The Language of Efficiency. AI-native Intermediate Representation with 10× compression over Python and deterministic safety guarantees.
Publication Status:
Preprint. Work in progress under peer review.
10×
Compression
90%
Energy Saved
Dec 2025
Preprint Date
Compression
10× vs Python
Energy Efficiency
90% Savings
HBM Speed
3,350 GB/s
Security
Deterministic
7.5× more meaning per token than Python through functional operators
Capability-based security with static type validation before execution
Execution stays on-device at 3,350 GB/s vs 128 GB/s PCIe
1024-bit vector instructions for single-cycle complex operations
This paper introduces Neural Bytecode, a dense Intermediate Representation (IR) designed to decouple logic from linguistics. Modern models waste up to 80% of tokens on "human readability" (Python), creating massive overhead.
Key Innovation: By replacing verbose syntax with semantic vector symbols, we achieve a compression ratio of 10×. This allows code to be executed residently on HBM, bypassing the PCIe bottleneck and increasing data movement speed by 26×.
Results: Deterministic safety at the logit level and an order-of-magnitude reduction in energy consumption. This enables complex agentic tasks to be performed with minimal cost while ensuring strict correctness guarantees.
10×
COMPRESSION
Fig. 1: Python vs. Neural Bytecode Density Comparison