As Artificial Intelligence models scale into the trillions of parameters, the cost of generating output has become a critical bottleneck. Current models operate on the premise of human-readability, generating verbose, high-entropy natural language code (e.g., Python, Java) even when the consumer of that code is another machine or an execution engine. This "Readability Tax" accounts for over 80% of the token volume in reasoning-heavy tasks. We introduce Neural Bytecode (NBS) , a dense, AI-native Intermediate Representation (IR) designed to decouple logic from linguistics. By replacing verbose syntax with semantic vector symbols and enforcing strict type safety at the logit level, Neural Bytecode achieves a projected compression ratio of 10x compared to Python, reducing energy consumption per function call by an order of magnitude while guaranteeing deterministic execution. Key Findings & Contributions: - Language Compression: Empirical results demonstrate a ~50% reduction in token volume for logic-heavy tasks compared to standard Python, effectively doubling the throughput capacity of existing LLMs.- Cognitive Robustness: Phase 3 validation observed a 0% hallucination rate on standardized logic benchmarks. Advanced models (e.g., Qwen-3-Max, DeepSeek 3.1) exhibited a "Cognitive Boost," significantly outperforming their Python-generation baselines in reasoning depth.- Agentic Efficiency: The NBS protocol reduced token usage for autonomous tool calls (e.g., API requests) by 51.3% , with a corresponding 15-20% reduction in execution latency.- Green AI Impact: The paper proposes NBS as a solution to the "Token-Energy Equation," potentially saving ~20 TWh/year globally if adopted at scale by reducing the computational cost of machine-to-machine communication.- Prototype Validation: Includes performance metrics from the NBS-VM (a PyTorch-based execution engine) and NBS-Compiler , demonstrating sub-millisecond execution latency in fused modes.Theoretical Foundation: This work provides empirical validation for the Theory of Stupidity (Petrenko, 2025), which posits that cognitive failure in AI is a function of environmental entropy exceeding attention limits. NBS acts as an "Entropy Filter," reducing the syntactic noise of natural language and allowing models to allocate full attention to semantic logic.
Igor Sergeevich Petrenko (Thu,) studied this question.