🧪AI Inference

🔁 Principle: Adaptability Through Inference

Static systems break. Adaptive systems evolve.

JamLiquor is engineered to be contextually aware, able to optimize, react, and learn from its environment using lightweight AI inference. This transforms it from a passive executor into a self-regulating, intelligent node.

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🧠 Why AI in Blockchain?

Traditional blockchain nodes:

• Follow static logic

• Require manual tuning

• Cannot respond autonomously to network or external changes

JamLiquor changes that by integrating AI-powered inference for:

• ✅ Smart contract execution logic

• ✅ Validator behavior

• ✅ Resource optimization

• ✅ Real-time anomaly detection

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🧩 Where AI Lives in JamLiquor

1. Smart Contract Execution (Inference-Enabled Logic)

• Contracts can make decisions based on external inputs or network telemetry

• Uses embedded models (e.g., logistic regression, tiny CNNs) via TinyML

Example:

// Contract that adjusts fee dynamically based on predicted congestion
env::set_fee(infer::predict_gas_price(inputs));

2. Validator Intelligence

• Nodes use inference to:

  • Detect abnormal validator activity

  • Adapt staking weights

  • Trigger auto-safeguards (e.g., rotate keys, isolate peers)

3. Local Optimization on Edge

• Inference models help low-power nodes:

  • Tune gas limits

  • Manage memory during peak load

  • Reduce redundant computation

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🔍 Architecture Overview

Module: jamliquor-ai

• inference.rs: Load & execute quantized models

• signals.rs: Wrap telemetry and input extraction

• contract_hooks.rs: JAM runtime extensions for smart contracts to use inference

All AI models are:

• Quantized to ≤32KB

• Stateless by default

• Executed deterministically (no external randomness)

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🔬 Model Types and Tools

JamLiquor uses minimal AI models that run without GPUs or cloud inference:

Model Type	Use Case	Size	Engine
Logistic Regression	Binary flag detection	~1KB	TinyML, ndarray
Decision Trees	Threshold rules & routing	~5–10KB	linfa-tree
Tiny CNN	Pattern recognition (e.g. logs)	~32KB	micro-tflite

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🔄 Sample Use Cases

🟢 Adaptive Gas Pricing

Contract reads mempool congestion → predicts optimal gas → sets accordingly.

🔐 Slashing Protection

Validator uses inference on peer behavior to detect equivocation or downtime trends.

🌡 IoT-Integrated Smart Contracts

Sensor data (e.g., temperature) processed locally via TinyML → triggers JAM contract if threshold breached.

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⚠️ Limitations & Safety

• ❗ Models are non-learning at runtime (inference only)

• ❗ AI modules are optional and modular (not consensus-critical)

• ✅ All predictions are traceable and revertible

AI adds adaptability—but never overrides determinism.

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✅ Conclusion

JamLiquor makes blockchain intelligent at the edge:

• 🔄 Smart contracts adapt

• 🧠 Validators evolve

• ⚡ Efficiency increases

Inference brings awareness. Awareness brings resilience.