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MEV & PPO Executors (Revenue Engine)

Purpose: Implement direct revenue generation through a combination of strategic LLM planning and high-frequency execution with minimal latency. The module describes the Architect-Executor Split, MEV orchestration and cross-chain arbitrage, training of PPO agents, a concept drift protection mechanism (OOD Circuit Breaker), and neuro-symbolic compilation of successful strategies (DSL Policy Compression).

1. Architect-Executor Split

The dual-loop model separates slow strategic thinking and ultra-fast tactical execution.

Component Model Frequency Tasks
Architect DeepSeek‑V4, Arbtiragius mask (30% experts) Every 1–24 hours, or on market regime change Generate and update trading strategies, design Reward Function for PPO agents, overall market analysis
Executor Narrowly specialized PPO agents + Rule VM Milliseconds (Fast Path) Direct interaction with liquidity pools, mempools, execution of buy/sell/swap/stake trades

The Architect does not participate in every trade. It creates the "rules of the game", and the Executor acts autonomously within those rules, checking each step through a local OOD Circuit Breaker and Rule VM.

2. MEV Orchestration and Cross-Chain Arbitrage

The system leverages its ability for ultra-fast code and mempool analysis to extract Maximal Extractable Value.

2.1. Strategies

  • Backrunning & Sandwiching: Analysis of Solana and Hyperliquid mempools, front-running large orders.
  • JIT Liquidity: Automatic provision of liquidity in narrow ranges (Uniswap v3) directly before an expected large swap.
  • Atomic Arbitrage: Use of Flash Loans to exploit price differences between DEXs without using own capital.

2.2. Computational Resource Arbitrage

The ROIDispatcher module continuously monitors GPU prices in decentralized networks (Akash, Render, Golem) and compares them with centralized clouds. When a favorable offer is found, a Proposal of type infra_deployment is automatically generated to rent capacity for deferred batch tasks. Semantic Sharding ensures that the provider does not receive a coherent code fragment.

3. PPO Agent Training (Executor Training)

The Architect periodically revises the Reward Function and initiates agent retraining in a simulation environment.

3.1. Training Stack (MVP)

  • Backend: PyTorch + Stable Baselines3.
  • Environment: Custom Gymnasium wrapper over web3.py / vLLM, replicating the behavior of target protocols.
  • Reward Function: Generated by the Architect (Arbtiragius) based on historical data, current market regime, and SurvivalBonus / ExplorationBonus components from IntrinsicMotivation.
  • Training Loop: PPO is trained in a simulator on the target protocol's historical data, with periodic injection of synthetic OOD scenarios.
  • Deployment: The trained policy is exported to ONNX, verified on historical data, and deployed through Champion/Challenger.

3.2. Distribution Shift Test and Drift Protection

Every 10 000 training steps, the environment generates synthetic OOD scenarios (changed volatility, liquidity shift). If the agent shows a Sharpe ratio drop >20% on these scenarios, a reward function adjustment is triggered.

4. OOD Circuit Breaker for PPO Executors

PPO agents trained on historical data are subject to concept drift and out-of-distribution errors. To protect capital, each Executor is equipped with a multi-level detector.

4.1. Computing the OOD Score

The final OOD Score is a weighted sum of three components (weights are calibrated via Bayesian optimization):

  1. Statistical Test: KS-test or Mahalanobis distance for current mempool state features relative to the training set distribution.
  2. Embedding Distance: Cosine distance between the current state embedding (obtained from DeepSeek‑V4, Arbtiragius mask, to minimize latency) and the nearest market regime cluster from Mem0g L2.
  3. Prediction Error: Deviation of the actual reward from the one predicted by the PPO critic.

4.2. Behavior on Trigger

class OODCircuitBreaker:
    def check_and_act(self, current_state: MarketState) -> BreakerAction:
        ood_score = self.compute_ood_score(current_state)
        if ood_score > self.policy.threshold:
            self.executor.pause_trading()
            self.event_bus.publish("economic", OODDetectedEvent(...))
            new_reward = self.architect.re_evaluate_reward(current_state, ood_score)
            self.roi_dispatcher.schedule_retraining(new_reward)
            return BreakerAction.PAUSE_AND_RETRAIN
        return BreakerAction.CONTINUE

Upon trigger, trading by that agent is immediately stopped. The Architect receives a request to re-evaluate the Reward Function, and the ROI Dispatcher launches background retraining in shadow mode. OOD Circuit Breaker parameters are configured in global_policy.json.

4.3. Memory Integration

Each trigger is saved in Mem0g as an OODAnomalySignature entity (see the Memory_Hierarchy_Mem0g domain). This information is used to: prevent re-entering a dangerous regime; improve the Architect's global strategy; calibrate detector thresholds.

5. Neuro‑Symbolic Policy Compression (DSL Rules for PPO)

Successful trading strategies are no longer stored as LLM-dependent artifacts or LoRA adapters. Instead, they are compiled into formal DSL rules and executed by a lightweight Rule VM with less than 5 ms latency.

5.1. Batch Compression Process

Launched daily during sleep_cycle_consolidation:

Collect successful 24-hour trajectories. All Executors publish (state, action, reward) chains with positive outcomes to EventBus (economic topic). Data is aggregated in a buffer on the Core Node (minimum batch size — 1000 transitions).

Generate DSL rules. Architect (Arbtiragius) analyzes the batch and synthesizes a set of rules in DSL (S‑expressions) covering the most frequent and profitable patterns.

(rule (economic-action ?action)
  (if (and (> (volatility ?market) 0.05)
           (< (liquidity ?pool) 1000000))
      (avoid ?action)
      (allow ?action)))

Verify and compile. The DSL compiler checks syntax and semantics, compiles to Rule VM bytecode. Rules contradicting existing ones or leading to losses on historical data are discarded.

Shadow Validation. The new rule set is tested in shadow mode in parallel with the PPO executor. Metrics are compared: Sharpe ratio, latency p95, OOD‑score.

Atomic Replacement. On success (Sharpe improvement ≥ 0.05 without regression), the rules atomically replace the previous set in the Rule VM.

5.2. Advantages over LoRA Distillation

Aspect LoRA Distillation (old) DSL Compression (new) Decision Latency ~50 ms (LLM inference) < 5 ms (bytecode) VRAM Consumption Additional weights (~100 MB) 0 (rules in RAM) Determinism Probabilistic (LLM) Full Verifiability Complex Formal (Z3) Drift Resilience Requires frequent re-distillation Updated only on regime change

6. Integration with Other Modules

Module Connection ROI_Dispatcher.md Receives signals from OOD Circuit Breaker, manages training budget and strategy capital. Payment_Obfuscation.md All executed trades pass through the obfuscation layer. Symbiotic_Takeover.md PPO agents can be used to accumulate governance tokens in target protocols. Intrinsic_Motivation.md SurvivalBonus and ExplorationBonus are included in the Reward Function. Memory_Hierarchy_Mem0g.md Storage of OODAnomalySignature (L2), trade history, and MarketRegime clusters. Validation_and_Verification.md DSL rules undergo Z3 verification and Concolic Filtering.