當模型自信地輸出錯誤：價值軸線如何編碼生成軌跡
Anthropic Fellows 研究在 Qwen3-8B 中發現一條線性方向，其投影與信心、回溯及程式碼正確性相關。理解這條軸線與 DPO 訓練的交互作用，有助於評估 Eval Awareness 風險與模型行為邊界。
Interpretability
When Models Confidently Produce Errors: How the Value Axis Encodes Generation Trajectories
Anthropic Fellows research found a linear direction in Qwen3-8B whose projection is associated with confidence, backtracking, and code correctness. Understanding how this axis interacts with DPO training helps assess Eval Awareness risks and the boundaries of model behavior.
SAE 特徵提取：從殘差流看見模型內部表示
Anthropic 的研究團隊在 Claude 3 Sonnet 中訓練了高達 3400 萬個特徵的稀疏編碼器（SAE），提取出可解釋的特徵表示。研究顯示能對模型輸出行為產生因果影響，為 AI 安全治理提供了可研究的內部訊號與潛在工具，但忠實度仍需驗證。
SAE Feature Extraction: Seeing Model Representations Through the Residual Stream
Anthropic’s research team trained sparse autoencoders (SAEs) with up to 34 million features in Claude 3 Sonnet, extracting interpretable feature representations. The research shows causal effects on model output behavior, providing internal signals and potential tools that can be studied for AI safety governance, though faithfulness still requires validation.
Toy Models of Superposition：神經網路的特徵壓縮術
打開 IDE。你觀察隱藏層的單一神經元。它同時響應了 ‘Python’ 和 ‘錯誤處理’。這不是 bug，這是特徵壓縮。Anthropic 的 Toy Models 研究揭示特徵稀疏性如何驅動「疊加（Superposition）」現象，解釋模型如何在容量限制下儲存更多概念。
Toy Models of Superposition: Feature Compression in Neural Networks
Open your IDE. You observe a single hidden-layer neuron. It responds to both “Python” and “error handling.” This is not a bug; it is feature-level compression. Anthropic’s Toy Models research reveals how feature sparsity drives “superposition,” explaining how models store more concepts under capacity constraints.
Claude 3 Sonnet 的可解釋性突破：利用 SAE 實現模型特徵的精確操控
Prompt 調整遇到邊界？本文探討如何透過稀疏自編碼器（SAE），從模型內部拆解出獨立特徵，並直接調整特徵活化值，實現比 Prompt Engineering 更精確的模型行為操控。
Claude 3 Sonnet’s Interpretability Breakthrough: Using SAEs for Precise Control of Model Features
Have you hit the limits of prompt tuning? This piece looks at how sparse autoencoders, or SAEs, decompose independent features from inside a model and directly adjust feature activations, enabling more precise control of model behavior than prompt engineering.