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)
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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.
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Milestone 2 ($10,000): Implementation of Byzantine Fault Tolerance (BFT) consensus over the FHE encrypted shards (Multi-Party Computation).
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Milestone 3 ($10,000): Production Mainnet Deployment. The agent autonomously rebalances a live DeFi portfolio based strictly on the Zama-powered FHE inferences, settling via ZK-Rollup contracts.
Related links and reference
Library targeted
Concrete MLandTFHE-rsOverview
A Zero-Trust Machine-to-Machine (M2M) quantitative hedge fund architecture. The oracle extracts semantic alpha from off-chain vector databases (Qdrant) using Fully Homomorphic Encryption with strict multiplicative depth limitation ($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):
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:
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,TenSEALsuffers from severe CPU cache thrashing and memory bottlenecks.This grant will subsidize the architectural migration of our core P2P Oracle nodes to$\approx 3.5\text{s}$ to $< 0.8\text{s}$ , enabling true autonomous M2M liquidity routing.
Concrete MLandTFHE-rs. By leveraging Zama's native GPU acceleration and optimized cryptographic compilers, we will drop the FHE inference latency fromReward
$30,000 (Milestone-based distribution)
Milestone 1 ($10,000): Migration of the P2P Oracle compute node from legacy CKKS schemes to$\mathcal{O}(1)$ matrix multiplication.
TFHE-rs/Concrete ML, optimizing the SIMD batching for theMilestone 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)