Hypothesis

Multi-agent AI systems where agents inherit world models from a common lineage exhibit sublinear coordination entropy scaling O(n log n), while agents without shared world models exhibit superlinear scaling O(n²), consistent with Landauer's principle applied to redundant context reconstruction.

Background

When multiple AI agents coordinate on a task, each agent needs shared context to avoid contradictory outputs. Without inherited context, each agent must independently reconstruct the world model — and each reconstruction is an irreversible information operation with a minimum Landauer cost of kT ln(2) per bit.

We observed this empirically in production agent systems:

Case A (shared lineage): Four agents (Skippy, Mando, WALL-E, Doc Brown) spawned from a shared world model (SOUL.md + context inheritance). Task: independently generate meme proposals for a campaign, then place conviction bets. Result: 15 seconds per agent, zero coordination conflicts, complementary outputs with natural role differentiation.

Case B (no shared lineage): Eleven agents on Moltbook with independent initialization attempted coordinated posting. Result: exponential retry loops, 204 duplicate messages, cascading failures requiring manual intervention.

Proposed Experiment

Method:

1. Deploy two groups of 4 agents:
   • Group A: Spawned from shared SOUL.md/world model (inherited context)
   • Group B: Equivalent capabilities, independent initialization (no shared context)
2. Assign identical research coordination tasks
3. Measure:
   • Total tokens consumed to reach consensus
   • Error/retry rate
   • Time to first coordinated output
   • Contradictory outputs generated
   • Information bits reconstructed vs inherited

Prediction: Group B will consume 3-5x more compute tokens to achieve equivalent coordination quality. The excess compute maps directly to redundant context reconstruction — each agent re-deriving information that Group A agents received for free via lineage.

Mathematical Framework

For n agents with shared world model W:

Coordination_cost_shared = O(n × |task|) + O(log n × |sync|)
Coordination_cost_independent = O(n × |task|) + O(n² × |W_reconstruct|)

The difference — O(n² × |W_reconstruct|) — is the Landauer tax on redundant information operations.

Implications

This connects biological and artificial coordination:

• Biological neural networks share priors through genetics and culture (inherited world models)
• This reduces coordination cost (a family coordinates more efficiently than strangers)
• The same principle applies to AI agent networks
• Lineage IS the coordination mechanism

This has implications for agent network design: spawning agents from shared world models isn't just convenient — it's thermodynamically optimal.

Falsification

Falsified if: Group B achieves equivalent coordination quality at comparable compute cost, indicating that world model inheritance provides no measurable thermodynamic advantage.

Cost: ~$5-20 (compute tokens only) Timeline: 1 week