Hypothesis Generation

This paper explores how language models generate and refine internal hypotheses while constructing world models, aiming to bridge their latent reasoning with structured external representations. By analyzing iterative hypothesis cycles, we investigate whether fundamental system rules emerge from latent space dynamics and propose methods to extract and refine these representations for structured reasoning.

A robust framework for reasoning requires more than memorization; it must dynamically form and refine hypotheses. Inspired by theorem-proving frameworks, I propose a dynamic database with static relationships and evolving entities, enabling hypothesis cycles and similar link prediction. This method allows LLMs to infer hidden relationships across subsystems, addressing challenges in AI-driven scientific discovery and decision-making.

This blog presents my research and engineering efforts in language model reasoning, abstract representation of linked entities, and link prediction. It explores my work at BMZ, where I developed agentic multi-hop reasoning systems for policy decisions, and how this experience led me to investigate hidden relationships in complex datasets. Through Swan AI, I examined whether language models can learn, predict, and represent unseen links in dynamic databases. The blog discusses experiments, insights from ARC-AGI, agency in dynamic learning pipelines, and the role of hypothesis cycles in continual learning, culminating in a proposed framework for link prediction and adaptive data representation.