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Can Rager

5 accepted papers

2026

Priors in time: Missing inductive biases for language model interpretability

ICLR 2026poster

A central aim of interpretability tools applied to language models is to recover meaningful concepts from model activations. Existing feature extraction methods focus on single activations regardless of the context, implicitly assuming independence (and therefore stationarity). This leaves open whet…

Cited by 0SourcecodeScholar
2025

NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals

ICLR 2025poster

We introduce NNsight and NDIF, technologies that work in tandem to enable scientific study of the representations and computations learned by very large neural networks. NNsight is an open-source system that extends PyTorch to introduce deferred remote execution. The National Deep Inference Fabric (…

2025

SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability

ICML 2025poster

Sparse autoencoders (SAEs) are a popular technique for interpreting language model activations, and there is extensive recent work on improving SAE effectiveness. However, most prior work evaluates progress using unsupervised proxy metrics with unclear practical relevance. We introduce SAEBench, a c…

Cited by 0SourcePDFScholar
2025

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

ICLR 2025oral

We introduce methods for discovering and applying **sparse feature circuits**. These are causally implicated subnetworks of human-interpretable features for explaining language model behaviors. Circuits identified in prior work consist of polysemantic and difficult-to-interpret units like attention…

2024

Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models

NeurIPS 2024poster

What latent features are encoded in language model (LM) representations? Recent work on training sparse autoencoders (SAEs) to disentangle interpretable features in LM representations has shown significant promise. However, evaluating the quality of these SAEs is difficult because we lack a ground-t…