Sparse Autoencoders (SAEs)

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  "dateCreated": "2026-02-15",
  "summary": "Comprehensive review of sparse autoencoders (SAEs) for mechanistic interpretability, covering Anthropic's 34M features from Claude 3 Sonnet (90% interpretability), OpenAI's 16M latent GPT-4 SAEs, DeepMind's 1T+ parameter Gemma Scope releases, and Goodfire's \\$50M Series A and 671B DeepSeek R1 SAEs. Despite promising safety applications including deception detection features, DeepMind's March 2025 negative results showed SAEs underperforming simple probes on downstream tasks. Global investment estimated at \\$75-150M/year with 150-200 researchers.",
  "description": "Sparse autoencoders extract interpretable features from neural network activations using sparsity constraints.",
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    {
      "text": "Anthropic 2024",
      "url": "https://transformer-circuits.pub/2024/scaling-monosemanticity/",
      "resourceId": "e724db341d6e0065",
      "resourceTitle": "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet"
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    {
      "text": "DeepMind deprioritized",
      "url": "https://deepmindsafetyresearch.medium.com/negative-results-for-sparse-autoencoders-on-downstream-tasks-and-deprioritising-sae-research-6cadcfc125b9",
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      "url": "https://arxiv.org/abs/2309.08600",
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      "resourceTitle": "Sparse Autoencoders Find Highly Interpretable Features in Language Models"
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      "text": "Anthropic",
      "url": "https://transformer-circuits.pub/2024/scaling-monosemanticity/",
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      "resourceTitle": "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet"
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      "text": "OpenAI",
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      "resourceTitle": "Extracting Concepts from GPT-4"
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      "text": "DeepMind",
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      "text": "Gated SAE",
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      "text": "The landmark result",
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      "text": "Gemma Scope 2",
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      "resourceTitle": "Gemma Scope 2: Helping the AI Safety Community Deepen Understanding of Complex Language Model Behavior"
    },
    {
      "text": "DeepMind deprioritization",
      "url": "https://deepmindsafetyresearch.medium.com/negative-results-for-sparse-autoencoders-on-downstream-tasks-and-deprioritising-sae-research-6cadcfc125b9",
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    {
      "text": "Protein language model SAEs",
      "url": "https://www.pnas.org/doi/10.1073/pnas.2506316122",
      "resourceId": "4d1186e8c443a9a9",
      "resourceTitle": "Sparse autoencoders uncover biologically interpretable features in protein language model representations"
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      "text": "DeepMind's March 2025 announcement",
      "url": "https://deepmindsafetyresearch.medium.com/negative-results-for-sparse-autoencoders-on-downstream-tasks-and-deprioritising-sae-research-6cadcfc125b9",
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      "text": "Anthropic 2024",
      "url": "https://transformer-circuits.pub/2024/scaling-monosemanticity/",
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      "text": "DeepMind 2024",
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      "text": "Scaling Monosemanticity",
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      "text": "GPT-4 SAEs",
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      "text": "negative results",
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      "text": "Automated interpretation",
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    {
      "text": "Original SAE paper",
      "url": "https://arxiv.org/abs/2309.08600",
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      "resourceTitle": "Sparse Autoencoders Find Highly Interpretable Features in Language Models"
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      "text": "Scaling Monosemanticity",
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      "text": "Gemma Scope 2",
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      "text": "Negative Results for SAEs on Downstream Tasks",
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