Abstract
Hybrid human–AI decision systems increasingly rely on collective intelligence over social graphs, yet existing approaches suffer from fundamental limitations. Classical Wisdom of Crowds methods degrade under correlated biases and network effects, while single-model large language model (LLM) reasoning lacks the multi-timescale structure and stability required for institutional decision-making.
We introduce the Sangha Dhamma Engine (SDE), a quantum-inspired framework for coherence-gated institutional reasoning and action. SDE models institutions as latent dynamical systems in which beliefs, actions, and values evolve over social graphs under structured interventions.
The system integrates (i) hierarchical LLM reasoning layers operating across multiple temporal scales, (ii) state-space–based action dynamics using Mamba-style linear-time propagation for scalable institutional memory and actuation, and (iii) a Latent Wisdom Orchestrator (LWO) that enforces epistemic coherence and value alignment. Together, these components transform social graphs into action-augmenting latent fields, enabling systematic reasoning beyond agent-centric or token-based paradigms.
The Framework
We formalize Wisdom of Crowds as an emergent, quantum-like phenomenon arising from superposed belief states constrained by coherence and consensus rules inspired by blockchain governance. Information is treated as a fundamental organizing force shaping belief distributions and institutional behavior drift.
SDE introduces diagnostic signals such as belief churn as a grokking indicator, and stability metrics based on basin resilience under perturbations. We present an end-to-end institutional intelligence platform (iPaaS) supporting counterfactual policy simulation, episodic memory–based action verification, and adversarial intervention testing.
Empirical simulations demonstrate linear-time scalability over social graphs and basin stability exceeding 85% under adversarial perturbations. SDE provides a foundation for deployable, value-aligned institutional intelligence across governance, education, and enterprise memory systems.
Key Contributions
This framework makes several groundbreaking contributions to the field of collective intelligence and institutional reasoning:
- Institutional Reasoning as a Latent Dynamical System: A formal framework that models institutions as latent belief–action fields evolving over social graphs, shifting collective intelligence from agent-centric aggregation to system-level dynamics.
- Quantum-Inspired Reformulation of Wisdom of Crowds: Reinterprets Wisdom of Crowds as an emergent, quantum-like phenomenon characterized by superposition, interference, and coherence of belief states, constrained by consensus mechanisms inspired by blockchain and smart contracts.
- Hierarchical Multi-Timescale Reasoning Architecture: Institutional Action in Quantum Layers—a hierarchical reasoning structure combining multi-headed LLM inference with temporal abstraction across tactical, operational, strategic, and normative horizons.
- Linear-Time Institutional Actuation via State-Space Models: The Action Flow Matrix, a Mamba-inspired state-space actuator enabling O(n) propagation of institutional state and memory over large social graphs, overcoming token-based scaling bottlenecks.
- Coherence-Gated Collective Intelligence (LWO): The Latent Wisdom Orchestrator, a coherence-limiting mechanism that stabilizes belief evolution, enforces value alignment, and prevents collapse under correlated biases and adversarial perturbations.
Novel Diagnostics and Verification
SDE introduces innovative approaches to measuring and validating institutional intelligence:
- Churn-as-Grokking Diagnostic for Collective Learning: Identifies belief churn as a measurable signal of collective phase transitions (grokking) and introduces metrics for detecting epistemic instability versus genuine learning in institutional systems.
- Action-Verified Institutional Memory and Policy Evaluation: An ecosystem-model–based policy verifier that uses episodic memory and action outcomes to validate beliefs, enabling counterfactual simulation, adversarial testing, and systemic risk analysis.
Institutional Reasoning as a Latent Dynamical System
We introduce a formal framework that models institutions as latent belief–action fields evolving over social graphs. This approach fundamentally shifts collective intelligence from agent-centric aggregation to system-level dynamics.
Rather than treating institutional knowledge as a simple aggregation of individual beliefs, SDE models the institution itself as a dynamical system where beliefs, values, and actions exist in a latent space that evolves according to structured rules and interventions.
This paradigm shift enables us to reason about institutional behavior as emergent phenomena arising from the interaction of multiple agents and their shared context, rather than as a mere sum of individual contributions.
Quantum-Inspired Wisdom of Crowds
The classical Wisdom of Crowds assumes independence and diversity of opinion. However, in real social networks, beliefs are correlated through network effects, social influence, and shared information environments.
SDE reinterprets collective intelligence through a quantum-inspired lens: belief states exist in superposition until measured (consensus), and the act of observation (voting, deliberation) collapses the collective wavefunction into a definite institutional position.
This framework naturally accounts for interference effects (how beliefs reinforce or cancel), coherence (how long beliefs remain stable), and decoherence (how external noise and internal disagreement destabilize consensus). Blockchain-inspired consensus mechanisms provide the measurement apparatus that collapses superposed states into actionable decisions.
Multi-Timescale Hierarchical Reasoning
Institutions operate across multiple temporal scales simultaneously: tactical decisions (minutes to hours), operational planning (days to weeks), strategic direction (months to quarters), and normative evolution (years to decades).
The Institutional Action in Quantum Layers architecture implements this through a hierarchy of LLM reasoning modules, each operating at different temporal resolutions. Lower layers handle immediate tactical responses, while higher layers integrate context over longer horizons and enforce value alignment.
This multi-timescale approach prevents the myopia of single-step reasoning while maintaining the agility to respond to immediate challenges. It also provides natural interfaces for human oversight at different organizational levels.
Linear-Time Action Propagation
Traditional token-based LLM approaches scale poorly for institutional memory over large social graphs. The Action Flow Matrix addresses this through Mamba-inspired state-space models that propagate institutional state in O(n) time.
By treating institutional memory as a continuous state rather than discrete tokens, SDE can efficiently maintain context over thousands of participants and millions of interactions. This enables real-time institutional intelligence at scales previously impossible with transformer-based architectures.
The Latent Wisdom Orchestrator
The LWO serves as the coherence gate for the entire system. It monitors belief evolution across the social graph, detecting when the system approaches dangerous attractors (groupthink, polarization, value drift) and applies targeted interventions to restore stability.
Key mechanisms include epistemic diversity enforcement (ensuring heterogeneous viewpoints remain represented), value alignment verification (checking that evolving beliefs remain consistent with institutional values), and adversarial robustness testing (probing the system's resilience to manipulation).
Under adversarial perturbations designed to exploit network effects and correlated biases, SDE maintains basin stability exceeding 85%, demonstrating robust collective intelligence even in hostile information environments.
Applications and Deployment
SDE functions as an institutional intelligence platform-as-a-service (iPaaS), enabling deployment across diverse domains:
- Governance: Democratic deliberation, policy simulation, and collective decision-making with formal guarantees on epistemic quality and value alignment.
- Education: Institutional learning systems that track collective knowledge evolution, identify learning phase transitions, and optimize pedagogical interventions.
- Enterprise Memory: Organizational knowledge systems that maintain coherent institutional memory, support counterfactual reasoning, and enable systematic policy evaluation.
Future Directions
The Sangha Dhamma Engine opens new research directions at the intersection of collective intelligence, quantum-inspired computation, and institutional design. Future work will explore formal verification of value alignment, integration with decentralized autonomous organizations (DAOs), and extension to multi-institutional ecosystems.
By treating institutions as first-class computational objects with their own dynamics, memory, and reasoning capabilities, SDE provides a foundation for the next generation of hybrid human–AI systems that preserve human agency while amplifying collective intelligence.