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Axbench: Steering llms? even simple baselines outperform sparse autoencoders

10 Pith papers cite this work. Polarity classification is still indexing.

10 Pith papers citing it

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WriteSAE: Sparse Autoencoders for Recurrent State

cs.LG · 2026-05-12 · unverdicted · novelty 8.0

WriteSAE introduces sparse autoencoders with rank-1 matrix atoms for recurrent state updates, allowing replacement tests that outperform deletion on 92.4% of positions and a formula predicting logit changes with R²=0.98.

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders

cs.LG · 2026-06-13 · unverdicted · novelty 7.0

Cosine-scored SAEs with a learned direction-magnitude blend learn more concept-aligned features than standard inner-product SAEs at matched reconstruction quality.

Are Sparse Autoencoder Benchmarks Reliable?

cs.LG · 2026-05-18 · unverdicted · novelty 6.0

An audit of SAEBench reveals that Targeted Probe Perturbation and Spurious Correlation Removal metrics fail reliability tests and should not be used to evaluate sparse autoencoders.

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