Distributed Mechanistic Interpretability at Scale: Activation Streaming, Split-Layer Inference, and Distributed Sparse Autoencoder Training

ActStream: a two-node system that splits inference and analysis, streams intermediate activations over a binary wire protocol, and trains sparse autoencoders without staging activations to disk. Thunderbolt measured at 2.150 GB/s, 86% of negotiated rate and 26x the LAN path, with p99 latency within 1.5x of median. Includes a feature-based safety classifier evaluated against an LLM-judge baseline on PKU-SafeRLHF.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-11
DOI
https://doi.org/10.5281/zenodo.22699164
Primary Topic
Adversarial Robustness in Machine Learning
Type
preprint
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preprint

Distributed Mechanistic Interpretability at Scale: Activation Streaming, Split-Layer Inference, and Distributed Sparse Autoencoder Training

J. Melton
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
preprint

Distributed Mechanistic Interpretability at Scale: Activation Streaming, Split-Layer Inference, and Distributed Sparse Autoencoder Training

J. Melton
preprint en

Abstract

ActStream: a two-node system that splits inference and analysis, streams intermediate activations over a binary wire protocol, and trains sparse autoencoders without staging activations to disk. Thunderbolt measured at 2.150 GB/s, 86% of negotiated rate and 26x the LAN path, with p99 latency within 1.5x of median. Includes a feature-based safety classifier evaluated against an LLM-judge baseline on PKU-SafeRLHF.

Zenodo (CERN European Organization for Nuclear Research)
American Standard (United States) (US)
Adversarial Robustness in Machine Learning
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