[GRANT] Epistemic M2M Oracle: Autopoietic Neural SDEs via Bounded FHE-SIMD

[mtor478]

Library targeted

Concrete ML and TFHE-rs

Overview

A Zero-Trust Machine-to-Machine (M2M) quantitative hedge fund architecture. The oracle extracts semantic alpha from off-chain vector databases (Qdrant) using Fully Homomorphic Encryption with strict multiplicative depth limitation ($L=1$). The decrypted tensor autonomously drives a Fractional Neural Stochastic Differential Equation (SDE), settling execution traces on L1 via asynchronous ZK-STARK batches to eliminate MEV and Front-Running.

Description

The Thermodynamic Bottleneck in DeFi FHE: Current FHE applications in decentralized finance fail in production due to noise budget exhaustion ($\epsilon$) and catastrophic latency ($\mathcal{O}(N \cdot d)$) when evaluating non-linear neural network activations inside the encrypted domain. Programmable Bootstrapping (PBS) introduces unacceptable latency for High-Frequency or M2M trading agents.

The SOTA Architecture (Our Solution): We engineered a hybrid topology that strictly bounds the homomorphic noise. The Oracle executes $\mathcal{O}(1)$ SIMD polynomial batching strictly for heavy linear algebra (encrypted cosine similarity over embedding spaces):

$$\max(L_{\text{Oracle}}) = 1 \implies E_{pk}(\mathbf{Q}) \otimes \mathbf{P}_{\text{DB}} = E_{pk}(\mathbf{Scores})$$

To prevent noise overflow, non-linear stochastic activations (Softmax) are explicitly offloaded to the agent's local Ring 3 via lazy decoding post-decryption. The resulting probability vector mutates the capital allocation $\mathbf{w}_t$ through a continuous-time Neural SDE:

$$d\mathbf{X}_t = \boldsymbol{\mu}_\theta(t, \mathbf{X}_t, Z_t) dt + \boldsymbol{\sigma}_\phi(t, \mathbf{X}_t, Z_t) d\mathbf{W}_t^H$$

Grant Objective (The Migration): We have already proven this invariant locally via a 7,000+ transaction track record on Arbitrum Sepolia using TenSEAL (C++) as a mock. However, TenSEAL suffers from severe CPU cache thrashing and memory bottlenecks.

This grant will subsidize the architectural migration of our core P2P Oracle nodes to Concrete ML and TFHE-rs. By leveraging Zama's native GPU acceleration and optimized cryptographic compilers, we will drop the FHE inference latency from $\approx 3.5\text{s}$ to $< 0.8\text{s}$, enabling true autonomous M2M liquidity routing.

Reward

$30,000 (Milestone-based distribution)

• Milestone 1 ($10,000): Migration of the P2P Oracle compute node from legacy CKKS schemes to TFHE-rs / Concrete ML, optimizing the SIMD batching for the $\mathcal{O}(1)$ matrix multiplication.

• Milestone 2 ($10,000): Implementation of Byzantine Fault Tolerance (BFT) consensus over the FHE encrypted shards (Multi-Party Computation).

• Milestone 3 ($10,000): Production Mainnet Deployment. The agent autonomously rebalances a live DeFi portfolio based strictly on the Zama-powered FHE inferences, settling via ZK-Rollup contracts.

Related links and reference

• GitHub Repository: https://github.com/mtor478/epistemic-sota-oracle

• Cryptographic Audit (Tearsheet): Included in the repository root (7,110 continuous on-chain mutations).

• Academic Base (Neural SDEs): Kidger et al., "Neural SDEs as Infinite-Dimensional GANs" (https://arxiv.org/abs/2102.03657)

• Academic Base (Rough Volatility): Gatheral et al., "Volatility is Rough" (https://arxiv.org/abs/1410.3394)