Swarm Benchmarking

CT-Organized Multi-Agent Systems vs Single-Instance LLMs on Standard Capability Benchmarks

Empirical CT·Budget Regime Theory + Organism Elements

T1T2T6T7A3A5A9B3B4B6Element-IElement-IIElement-IIIElement-IVElement-VElement-VIBudget RegimesSEP

THE INSIGHT

CT predicts that multi-agent swarms organized with scaffold/binder/loops/walls/editors outperform single LLM instances above a complexity threshold C_min, with the gap widening on harder problems.

This is not about raw model intelligence but about organizational coherence — the same claim CT makes about biological organisms, startups, and physical systems.

Budget regime theory places the AI industry at the B_th → B_cxtransition where organizational coherence becomes the binding constraint.

Five CT Mechanisms

Each predicts independently measurable capability gains

T1: EDITOR COVERAGE

Separate editor agents with different prompts have less correlated blind spots than self-review. dim(R_total) > dim(R_i).

Metric: Error detection rate on known-buggy solutions.

T2: MULTI-ROOT EXPLORATION

Multiple independent solution attempts with different strategies cover more of the solution space.

Metric: Success rate on problems with multiple valid approaches.

T7: LOOP-BASED REVIEW

Explicit review loops (generate -> review -> revise) catch errors that single-pass generation misses.

Metric: Accuracy improvement from review iterations.

ELEMENT IV: DOMAIN WALL FILTERING

Structured task decomposition with interface contracts reduces error propagation between sub-tasks.

Metric: Cascading error rate across multi-step solutions.

ELEMENT I: SCAFFOLD STABILITY

Stable role definitions (CLAUDE.md) and consistent context reduce hallucination and drift.

Metric: Consistency of outputs across repeated runs.

Experimental Design

3 benchmarks, 4 conditions, 50 problems each

Benchmarks: SWE-bench (software engineering), ARC-AGI (abstract reasoning), GAIA (general assistants)

Conditions: (1) Single instance, (2) Naive swarm (parallel without CT), (3) CT-organized swarm, (4) Mixed-model CT swarm

Estimated cost: $1,800 total

THE CROSSOVER POINT

Below C_min, single instances win (coordination is pure overhead). Above C_min, CT-organized swarms win (organization enables capability that raw intelligence cannot reach alone). The crossover is the central empirical question.

Key Falsifiable Predictions

AI Benchmarks

P1: Crossover Threshold

There exists a problem complexity threshold C_min above which CT-organized swarms strictly outperform single LLM instances on accuracy.

CONFIRMS IF

CT swarm outperforms single instance above C_min

FALSIFIES IF

No crossover point exists — single instance dominates at all difficulty levels

AI Benchmarks

P2: Widening Gap

The CT swarm's advantage over single instances grows with problem difficulty: the harder the problem, the more organization matters.

CONFIRMS IF

Capability gap grows with problem difficulty

FALSIFIES IF

Gap is constant or shrinks with difficulty

AI Organization

P3: CT vs Naive Swarm

CT-organized swarms (with scaffold/binder/loops/walls/editors) outperform naive swarms (parallel instances without structure) by at least 15% on problems above C_min.

CONFIRMS IF

CT-organized swarm outperforms naive swarm

FALSIFIES IF

Random parallel execution matches CT organization

Source: CT_RESEARCH_SWARM_BENCHMARKING.md · Full experimental design with 10 falsifiable predictions, cost analysis, and CT mechanism decomposition.