Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines?
Sparse Autoencoders (SAEs) have emerged as a promising tool for interpreting neural networks by decomposing their activations into sparse sets of human-interpretable features. Recent work has introduced multiple SAE variants and successfully scaled them to frontier models. Despite much excitement, a growing number of negative results in downstream tasks casts doubt on whether SAEs recover meaningful features. To directly investigate this, we perform two complementary evaluations. On a synthetic setup with known ground-truth features, we demonstrate that SAEs recover only 9% of true features despite achieving 71% explained variance, showing that they fail at their core task even when reconstruction is strong. To evaluate SAEs on real activations, we introduce three baselines that constrain SAE feature directions or their activation patterns to random values. Through extensive experiments across multiple SAE architectures, we show that our baselines match fully-trained SAEs in interpretability (0.87 vs 0.90), sparse probing (0.69 vs 0.72), and causal editing (0.73 vs 0.72). Together, these results suggest that SAEs in their current state do not reliably decompose models' internal mechanisms.
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Anton Korznikov
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2026-02-18T13:37:45.391Z
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Anton Korznikov
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AntonKorznikov
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Andrey Galichin
admin_assigned
2026-02-18T13:37:51.000Z
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Andrey Galichin
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andreuka18
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Alexey Dontsov
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2026-02-18T13:37:38.913Z
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Alexey Dontsov
therem
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therem
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Oleg Rogov
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2026-02-18T13:37:59.681Z
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Oleg Rogov
Olegario228
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Olegario228
69959cbbed493589ceb5be35
Ivan Oseledets
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Elena Tutubalina
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2026-02-18T12:30:12.235Z
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Elena Tutubalina
tlenusik
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tlenusik
Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines?
Sparse Autoencoders (SAEs) have emerged as a promising tool for interpreting neural networks by decomposing their activations into sparse sets of human-interpretable features. Recent work has introduced multiple SAE variants and successfully scaled them to frontier models. Despite much excitement, a growing number of negative results in downstream tasks casts doubt on whether SAEs recover meaningful features. To directly investigate this, we perform two complementary evaluations. On a synthetic setup with known ground-truth features, we demonstrate that SAEs recover only 9% of true features despite achieving 71% explained variance, showing that they fail at their core task even when reconstruction is strong. To evaluate SAEs on real activations, we introduce three baselines that constrain SAE feature directions or their activation patterns to random values. Through extensive experiments across multiple SAE architectures, we show that our baselines match fully-trained SAEs in interpretability (0.87 vs 0.90), sparse probing (0.69 vs 0.72), and causal editing (0.73 vs 0.72). Together, these results suggest that SAEs in their current state do not reliably decompose models' internal mechanisms.
Sparse Autoencoders fail to reliably decompose neural network internals despite strong reconstruction performance, as demonstrated through synthetic and real activation evaluations.
Sparse Autoencoders neural networks activations explained variance interpretability sparse probing causal editing