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。
AI Safety
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.
Sparrow 論文：用「規則條件獎勵模型」訓練可信賴對話 Agent
CI/CD 流水線又紅了。你切回日誌，發現模型自信地編造了一個不存在的 API endpoint。DeepMind 的 Sparrow 論文提出 RCRM 架構，透過規則條件獎勵模型與證據鏈，讓模型在生成時自動驗證來源。這不是讓模型變聰明，而是讓它學會如何證明答案是對的。
Sparrow Paper: Training Trustworthy Dialogue Agents with Rule-Conditional Reward Models
The CI/CD pipeline is red again. You switch back to the logs and find the model confidently inventing an API endpoint that does not exist. DeepMind’s Sparrow paper proposes an RCRM architecture that uses rule-conditional reward models and evidence chains to let the model automatically verify sources while generating. This is not about making the model smarter. It is about teaching it how to prove that its answer is correct.
Constitutional AI: 如何透過 AI Feedback 實現自動化安全治理
標註員 A 給 5 分，B 給 1 分。同一個回答。傳統 RLHF 的瓶頸不在成本，而在訊號雜訊比。本文解析 Constitutional AI 如何透過 AI Feedback 建立自動化安全邊界，以及落地時需要評估的成本結構。
Constitutional AI: How AI Feedback Enables Automated Safety Governance
Annotator A gives it a 5, annotator B gives it a 1. The same answer. The bottleneck in traditional RLHF is not cost, but the signal-to-noise ratio. This article breaks down how Constitutional AI uses AI Feedback to build automated safety boundaries, and the cost structure teams need to evaluate when putting it into practice.
AGI 的維度：如何透過能力與自主度評估 AI 風險
當 AI 標籤被行銷用語模糊化時，工程師該如何評估風險？本文透過 Performance 與 Autonomy 的正交維度，提供一套實用的部署風險矩陣。
AGI Dimensions: How to Assess AI Risk Through Capability and Autonomy
When AI labels are blurred by marketing language, how should engineers assess risk? This article uses the orthogonal dimensions of Performance and Autonomy to provide a practical deployment risk matrix.