Benchmarks

How Trinity Sky
compares

Evidence-bounded comparisons against RAG, vector databases, extended context windows, and weight quantization. Every claim is testable.

Comparison 01

FHRR vs Retrieval-Augmented Generation

DimensionTrinity FHRRRAG Systems
Recall MechanismPhase-conjugate algebraic unbindingEmbedding similarity search
Structured Relations Role-filler binding Chunk retrieval
Partial-Cue Recall Native Query reformulation needed
Temporal Ordering Bound timestamps No native model
Recall Latency<132 μs100 ms – 1 s
Update Coherence Bind + superpose~ Re-index needed
InspectabilityMerkle + coherence scoreChunk metadata

Comparison 02

FHRR vs vector databases

CapabilityTrinity FHRRPinecone / Weaviate / Qdrant
Search TypeAlgebraic unbindingApproximate Nearest Neighbor
Composition Circular convolution Flat vectors
Dimensions16,384 (complex64)768 – 1,536 (float32)
CompressionqFHRR 16×PQ / SQ (lossy)
Accuracy99.97% cleanup95–99% recall@10
Sovereignty Local / air-gap~ Cloud-first

Latency

Sub-millisecond recall

FHRR holographic recall at <132 μs (p99). RAG retrieval: 100 ms–1 s. Context re-attention: scales quadratically. At 30 Hz tick rate, only FHRR fits the budget.

<132 μs
vs 100ms+ for RAG retrieval

Hallucination

Coherence-gated recall

LLMs hallucinate at 5.6–13.6% (benchmark-dependent). Trinity FHRR returns a coherence score with every recall — low-coherence results are gated before output emission.

0.892
coherence gate threshold

Compression

qFHRR: 16× smaller

Discrete cyclic group quantization (K=16 bins). 128 KB → 8 KB per vector. Integer-only arithmetic path. 0.987 fidelity retained. Fits in L1 cache.

16×
vs 4–8x for weight quantization

Energy

~30 W full cognitive stack

M4 Max sovereign edge runs complete 30 Hz pipeline at approximately 30 watts. Compare: single A100 inference at 250–400W. Neuromorphic spikes: ~pJ per spike event.

~30 W
vs 250–400W for cloud GPU inference

Every claim is evidence-bounded. Every benchmark is reproducible.

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