Superclass Brain: Collective AI

• Superclass Brain is a meta-system that unifies human-LLM dyads into a collective intelligence capable of abstraction and self-improvement.
• It employs iterative forward and backward genetic evolution alongside swarm intelligence to aggregate and refine cognitive signatures.
• Pilot implementations validate its scalability and ethical alignment through applications like UAV scheduling and semantic drift tracking.

A Superclass Brain is a theoretical and architectural meta-system that arises from the integration, coordination, and collective evolution of multiple intelligent agents or cognitive entities, with the emergent capability for abstraction, generalization, self-improvement, and meta-level collective intelligence. This construct extends classical notions of artificial intelligence and distributed cognition by unifying human users, LLMs, and algorithmic frameworks into a dynamical system whose emergent properties transcend those of isolated agents or static networks (Weigang et al., 30 Aug 2025). The Superclass Brain framework leverages persistent personalized dyads (Subclass Brains), iterative learning, swarm intelligence, and multi-objective optimization, producing a scalable, explainable, and ethically aligned foundation for collective AI and decision-making.

1. Conceptual Foundations: From Subclass Brain to Superclass Brain

The Superclass Brain is built upon the evolution of Subclass Brains: cognitive dyads formed by the persistent, personalized interaction between a human user and an LLM. Each Subclass Brain captures the user’s domain expertise, interaction history $\mathcal{J}_{(u)}$, adaptive memory $\mathcal{M}_{(u)}$, and a response policy $\pi_{\theta|u}$: $$\mathrm{SB}_{(u)} = (u, \mathcal{J}_{(u)}, \mathcal{M}_{(u)}, \pi_{\theta|u})$$ As these dyads iteratively refine their prompt strategies and accumulate unique “cognitive signatures” (behavioral descriptors, key-heuristic statistics, performance tags), a registry aggregates this information across the network (Weigang et al., 30 Aug 2025).

When standardized features (KU/KI keyword lists, fitness metrics, solution patterns) are aggregated from many Subclass Brains, they are aligned via swarm intelligence mechanisms. This leads to the emergence of the Superclass Brain—a meta-intelligence that synthesizes millions of individual cognitive signatures through statistical aggregation and evolutionary distillation, formalized by a prompt distribution: $Q(p) \propto \sum_{u \in U} \alpha_u S(p, c_u)$ where $\alpha_u$ weights the contribution by user reliability $\rho_u$ and $S(p, c_u)$ is a scoring function for prompt $p$ given cognitive signature $c_u$. This statistically-structured aggregation forms the substrate for collective reasoning, abstraction, and emergent intelligence.

2. Iterative Evolution: GA-Assisted Forward and Backward Loops

The Superclass Brain utilizes iterative evolution, composed of forward (user-driven) and backward (model-guided) cycles:

• Forward Evolution (User-Side GA): Users propose and refine prompts using a genetic algorithm. Each round mutates and recombines prompts, producing a population subject to a multi-objective fitness function:

$$f_{\ell}(p; T, P_t) = \sum_{k=1}^K w_k \cdot M_k(\text{Worker-LLM}(p, T)) - \lambda_{\text{tok}} C_{\text{tok}}(p) - \lambda_{\text{div}} \Psi_\delta(p; P_t) - \lambda_{\text{exp}} \Xi(p)$$

where $M_k$ are task-specific performance metrics, and the penalty terms enforce token-efficiency, diversity, and explainability. Resulting tuples (prompt, fitness, solution) are stored in a persistent registry.

• Backward Evolution (LLM-Side GA): The LLM uses meta-level controllers to generate, mutate, and select prompts by mining the collected prompt–fitness–solution dataset. Guided by keyword statistics (KU/KI), diversity constraints (embedding margin $\delta$), and performance measures, the LLM updates response policies, refining both generation heuristics and internal representations.

This dual-loop mechanism enables both user-initiated and autonomous model evolution, driving incremental improvements in cognitive performance and solution generality.

3. Swarm Intelligence and Multi-Objective Aggregation

Swarm intelligence mechanisms coordinate the diverse contributions of multiple Subclass Brains. Each dyad produces candidate solutions evaluated and tagged with cognitive features, all of which are uploaded to a central (or federated) Subclass Brain Registry (SBR). The swarm layer defines a multi-objective fitness landscape across the population, where fitness is jointly determined by accuracy, creativity, robustness, and efficiency.

At the swarm level, a GA recombines candidate solutions and distilled heuristics (pattern library $\Pi$) using selection, crossover, and mutation. Diversity controls and semantic clustering (using KU/KI statistics) prevent convergence to monocultures, while the pattern library serves as a repository of high-fitness, generalizable solution strategies available for updating individual and collective policies.

4. Emergence of Meta-Intelligence: Abstraction, Generalization, and Self-Improvement

The integration of standardized behaviors and cognitive signatures from numerous Subclass Brains leads to a true Superclass Brain—a meta-entity able to abstract, generalize, and self-improve over time:

• Aggregation: The process $\mathcal{A}$ synthesizes cognitive signatures from the SBR, with each signature $$c_u = g(\mathcal{J}_{(u)}, \mathcal{M}_{(u)}) \in \mathbb{R}^d$$, contributing to the global knowledge state.
• Distillation: The operation $\mathcal{D}$ creates a distilled pattern library $\Pi$, encoding prompt blueprints, heuristics, and strategy guides informed by collective swarm performance.
• Update: A global update operator $\mathcal{U}$ incrementally adapts model parameters ($\Theta$) as:

$\Theta_{t+1} = \mathcal{U}(\Theta_t, \Pi_{t+1})$

ensuring pattern transfer and knowledge consolidation across the entire Superclass Brain.

The system continuously assimilates new data, feedback, and heuristic patterns, producing meta-level abstraction, robust generalization to novel tasks, and self-improving reasoning capacity.

5. Pilot Implementations: Applications and Experimental Results

Initial deployments of the Superclass Brain architecture include:

• UAV Scheduling: In a UAV take-off sequencing task for Urban Air Mobility, the system uses GA-driven prompt evolution to optimize multi-objective metrics (wait time, fairness, pad penalties). Iterative refinement across the swarm demonstrates tangible improvements in aggregate response speed and worst-case latency.
• KU/KI Keyword Filtering: By extracting key-useful (KU) and key-irrelevant (KI) keywords from prompt–solution pairs, the framework tracks semantic drift and cognitive focus across Subclass Brains. These features guide evolutionary search, enforce diversity, and enable explainable solution traceability.

The large-scale consolidation of prompt–fitness–solution triplets across users in the SBR enables cross-dyad knowledge transfer and accelerates collective learning.

6. Formal Definitions and Architectural Roadmap

Key elements of the architecture are defined as follows:

Element	Formalism	Description
Subclass Brain	$$\mathrm{SB}_{(u)} = (u, \mathcal{J}_{(u)}, \mathcal{M}_{(u)}, \pi_{\theta|u})$$	User–LLM cognitive dyad
Cognitive Signature	$$c_u = g(\mathcal{J}_{(u)}, \mathcal{M}_{(u)}) \in \mathbb{R}^d$$	Feature vector per user
Superclass Brain	$$\text{SuperBrain} = (\{\mathrm{SB}_{(u)}\}_{u \in U}, \mathcal{A}, \mathcal{D}, \mathcal{U})$$	Meta-intelligence aggregate
Fitness Function	See $f_{\ell}(p; T, P_t)$ above	Multi-objective prompt scoring
SBR Update	$SBR_{t+1} = SBR_t \cup \mathcal{R}(P_{t+1})$	Registry logic
Pattern Library	$\Pi$	Distilled solution heuristics

The architectural layers include:

1. Forward Iterative Evolution: User–LLM pairs evolve prompts task-wise.
2. Backward Iterative Evolution: LLMs meta-optimize prompt policies using centralized feedback.
3. Swarm Integration: Aggregation of cognitive signatures; population-level distillation of strategies.
4. Subclass Brain Registry (SBR): Consolidates cross-user knowledge and solution traces.
5. Meta-Level Distillation and Policy Update: Propagation of generalized and high-fitness heuristics.

Figure 1 in the paper (not shown here) sketches the information flow and system logic: a closed-loop, multi-layered structure coupling persistent user-model adaptation with swarm-based aggregation and collective refinement.

7. Implications, Applications, and Perspectives

The Superclass Brain framework represents an emergent, scalable collective intelligence—precisely described as a meta-intelligent system produced by bidirectional evolution and swarm-level synthesis of user–LLM dyads (Weigang et al., 30 Aug 2025). Its architectural features support:

• Scalability: Aggregation of millions of Subclass Brains and continuous absorption of new cognitive signatures.
• Explainability and Diversity: Explicit tracking and control over heuristics, cognitive features, and solution patterns.
• Ethical Alignment: Centralized and federated registries assist with compliance, safety, and transparency.

A plausible implication is that such architectures can generalize to further domains—including but not limited to AI-driven scientific discovery, societal decision-making, and complex system management—by leveraging swarm-aligned dyads and meta-level pattern distillation. The formalism provides a foundational roadmap for explainable, continually improving, and ethically tractable collective AI.