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• Automatically Finding Reward Model Biases
• Inference-Time Toxicity Mitigation in Protein Language Models via Logit-Diff Amplification
• Biases in the Blind Spot: Detecting What LLMs Fail to Mention
• Measuring Chain-of-Thought Monitorability Through Faithfulness and Verbosity
• Base Models Know How to Reason, Thinking Models Learn When
• Chain-of-Thought Is Not Explainability
• MIB: A Mechanistic Interpretability Benchmark
• Chain-of-Thought Reasoning In The Wild Is Not Always Faithful
• Understanding Reasoning in Thinking Language Models via Steering Vectors
• InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques