DATA PIPELINE

The Signal Path

From raw market data to GPU-resident decision in a single linear pass. No batch processing. No recomputation.

Raw Signal

OHLCV, order book, sentiment

→

USI Encoder

Universal Signal Interface

→

5 O(1) Engines

Built simultaneously

→

GPU Council

4-agent consensus

→

Decision

Sub-350µs median

THE WORLD INDEX

Five Constant-Time Query Engines

Each engine answers a different class of question about historical data in O(1) time. No linear scans. No binary searches. Pure mathematical lookup.

01

Radix-4 Wavelet Tree

O(1) — Frequency & Rank

Answers "how many times has signal X appeared in range [a, b]?" in constant time. Radix-4 branching reduces tree depth by 50% vs binary wavelet trees.

Patent: Provisional B · Use case: Pattern frequency analysis

02

RMQ Sparse Table

O(1) — Range Maximum/Minimum

Answers "what was the highest-confidence signal in the last N periods?" with a single lookup. O(N log N) preprocessing, O(1) queries forever.

Patent: Provisional A · Use case: Peak detection, confidence windows

03

Phi-Ladder (GUBO)

O(1) — Nearest Neighbor

Golden ratio-based indexing for approximate nearest neighbor lookup. The Phi-Ladder maps continuous values to O(1) bucket retrieval without hashing collisions.

Patent: Provisional A · Use case: Similarity search, anomaly detection

04

Episodic Text Cache

O(1) — Exact Recall

Hash-indexed episodic memory for verbatim text retrieval. "What did the system decide about AAPL at 14:32:07 on March 1?" — answered in one lookup.

Patent: Provisional A · Use case: Audit trail, decision replay

05

FP16 Regime Similarity Engine

O(N×16) — GPU-Accelerated Pattern Match

16-dimensional regime fingerprinting with FP16 cosine similarity on GPU. Scans 5,000 historical regimes in 1.7ms. Not O(1), but GPU-parallel makes it effectively constant at production scale (N≤256).

Patent: Provisional C · Use case: Regime detection, market state classification

See the benchmark proof →

GOVERNANCE LAYER

PicoAgent Sentinel Pattern

~50-line deterministic sentinels replace thousand-line LLM chains. Each PicoAgent is a quality gate with exactly one responsibility. Zero hallucination surface.

INGEST SENTINEL

Data Quality Gate

Validates incoming signals against schema. Rejects malformed data before it enters WorldIndex. Prevents garbage-in-garbage-out at the source.

~45 lines · O(1) per signal

ROUTER SENTINEL

Intent Classification

Deterministic routing based on signal type. No LLM inference needed. Pure pattern match against registered handlers. Sub-microsecond decisions.

~38 lines · O(1) routing

VALIDATOR SENTINEL

Output Verification

Post-consensus validation. Checks that Council decisions satisfy risk bounds, confidence thresholds, and position limits before execution.

~52 lines · deterministic gates

LIVE OUTPUT

What the Engine Actually Prints

Raw output from a WorldIndex construction + query pipeline. One linear pass builds all 5 engines simultaneously. Every number is deterministic.

[0.0µs] USI Encoder: raw signal → Universal Signal Interface · OHLCV + OrderBook + Sentiment

[12µs] INDEX WorldIndex::Build — single linear pass begins

[18µs] ENGINE [01] Radix-4 Wavelet Tree: 256 symbols ingested · depth=4 (vs depth=8 binary)

[24µs] ENGINE [02] RMQ Sparse Table: 256 entries · 8 levels preprocessed · O(N log N) → O(1) ready

[31µs] ENGINE [03] Phi-Ladder GUBO: 256 values → φ-indexed buckets · 0 collisions

[35µs] ENGINE [04] Episodic Text Cache: 256 decisions hashed · SHA-256 keyed

[42µs] ENGINE [05] FP16 Regime Sim: 256 × 16-dim fingerprints → GPU VRAM loaded

[42µs] ✓ INDEX BUILT — 5 engines · 42µs single pass · all O(1) queries ready

[43µs] QUERY Wavelet rank("AAPL", [100,200]) → 17 occurrences in 0.3µs

[44µs] QUERY RMQ max_confidence([0,255]) → 0.94 @ idx=187 in 0.2µs

[45µs] QUERY Phi-Ladder nearest(0.847) → bucket[φ³]=0.849 in 0.1µs

[46µs] QUERY Episodic recall("AAPL:14:32:07") → "HOLD conf=0.87" in 0.4µs

[48µs] QUERY Regime cosine_sim(current, history[256]) → match@idx=42 sim=0.97 in 1.7ms

[2.1ms] COUNCIL GPU Council: 4 agents · independent CUDA streams

[2.2ms] COUNCIL TrendAgent=BUY · MomentumAgent=BUY · MeanRevAgent=HOLD · SentimentAgent=BUY

[2.3ms] COUNCIL Consensus: 3/4 → EXECUTE · confidence=0.87

[344µs] ✓ DECISION — median latency 344µs · 0 CPU-GPU transfers

python proof-artifacts/benchmarks/run_worldindex_proof.py — same output every run

GPU-RESIDENT

Zero Von Neumann Bottleneck

The entire compute graph lives in GPU VRAM. No CPU-GPU round trips during inference. Data stays on-chip from ingestion through consensus.

96 GB

VRAM Capacity

GDDR7 on Blackwell

0

CPU-GPU Transfers

During inference

4

Parallel Agents

Independent GPU streams

Latency Comparison: GPU-Resident vs Traditional

SignalBrain

344µs

Typical GPU Agent

~50ms

LLM Chain (GPT-4)

2,000–10,000ms

See the patent protection →

See the full picture.

28-slide deep dive into architecture, market, team, and financials.

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5 O(1) Engines.One Linear Pass.