Speaking to Silicon: Neural Communication with Bitcoin Mining ASICs
Francisco Angulo de Lafuente, Vladimir Veselov, Richard Goodman
cs.NE
Jan 17, 2026 · v1
TL;DR
Provides a Lean 4 and Mathlib formalization with machine-verified theorems about ASIC information leakage.
Abstract
This definitive research memoria presents a comprehensive, mathematically verified paradigm for neural communication with Bitcoin mining Application-Specific Integrated Circuits (ASICs), integrating five complementary frameworks: thermodynamic reservoir computing, hierarchical number system theory, algorithmic analysis, network latency optimization, and machine-checked mathematical formalization. We establish that obsolete cryptocurrency mining hardware exhibits emergent computational properties enabling bidirectional information exchange between AI systems and silicon substrates. The research program demonstrates: (1) reservoir computing with NARMA-10 Normalized Root Mean Square Error (NRMSE) of 0.8661; (2) the Thermodynamic Probability Filter (TPF) achieving 92.19% theoretical energy reduction; (3) the Virtual Block Manager achieving +25% effective hashrate; and (4) hardware universality across multiple ASIC families including Antminer S9, Lucky Miner LV06, and Goldshell LB-Box. A significant contribution is the machine-checked mathematical formalization using Lean 4 and Mathlib, providing unambiguous definitions, machine-verified theorems, and reviewer-proof claims. Key theorems proven include: independence implies zero leakage, predictor beats baseline implies non-independence (the logical core of TPF), energy savings theoretical maximum, and Physical Unclonable Function (PUF) distinguishability witnesses. Vladimir Veselov's hierarchical number system theory explains why early-round information contains predictive power. This work establishes a new paradigm: treating ASICs not as passive computational substrates but as active conversational partners whose thermodynamic state encodes exploitable computational information.
Problem
Bitcoin mining ASICs waste 99.99% of computations (which fail but consume full energy) and suffer idle time from network latency. Whether obsolete mining hardware exhibits exploitable emergent computational properties for bidirectional information exchange with AI systems was unexplored.
Approach
The work integrates five frameworks: thermodynamic reservoir computing (treating ASIC timing jitter as reservoir state), hierarchical number system theory (explaining why early-round SHA-256 information contains predictive power), the Thermodynamic Probability Filter for early-abort prediction, the Virtual Block Manager for latency elimination, and machine-checked Lean 4/Mathlib proofs of core theoretical claims including independence-implies-zero-leakage and energy savings bounds.
Results
Reservoir computing achieves NARMA-10 NRMSE of 0.8661. The Thermodynamic Probability Filter achieves 92.19% theoretical energy reduction. The Virtual Block Manager achieves +25% effective hashrate. Hardware universality is validated across Antminer S9, Lucky Miner LV06, and Goldshell LB-Box. Key theorems are machine-checked with 0 sorry in Lean 4.
| Framework | Core Insight | Result |
|---|
| Thermodynamic RC | Timing jitter encodes state | NRMSE 0.8661 |
| TPF | Early-abort prediction | 92% savings |
| Hierarchical Numbers | Logarithmic energy scaling | O(log N) |
| Virtual Block Mgr | Latency elimination | +25% hashrate |
| Lean Formalization | Machine-verified proofs | 0 sorry |
Framework results summary
