InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

Yifan Luo; Zhennan Zhou; Bin Dong

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arXiv · 2506.07406

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

Yifan Luo·Zhennan Zhou·Bin Dong

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Abstract

Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research. Existing feature interpretability methods often rely on strong structural assumptions--such as linearity or sparsity--that may not hold in practice. In this work, we introduce InverseScope, an assumption-light and scalable framework for interpreting neural activations via input inversion. Given a target activation, InverseScope characterizes its encoded information by generating natural-language inputs that produce nearby activations, grounding abstract internal states in concrete language. To overcome the prohibitive cost of sampling in high-dimensional activation spaces, we propose a novel control-layer conditioning architecture that substantially improves sample efficiency compared to prior token-prepending approaches. We demonstrate that InverseScope reveals rich geometric structure in LLM representation spaces, including sentence-level linear analogies. The framework scales to state-of-the-art open-source models of up to 14B parameters and generalizes to out-of-distribution inputs, enabling systematic analysis of activation neighborhoods.

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BibTeX RIS

Yifan Luo, Zhennan Zhou, Bin Dong. 2026-07-06. InverseScope: Scalable Activation Inversion for Interpreting Large Language Models. https://arxiv.org/abs/2506.07406

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