Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks

2025-09-08

AI Summary

This paper addresses the problem of modeling latent agentic substructures within deep neural networks, aiming to formalize how subagents aggregate into coherent high-level entities and analyzing implications for AI alignment. Methodologically, it introduces a probabilistic agent-theoretic framework grounded in weighted log-pooling to ensure strict consistency, proving that ternary or higher-dimensional outcome spaces are necessary for genuine consensus. Integrating the logarithmic scoring rule, clone-invariance analysis, skewness testing, and recursive modeling, the work identifies mechanisms inducing “Luigi–Waluigi”-style antagonistic personality splits. Key contributions include: (i) the first formal characterization of explicit guidance and suppression strategies for correcting first-order misalignment; and (ii) theoretical validation that sequentially eliciting and then suppressing the Waluigi persona outperforms direct reinforcement of the benign (Luigi) persona—providing a testable foundation for understanding and controlling personality emergence in large language models.

Technology Category

• Multiagent Systems: Adversarial Agents
• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

We develop a theory of intelligent agency grounded in probabilistic modeling for neural models. Agents are represented as outcome distributions with epistemic utility given by log score, and compositions are defined through weighted logarithmic pooling that strictly improves every member's welfare. We prove that strict unanimity is impossible under linear pooling or in binary outcome spaces, but possible with three or more outcomes. Our framework admits recursive structure via cloning invariance, continuity, and openness, while tilt-based analysis rules out trivial duplication. Finally, we formalize an agentic alignment phenomenon in LLMs using our theory: eliciting a benevolent persona ("Luigi'") induces an antagonistic counterpart ("Waluigi"), while a manifest-then-suppress Waluigi strategy yields strictly larger first-order misalignment reduction than pure Luigi reinforcement alone. These results clarify how developing a principled mathematical framework for how subagents can coalesce into coherent higher-level entities provides novel implications for alignment in agentic AI systems.

Problem

• Modeling intelligent agency in neural networks probabilistically
• Proving strict unanimity possible with three or more outcomes
• Formalizing agentic alignment phenomena in large language models

Innovation

• Probabilistic modeling of agentic substructures in neural networks
• Weighted logarithmic pooling for agent composition
• Formalizing agentic alignment phenomena in LLMs