The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models

This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automating complex analysis workflows, with fundamental investment analysis as a primary use case. This domain poses major challenges: high structural uncertainty, noisy and subjective data, tight attention budgets, and the need for justified, reproducible decisions. Human analysts often struggle in this domain due to cognitive biases and limitations, suggesting significant value in automation. But while LLM-based agents have been proposed as analytical aids, their limitations -- poor numerical reasoning, unawareness of uncertainty, and lack of reproducibility -- hinder their effectiveness in this context. CRISTAL addresses these gaps through a principled blend of statistical model synthesis, continuous learning, and active learning. Starting from a natural-language prior knowledge curriculum, CRISTAL builds a dynamic, interpretable probabilistic program that enables full Bayesian inference, including uncertainty quantification and budget-aware data acquisition. CRISTAL continually refines its world model during analysis, leveraging LLMs for code synthesis and learning. We validate CRISTAL on a novel benchmark of synthetic equities with rich financial and textual data. On a company classification task, CRISTAL achieves Bayes-optimal accuracy with just 5 examples and a 5-second budget, outperforming state-of-the-art LLMs that plateau around 40\% accuracy even with order-of-magnitude more input data and compute.

Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping Multi-Agent Study

We investigate the Free Energy Principle as a foundation for measuring risk in agentic and multi-agent systems. From these principles we introduce a Cumulative Risk Exposure metric that is flexible to differing contexts and needs. We contrast this to other popular theories for safe AI that hinge on massive amounts of data or describing arbitrarily complex world models. In our framework, stakeholders need only specify their preferences over system outcomes, providing straightforward and transparent decision rules for risk governance and mitigation. This framework naturally accounts for uncertainty in both world model and preference model, allowing for decision-making that is epistemically and axiologically humble, parsimonious, and future-proof. We demonstrate this novel approach in a simplified autonomous vehicle environment with multi-agent vehicles whose driving policies are mediated by gatekeepers that evaluate, in an online fashion, the risk to the collective safety in their neighborhood, and intervene through each vehicle's policy when appropriate. We show that the introduction of gatekeepers in an AV fleet, even at low penetration, can generate significant positive externalities in terms of increased system safety.