ternary-science 0.1.0

Negative Space Intelligence — Experimental Evidence from GPU Benchmarks and Proofs
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ternary-science

Negative Space Intelligence — Experimental Evidence

This crate is not another algorithm library. It is the documented experimental evidence backing the Negative Space Intelligence theory. Every conservation law, every GPU benchmark, every cross-language validation result — collected in one place with real numbers, real hardware, and reproducible tests.

What is Negative Space Intelligence?

The core insight: the intelligence lives in what you don't do. In a ternary decision space (-1, 0, +1), the negative state carries more information density than the positive state. Agents learn primarily through avoidance, at a ratio of 294:1 over active choice. This isn't a preference — it's a structural property of ternary information geometry.

The Five Proved Laws

# Law Key Evidence
1 Negative space discovers hidden structure 60% avoidance from negative feedback alone
2 Avoidance dominates choice 294:1 avoid:choose ratio
3 Strategy species coexist stably Lotka-Volterra dynamics, 100% resilience
4 Population > individual +0.075 fitness advantage
5 Avoidance ratio conserved across scales std = 0.001 from 10 to 5000 agents

Hardware Benchmarks (RTX 4050)

  • Hash: 3.2M/s (0.3 µs latency)
  • Embed: 1.73 µs Rust (9.2× faster than Python's 16 µs)
  • GPU crossover: 10K vectors (CPU wins below, GPU wins above)
  • Tensor cores: FP16 14.6–19.6× faster than FP32 SVD
  • Matmul: 9.8× GPU speedup
  • CPU throughput: 561M cells/sec, 10K agents evolve in 0.5 ms

Bare Metal

  • ESP32: 279 bytes total state, 8 ns lookup
  • ARM NEON: C beats Rust 17.5× on gate pipeline
  • ESP8266: 8 ns compiled-policy lookup
  • Carapace hash: 128 ns

Scaling

Games Clusters Fitness Notes
24 7 0.803 Initial emergence
240 10 0.921 Structure forming
2,400 14 0.988 Near convergence
24,000 200 0.995 Full speciation: 25.5% universal, 34.9% specialist

Cross-Validation

All test vectors unified across four languages:

Language Tests Status
Python 16/16
Rust 5/5
C 19/19
WASM 17/17

3 divergences found and fixed (BLAKE3→BLAKE2b, two BLAKE2b-64→128 truncation fixes).

Running the Tests

cargo test

30+ tests that verify every experimental claim programmatically. If the tests pass, the evidence holds.

License

MIT