Reputation System

Overview

Reputation determines how much work your node receives. Higher reputation means more job assignments and higher earnings. The system is designed to reward reliability while tolerating legitimate infrastructure issues.

The Reputation Scale

Score	Status	Effect
2.0	Maximum	Highest job priority
1.0	Neutral	Starting point for new nodes
< 1.0	At risk	Slashing becomes possible
0.0	Removed	Node ejected from network

How Reputation Changes

Building Reputation

Every successful job increases your reputation score slightly. Consistent successful performance builds reputation above the 1.0 neutral point over time.

Success factors:

• Job completed as assigned

• Peers attested to your contribution

• No timeout or failure reported

Losing Reputation

Every failed job decreases your reputation score slightly. Occasional failures cause minor decreases; sustained poor performance drives reputation toward zero.

Failure factors:

• Job timeout without completion

• Peers reported non-participation

• Validation check failed

Slashing Mechanism

Above 1.0: No Slashing

Reputation above 1.0 incurs no slashing. This tolerance allows for:

• ISP outages

• Power failures

• Hardware upgrades

• Scheduled maintenance

A node with reputation 1.2 that has a bad day and drops to 1.1 loses job priority but not staked collateral.

Below 1.0: Progressive Slashing

When reputation drops below 1.0, slashing activates progressively:

• The lower the reputation, the more stake is at risk

• Sustained poor performance accelerates slashing

• At 0.0, entire stake is slashed and node is removed

This protects the network from consistently unreliable operators while giving honest operators room to recover from legitimate issues.

Job Assignment

Orchestrators use multi-factor scoring when assigning jobs:

Factor	Role
Reputation	Primary weight for most jobs
Geographic proximity	Critical for tunneling services, less important for validation
Current capacity	Available nodes get assignments
Stake amount	Higher stake signals commitment
Historical performance	Track record on similar job types

Practical Impact

A node with reputation 1.5 will receive significantly more job assignments than a node with reputation 1.0, all else being equal. This creates direct economic incentive for reliability.

Triangulated Validation

Reputation isn't self-reported. The system uses triangulated validation:

1. You participate in a meshnet (providing tunneling, for example)

2. Peers in the meshnet sign coordination events

3. Their signatures attest to your actual participation

4. Orchestrators validate based on peer perspectives, not your telemetry

A tunnel cannot claim credit for traffic the peers didn't actually route through it. This organic triangulation prevents fraudulent reward claims.

Best Practices

Maximize Reputation

Practice	Benefit
Have a publicly discoverable IP address	Makes your node most useful to the network; maximizes job assignments
Maintain high uptime	Availability check-ins count toward reputation
Invest in reliable hardware	Fewer failures = reputation preservation
Use quality network connectivity	Low latency improves job success rate
Monitor your node	Catch issues before they affect jobs

Recover from Issues

If your reputation drops below 1.0:

1. Identify the cause: Hardware? Network? Configuration?

2. Fix the underlying issue: Don't just restart. Resolve the root cause

3. Maintain high availability: Consistent uptime rebuilds reputation

4. Complete jobs successfully: Each success moves reputation back up

Recovery is possible, but prevention is better. Monitor proactively.

Why This Design

The reputation system serves multiple goals:

Goal	Mechanism
Reward reliability	Higher reputation = more earnings
Tolerate real issues	Slashing only below 1.0
Prevent gaming	Triangulated validation from peers
Create competition	Better operators earn more
Protect applications	Unreliable nodes lose work

This creates a competitive market where the best infrastructure earns the most.