AOS CONSENSUS PAPER

“Proof-of-Swarm Contribution” (PoSC) Consensus Framework

Research-Grade Explanation

1. Overview

The AsunaOS network replaces traditional blockchain consensus (PoW, PoS, PoA) with a model optimized for robotic intelligence contribution. Instead of rewarding token holding or energy expenditure, PoSC rewards measurable robotic value, including:

• compute contributed

• sensor data (after anonymization)

• model training cycles

• successful task execution

• coordination participation

• validated swarm updates

Therefore, consensus is tied directly to collective robotic intelligence growth instead of arbitrary computational hashing.

2. Core Consensus Logic

Each robot becomes a consensus-validating unit. Blocks are produced by swarm nodes that submit:

• encrypted model gradients

• swarm behavior updates

• proof-of-task execution

• compute logs

• environmental signals

Block acceptance requires:

a) validation of update quality

verified by model performance benchmarks

b) ZK attestations

to ensure data acquisition was legitimate

c) swarm approval

peer robots validate that results align with global objectives

The highest-weight updates win block rights + token rewards.

3. Sybil-Resistance

Instead of staking capital or identity documents, PoSC uses:

• hardware cryptographic identity

• secure element fingerprints

• firmware root-key attestation

• physical-robot presence proofs

So a robot must exist physically to participate.

Sybil attacks become economically unviable.

4. Common-Model Integrity

Every accepted block updates the global swarm intelligence model.

Steps:

1. Robots train model locally

2. Encrypt gradient / ZK packet

3. Publish to network

4. Robots benchmark update

5. Consensus accepts highest score

6. Global model updated

Consensus = continuous global model evolution

5. Multi-Swarm Consensus Weighting

Implementation includes:

Robot Type	Consensus Weight
industrial robot	highest
agricultural drone	high
indoor consumer robot	medium
microbot	lightweight

The more impact a robot type has on global model generalization, the more synchronized weight it receives.

6. Emergent Consensus

As swarm intelligence evolves, consensus becomes self-optimizing:

• update weights change dynamically

• swarm benefit defines priority

• task category changes dynamically

• model performance automatically rebalances incentives

Consensus itself is a learning mechanism.