fracttalix 12.1.0

Real-time streaming anomaly detection via a Three-Channel Dissipative Network Model

Fracttalix Sentinel v12.1

Streaming anomaly detection grounded in the Three-Channel Model of Dissipative Network Information Transmission — extended with four physics-derived capabilities from Session 36.

Sentinel ingests one scalar (or multivariate) observation at a time and emits a rich result dictionary on every call — no batching, no retraining, no warmup gap once past the configurable warmup window.

Theoretical foundation: Fractal Rhythm Model Papers 1–6 DOI: 10.5281/zenodo.18859299 License: CC0 — public domain

Full documentation | Examples | CHANGELOG

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Table of Contents

1. Overview
2. Three-Channel Model
3. V12.1 Changes
4. Installation
5. Quick Start
6. SentinelConfig — Configuration
7. Pipeline Architecture — 37 Steps
8. Alert Types and Data Structures
9. SentinelResult API
10. MultiStreamSentinel
11. SentinelBenchmark
12. SentinelServer — REST API
13. CLI Reference
14. Backward Compatibility
15. Theoretical Foundation
16. Authors & License

---

Overview

Fracttalix Sentinel is a Python package (pip install fracttalix) for real-time streaming anomaly detection. Its design priorities are:

• Zero external dependencies for core operation — works on the Python standard library alone; numpy, scipy, numba, matplotlib, and tqdm are optional accelerators.
• Immutable, inspectable configuration — SentinelConfig is a frozen dataclass; every parameter is readable and picklable.
• Composable pipeline — 37 DetectorStep subclasses execute in sequence.
• Three-channel anomaly model — monitors structural properties, broadband rhythmicity, and temporal degradation sequences as independent information channels.
• Physics-derived collapse dynamics — maintenance burden, PAC pre-cascade detection, diagnostic window estimation, and reversed sequence detection derived from the Kuramoto synchronization framework.
• Full backward compatibility — all v7.x, v8.0, v9.0, and v10.0 call patterns continue to work unchanged.

---

Three-Channel Model

Implemented from Meta-Kaizen Paper 6:

Channel	Name	What it monitors
1	Structural	Network topology as active transmitter — mean, variance, skewness, kurtosis, autocorrelation, stationarity
2	Rhythmic	Broadband multiplexed oscillatory transmission — FFT decomposition into five carrier-wave bands and cross-frequency phase-amplitude coupling
3	Temporal	One-way irreversible carrier wave — temporal sequence and ordering of channel degradation events

Degradation cascade logic (v9.0):

Band anomaly detected  →  Cross-frequency coupling degrades  →
  Structural-rhythmic channels decouple  →  CASCADE PRECURSOR (CRITICAL)

Extended diagnostics (v10.0):

PAC pre-cascade detected  →  Δt window opens  →  Maintenance burden μ → 1  →
  Coupling rate dκ̄/dt negative  →  Collapse imminent

---

V12.1 Changes

Bug Fixes

• VarCUSUM non-reset (VarCUSUMStep): accumulators s_hi/s_lo now re-arm after each threshold crossing. Normal alert rate: 97% → 35.6%.
• ChannelCoherence unit mismatch (ChannelCoherenceStep): replaced rate-difference formula with Pearson correlation (scale-invariant). Normal data now scores ~0.5 above threshold.

v12.1 Benchmark (n=1000, seed=42)

Archetype	F1	Normal alert rate
point	0.415	—
contextual	0.247	—
collective	0.239	—
drift	0.723	—
variance	0.876	—
normal	—	35.6%

---

Physics-Derived Capabilities

Four capabilities derived from the Kuramoto synchronization framework (added v10.0):

1. Maintenance Burden μ (Tainter Regime Detection)

μ = N · κ̄ · E_coupling / P_throughput

When μ → 1, the network spends all energy on coupling maintenance with zero adaptive reserve — the Tainter collapse condition.

μ range	Regime	Meaning
< 0.5	HEALTHY	Full adaptive reserve
0.5 – 0.75	REDUCED_RESERVE	Adaptive capacity diminishing
0.75 – 0.9	TAINTER_WARNING	Approaching critical burden
≥ 0.9	TAINTER_CRITICAL	Spending all energy on maintenance

mb = result.get_maintenance_burden()
# {"mu": 0.82, "regime": "TAINTER_WARNING"}

2. PAC Pre-Cascade Detection (Extended Diagnostic Window)

Phase-Amplitude Coupling (PAC) measures the depth of nonlinear coupling architecture — the structural memory of the network. PAC degrades before mean coupling strength κ̄ measurably decreases, providing an earlier warning signal than the v9.0 cascade precursor.

Method: Modulation Index (Tort et al. 2010) across 6 slow-phase/fast-amplitude band pairs.

pac = result.get_pac_status()
# {"mean_pac": 0.41, "degradation_rate": 0.18, "pre_cascade_pac": True}

3. Diagnostic Window Δt Estimation (Time-to-Collapse)

Δt = (κ̄ - κ_c) / |dκ̄/dt|

The estimated number of observations remaining before coherence collapse, given current coupling strength and its rate of change. Sentinel stops just detecting that collapse is coming and starts estimating when.

• Only active when κ̄ > κ_c and dκ̄/dt < 0
• Confidence graded: HIGH / MEDIUM / LOW based on rate stability
• Detects supercompensation (adaptive recovery in progress)

dw = result.get_diagnostic_window()
# {"steps": 47.3, "confidence": "HIGH", "supercompensation": False}

4. Reversed Sequence Detection (Intervention Signature)

The thermodynamic arrow of network collapse is irreversible: coupling degrades before coherence collapses. A reversed sequence — coherence collapsing before coupling degrades — indicates:

1. Measurement error
2. Non-universality class membership
3. Deliberate external intervention (a civilization being collapsed vs. one that collapses)

if result.is_reversed_sequence():
    sig = result.get_intervention_signature()
    # {"score": 0.74, "sequence_type": "REVERSED",
    #  "phi_rate": -0.18, "coupling_rate": -0.01}

---

Installation

pip install fracttalix            # zero dependencies — pure stdlib core
pip install fracttalix[fast]      # + numpy, scipy
pip install fracttalix[full]      # + numpy, scipy, numba, matplotlib, tqdm

Optional accelerators (install any or none):

pip install numpy          # FFT, PAC computation, Hilbert transform
pip install scipy          # scipy.signal.hilbert (falls back to numpy)
pip install numba          # JIT compilation for hot loops
pip install matplotlib     # plot_history() dashboard
pip install tqdm           # progress bars in benchmark

Run tests (405 tests, all expected to pass):

pip install fracttalix[dev]       # pytest, ruff, mypy, mkdocs
pytest

---

Quick Start

Basic scalar stream

from fracttalix import SentinelDetector, SentinelConfig

det = SentinelDetector(SentinelConfig.production())

for value in my_data_stream:
    result = det.update_and_check(value)
    if result["alert"]:
        print(f"Step {result['step']}: {result['alert_reasons']}")

Collapse dynamics

result = det.update_and_check(value)

# Tainter regime
mb = result.get_maintenance_burden()
if mb["regime"] == "TAINTER_CRITICAL":
    print(f"Tainter critical: μ={mb['mu']:.2f}")

# PAC pre-cascade (earlier warning than cascade precursor)
pac = result.get_pac_status()
if pac["pre_cascade_pac"]:
    print("PAC pre-cascade: coupling architecture degrading")

# Time to collapse estimate
dw = result.get_diagnostic_window()
if dw["steps"] is not None:
    print(f"Δt ≈ {dw['steps']:.0f} steps ({dw['confidence']} confidence)")
if dw["supercompensation"]:
    print("Supercompensation detected — adaptive recovery in progress")

# Intervention signature
if result.is_reversed_sequence():
    sig = result.get_intervention_signature()
    print(f"Reversed sequence — intervention score {sig['score']:.2f}")

V9.0 three-channel status

if result.is_cascade_precursor():
    print("CRITICAL: cascade precursor")

status = result.get_channel_status()
# {"structural": "healthy", "rhythmic_composite": "degrading",
#  "coupling": "healthy", "coherence": "healthy"}

print(result.get_degradation_narrative())
print(result.get_primary_carrier_wave())   # "mid", "low", etc.

Multivariate mode

cfg = SentinelConfig(multivariate=True, n_channels=3)
det = SentinelDetector(config=cfg)
result = det.update_and_check([v1, v2, v3])

Async usage

result = await det.aupdate(value)

Auto-tune from labeled data

labeled = [(value, is_anomaly), ...]
det = SentinelDetector.auto_tune(data=[], labeled_data=labeled)

---

SentinelConfig — Configuration

SentinelConfig is a frozen dataclass (slots=True). All fields are immutable after construction. Use dataclasses.replace(cfg, field=value) to derive a new config.

Factory presets

Preset	alpha	warmup	Notes
SentinelConfig.fast()	0.3	10	Fastest response, higher FP rate
SentinelConfig.production()	0.1	30	Balanced defaults
SentinelConfig.sensitive()	0.05	50	Catches subtle anomalies; tight multiplier (2.5)
SentinelConfig.realtime()	0.2	15	Quantile-adaptive thresholds

Parameter groups

A — Core EWMA

Field	Default	Description
alpha	0.1	EWMA smoothing factor (0 < α ≤ 1)
dev_alpha	0.1	EWMA factor for deviation estimation
multiplier	3.0	Alert threshold = EWMA ± multiplier × dev_ewma
warmup_periods	30	Observations before alerts are issued

B — Regime Detection

Field	Default	Description
regime_threshold	3.5	Z-score magnitude triggering regime change
regime_alpha_boost	2.0	Multiplicative alpha boost during transitions
regime_boost_decay	0.9	Decay rate of regime boost per observation

C — Multivariate

Field	Default	Description
multivariate	False	Enable Mahalanobis distance mode
n_channels	1	Number of input channels
cov_alpha	0.05	EWMA factor for covariance (Woodbury rank-1)

D — FRM Metrics

Field	Default	Description
rpi_window	64	Rhythm Periodicity Index FFT window
rfi_window	64	Rhythm Fractal Index R/S window
rpi_threshold	0.6	Minimum RPI for "rhythm healthy"
rfi_threshold	0.4	RFI alert threshold

E — Complexity & EWS

Field	Default	Description
pe_order	3	Permutation Entropy embedding dimension
pe_window	50	PE sliding window
pe_threshold	0.05	PE deviation alert threshold
ews_window	40	EWS rolling window (independent from scalar window)
ews_threshold	0.6	EWS "approaching critical" threshold

F — Fluid Dynamics

Field	Default	Description
sti_window	20	Shear-Turbulence Index window
tps_window	30	Temporal Phase Space window
osc_damp_window	20	Oscillation damping window
osc_threshold	1.5	Oscillation damping alert multiplier
cpd_window	30	Change-Point Detection window
cpd_threshold	2.0	CPD alert z-score threshold

G — Drift / Volatility / Seasonal

Field	Default	Description
ph_delta	0.01	Page-Hinkley incremental sensitivity
ph_lambda	50.0	Page-Hinkley cumulative threshold
var_cusum_k	0.5	VarCUSUM allowance
var_cusum_h	5.0	VarCUSUM decision threshold
seasonal_period	0	Seasonal period (0 = auto-detect via FFT)

H — AQB / Scoring / IO

Field	Default	Description
quantile_threshold_mode	False	Use Adaptive Quantile Baseline
aqb_window	200	AQB quantile estimation window
aqb_q_low	0.01	Lower AQB quantile
aqb_q_high	0.99	Upper AQB quantile
history_maxlen	5000	Maximum result records in memory
csv_path	""	Stream results to CSV if non-empty
log_level	"WARNING"	Python logging level

V9.0 — Frequency Decomposition

Field	Default	Description
enable_frequency_decomposition	True	Enable FFT into five carrier-wave bands
min_window_for_fft	32	Minimum window before FFT runs

V9.0 — Cross-Frequency Coupling

Field	Default	Description
enable_coupling_detection	True	Enable phase-amplitude coupling
coupling_degradation_threshold	0.3	Composite score below this → COUPLING_DEGRADATION
coupling_trend_window	10	FrequencyBands snapshots for coupling trend

V9.0 — Structural-Rhythmic Coherence

Field	Default	Description
enable_channel_coherence	True	Enable Channel 1–2 coherence
coherence_threshold	0.4	Score below this → STRUCTURAL_RHYTHMIC_DECOUPLING
coherence_window	20	Rolling coherence window

V9.0 — Cascade Precursor & Sequence Logging

Field	Default	Description
enable_cascade_detection	True	Enable CASCADE_PRECURSOR detection
cascade_ews_threshold	2	Minimum EWS indicators elevated
enable_sequence_logging	True	Enable temporal degradation sequence logging
sequence_retention	1000	Maximum sequences to retain

---

Pipeline Architecture — 37 Steps

Every call to update_and_check() runs all 37 steps in order. Steps read from and write to a shared StepContext.scratch dictionary.

#	Step	Version	Description
1	CoreEWMAStep	v8	EWMA baseline + deviation; must run first
2	StructuralSnapshotStep	v9	Channel 1: mean, variance, skewness, kurtosis, autocorrelation, stationarity
3	FrequencyDecompositionStep	v9	Channel 2: FFT into 5 carrier-wave bands with power and phase
4	CUSUMStep	v8	CUSUM persistent shift detection
5	RegimeStep	v8	Regime change with soft alpha boost
6	VarCUSUMStep	v8	CUSUM on variance
7	PageHinkleyStep	v8	Page-Hinkley drift detector
8	STIStep	v8	Shear-Turbulence Index
9	TPSStep	v8	Temporal Phase Space reconstruction
10	OscDampStep	v8	Oscillation damping detection
11	CPDStep	v8	Change-Point Detection
12	RPIStep	v8	Rhythm Periodicity Index (FFT)
13	RFIStep	v8	Rhythm Fractal Index (Hurst / R-S)
14	SSIStep	v8	Synchronization Stability Index — Kuramoto proxy (rsi alias preserved)
15	PEStep	v8	Permutation Entropy (FRM Axiom 3)
16	EWSStep	v8	Early Warning Signals — variance + AC(1) (FRM Axiom 9)
17	AQBStep	v8	Adaptive Quantile Baseline
18	SeasonalStep	v8	Seasonal decomposition with FFT auto-period
19	MahalStep	v8	Mahalanobis distance (multivariate)
20	RRSStep	v8	Robust Residual Score
21	BandAnomalyStep	v9	Per-carrier-wave anomaly invisible to composite
22	CrossFrequencyCouplingStep	v9	PAC coupling matrix + COUPLING_DEGRADATION alert
23	ChannelCoherenceStep	v9	Structural-rhythmic coherence + SR_DECOUPLING alert
24	CascadePrecursorStep	v9	CASCADE_PRECURSOR — CRITICAL; requires all three conditions
25	DegradationSequenceStep	v9	Channel 3: temporal degradation sequence log
26	ThroughputEstimationStep	v10	P_throughput from band amplitudes; populates band_amplitudes, band_powers, node_count, mean_coupling_strength
27	MaintenanceBurdenStep	v10	μ = N·κ̄ / P_throughput → Tainter regime classification
28	PhaseExtractionStep	v10	FFT bandpass + Hilbert transform → instantaneous phase per band
29	PACCoefficientStep	v10	Modulation Index (Tort 2010) across 6 slow/fast band pairs
30	PACDegradationStep	v10	Rolling PAC history → pac_degradation_rate, pre_cascade_pac
31	CriticalCouplingEstimationStep	v10	κ_c = 2/(π·g(ω₀)) from power-weighted frequency spread
32	CouplingRateStep	v10	dκ̄/dt from rolling coupling history
33	DiagnosticWindowStep	v10	Δt = (κ̄−κ_c)/
34	KuramotoOrderStep	v10	Φ =
35	SequenceOrderingStep	v10	COUPLING_FIRST / COHERENCE_FIRST / SIMULTANEOUS / STABLE per step
36	ReversedSequenceStep	v10	Reversed thermodynamic sequence → intervention signature
37	AlertReasonsStep	v8	Must run last — aggregates all alert signals

Custom steps

from fracttalix import DetectorStep, StepContext, register_step

@register_step
class MyStep(DetectorStep):
    def __init__(self, config):
        self.cfg = config

    def update(self, ctx: StepContext) -> None:
        ctx.scratch["my_metric"] = ctx.current * 2.0

    def reset(self) -> None:
        pass

    def state_dict(self):
        return {}

    def load_state(self, sd):
        pass

---

V9.0 Alert Types and Data Structures

Frozen data structures

Class	Channel	Description
FrequencyBands	2	Five carrier-wave band powers and phases
StructuralSnapshot	1	Mean, variance, skewness, kurtosis, autocorrelation, stationarity
CouplingMatrix	2	PAC coefficients between adjacent bands + composite score
ChannelCoherence	1↔2	Structural-rhythmic coherence score
DegradationSequence	3	Temporal ordering of channel degradation events

Alert types

AlertType	AlertSeverity	Trigger
BAND_ANOMALY	WARNING	Per-carrier-wave anomaly
COUPLING_DEGRADATION	WARNING	composite_coupling_score below threshold
STRUCTURAL_RHYTHMIC_DECOUPLING	ALERT	coherence_score below threshold
CASCADE_PRECURSOR	CRITICAL	All three: coupling + decoupling + ≥N EWS

---

SentinelResult API

SentinelResult is a dict subclass. All v8.0/v9.0 dictionary access patterns work unchanged.

Core fields

Key	Type	Description
"step"	int	Monotonic observation counter
"value"	float	Raw input value
"ewma"	float	Current EWMA estimate
"alert"	bool	Any anomaly triggered
"warmup"	bool	True during warmup period
"anomaly_score"	float	Normalized composite score
"z_score"	float	EWMA deviation in sigma units
"alert_reasons"	list[str]	Triggered conditions
"cascade_precursor_active"	bool	CASCADE_PRECURSOR active

V9.0 convenience methods

result.is_cascade_precursor() -> bool
result.get_channel_status() -> dict
result.get_degradation_narrative() -> str
result.get_primary_carrier_wave() -> str

V10.0 convenience methods

result.is_reversed_sequence() -> bool

result.get_maintenance_burden() -> dict
# {"mu": 0.82, "regime": "TAINTER_WARNING"}

result.get_pac_status() -> dict
# {"mean_pac": 0.35, "degradation_rate": 0.19, "pre_cascade_pac": True}

result.get_diagnostic_window() -> dict
# {"steps": 47.3, "confidence": "HIGH", "supercompensation": False}

result.get_intervention_signature() -> dict
# {"score": 0.74, "sequence_type": "REVERSED",
#  "phi_rate": -0.18, "coupling_rate": -0.01}

V10.0 scalar result keys

Key	Type	Description
"maintenance_burden"	float	μ (0.0–1.0)
"tainter_regime"	str	HEALTHY / REDUCED_RESERVE / TAINTER_WARNING / TAINTER_CRITICAL
"mean_pac"	float	Current PAC strength (0.0–1.0)
"pac_degradation_rate"	float	Fractional PAC decline rate
"pre_cascade_pac"	bool	PAC warning before cascade precursor
"diagnostic_window_steps"	float|None	Steps until coherence collapse
"diagnostic_window_confidence"	str	HIGH / MEDIUM / LOW / NOT_APPLICABLE
"supercompensation_detected"	bool	Adaptive recovery in progress
"kuramoto_order"	float	Φ inter-band phase coherence (0.0–1.0)
"reversed_sequence"	bool	Coherence collapsing before coupling
"intervention_signature_score"	float	0.0–1.0 confidence of deliberate intervention
"sequence_type"	str	ORGANIC / REVERSED / AMBIGUOUS / INSUFFICIENT_DATA
"coupling_rate"	float	dκ̄/dt (negative = degrading)
"critical_coupling"	float	κ_c estimated from frequency distribution

---

MultiStreamSentinel

Thread-safe manager for multiple independent named streams.

from fracttalix import MultiStreamSentinel, SentinelConfig

mss = MultiStreamSentinel(config=SentinelConfig.production())

result = mss.update("sensor_42", 3.14)
result = await mss.aupdate("sensor_42", 3.14)

mss.list_streams()
mss.get_detector("sensor_42")
mss.status("sensor_42")
mss.reset_stream("sensor_42")
mss.delete_stream("sensor_42")

state_json = mss.save_all()
mss.load_all(state_json)

---

SentinelBenchmark

Built-in evaluation harness with five labeled anomaly archetypes.

Archetype	Description
point	Sparse large spikes (8σ) at fixed intervals
contextual	Values anomalous given sinusoidal seasonal context
collective	Extended runs of moderately elevated values
drift	Slow linear mean drift starting mid-series
variance	Sudden 4× variance explosion in second half

from fracttalix import SentinelBenchmark, SentinelConfig

bench = SentinelBenchmark(n=500, config=SentinelConfig.sensitive())
bench.run_suite()   # reports F1, AUPRC, VUS-PR, mean lag, 3σ baseline

data, labels = bench.generate("drift")
metrics = bench.evaluate(data, labels)

---

SentinelServer — REST API

Asyncio HTTP server wrapping a MultiStreamSentinel. No framework dependencies.

python -m fracttalix --serve --host 0.0.0.0 --port 8765

Method	Path	Description
GET	/health	Version, uptime, stream count
GET	/streams	List all stream IDs
POST	/update/<id>	Feed observation: {"value": 3.14}
GET	/status/<id>	Stream stats and last result
DELETE	/stream/<id>	Delete stream
POST	/reset/<id>	Reset stream to factory state

curl -X POST http://localhost:8765/update/my_sensor \
     -H "Content-Type: application/json" \
     -d '{"value": 42.0}'

---

CLI Reference

fracttalix [OPTIONS]
# or: python -m fracttalix [OPTIONS]

Flag	Default	Description
--file, -f	—	CSV file to process (reads first column)
--alpha	0.1	EWMA smoothing factor
--multiplier	3.0	Alert threshold multiplier
--warmup	30	Warmup periods
--benchmark	—	Run benchmark suite and exit
--serve	—	Start HTTP server
--host	0.0.0.0	Server host
--port	8765	Server port
--version	—	Print version and exit
--test	—	Run smoke suite and exit

---

Backward Compatibility

v12.1 is a strict superset of all prior versions. No step is removed. No result key is removed.

V8.0 root-cause fixes (all preserved)

Fix	Label	Description
α	Frozen config	SentinelConfig is a frozen dataclass
β	WindowBank	Named independent deques per consumer
γ	Pipeline decomposition	37 DetectorStep subclasses
δ	Soft regime boost	Multiplicative boost + decay
ε	SSI naming	result["rsi"] alias preserved

V7.x compatibility

from fracttalix import SentinelDetector
det = SentinelDetector(alpha=0.1, multiplier=3.0, warmup_periods=30)  # v7.x kwargs accepted

State persistence

json_str = det.save_state()
det2 = SentinelDetector(config)
det2.load_state(json_str)

---

Theoretical Foundation

FRM Component	Sentinel Implementation
FRM Axiom 3 (ordinal pattern complexity)	PEStep — Permutation Entropy
FRM Axiom 9 (critical slowing down)	EWSStep — variance + lag-1 autocorrelation
Rhythm Periodicity Index	RPIStep — FFT spectral coherence
Rhythm Fractal Index	RFIStep — Hurst exponent via R/S
Synchronization Stability Index	SSIStep — Kuramoto proxy via FFT phase coherence
Three-channel model (Paper 6)	StructuralSnapshotStep, FrequencyDecompositionStep, ChannelCoherenceStep, CascadePrecursorStep, DegradationSequenceStep
Maintenance burden μ	ThroughputEstimationStep, MaintenanceBurdenStep
PAC pre-cascade (Tort 2010)	PhaseExtractionStep, PACCoefficientStep, PACDegradationStep
Diagnostic window Δt	CriticalCouplingEstimationStep, CouplingRateStep, DiagnosticWindowStep
Kuramoto order Φ / reversed sequence	KuramotoOrderStep, SequenceOrderingStep, ReversedSequenceStep

DOI: 10.5281/zenodo.18859299

---

Channel 2 — AI Layers

Machine-readable falsification layers for the Fracttalix corpus. All layers conform to ai-layers/ai-layer-schema.json (v2-S42, Dual Reader Standard).

ID	Paper	Status	File
P1	Fractal Rhythm Model (Paper 1)	PHASE-READY	ai-layers/P1-ai-layer.json
MK-P1	Meta-Kaizen Paper 1	PHASE-READY	ai-layers/MK-P1-ai-layer.json
DRP-1	Dependency Resolution Process	PHASE-READY	ai-layers/DRP1-ai-layer.json
SFW-1	Sentinel v12	PHASE-READY	ai-layers/SFW1-ai-layer.json

---

Authors & License

Authors: Thomas Brennan & Claude (Anthropic) & Grok (xAI)

License: CC0 1.0 Universal (Public Domain) — no restrictions, no attribution required.

GitHub: https://github.com/thomasbrennan/Fracttalix

Version history:

Version	Notes
v10.0.0	4 physics-derived capabilities, 37 steps, 98 tests
v9.0.0	Three-channel model, 26 steps, 65 tests
v8.0.0	Frozen config, WindowBank, 19-step pipeline
v7.11–v7.6	Earlier releases