margin before spending a hotkey. See local_validator/README.md for the GPU VPS setup + a sufficient Vast.ai spec.

Deploy (when ready)

cascade deploy ./my_generator --hub-repo <namespace/aurora-mix> \
    --wallet-name <coldkey> --wallet-hotkey <hotkey>
# optional outage fallback (Hub is always tried first; --hf-repo alone is refused):
#   --hf-repo <hf-namespace/aurora-mix>   # needs HF_TOKEN; commits repo@hf:<sha>

Remember: one hotkey = one submission, for life. Deploy only after the local gauntlet and a GPU cascade score A/B that clears the 0.02 margin vs the current king (bash local_validator/vps_score.sh).