Physics Steering: Causal Control of Cross-Domain Concepts in a Physics Foundation Model

TL;DR

This paper demonstrates that a physics-focused foundation model can be causally steered by manipulating internal representations, revealing it learns general physical principles beyond superficial patterns, enabling scientific discovery.

Contribution

It introduces a method to identify and manipulate concept directions in activation space of a physics foundation model, enabling causal control over physical behaviors.

Findings

Concept directions encode specific physical features.

Manipulating these directions can induce or remove physical behaviors.

The model learns generalized physical principles, not just superficial patterns.

Abstract

Recent advances in mechanistic interpretability have revealed that large language models (LLMs) develop internal representations corresponding not only to concrete entities but also distinct, human-understandable abstract concepts and behaviour. Moreover, these hidden features can be directly manipulated to steer model behaviour. However, it remains an open question whether this phenomenon is unique to models trained on inherently structured data (ie. language, images) or if it is a general property of foundation models. In this work, we investigate the internal representations of a large physics-focused foundation model. Inspired by recent work identifying single directions in activation space for complex behaviours in LLMs, we extract activation vectors from the model during forward passes over simulation datasets for different physical regimes. We then compute "delta" representations between the two regimes. These delta tensors act as concept directions in activation space, encoding specific physical features. By injecting these concept directions back into the model during inference, we can steer its predictions, demonstrating causal control over physical behaviours, such as inducing or removing some particular physical feature from a simulation. These results suggest that scientific foundation models learn generalised representations of physical principles. They do not merely rely on superficial correlations and patterns in the simulations. Our findings open new avenues for understanding and controlling scientific foundation models and has implications for AI-enabled scientific discovery.