Adversarial Examples Are Not Bugs, They Are Superposition - 惯性聚合

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Models know when they’re reward hacking — and we can catch them at scale - Goodfire Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation Steering Along Manifolds to Control Neural Networks Uncovering Neural Geometry in Vision Models With Block-Sparse Featurizers Uncovering Neural Geometry in Vision Models With Block-Sparse Featurizers Meandering on Manifolds: The Neural Geometry of Stories Over Time The Neural Geometry Series Can SAEs Capture Neural Geometry? A Geometric Calculator Inside a Neural Network Meandering on Manifolds: The Neural Geometry of Stories Over Time Interpreting Language Model Parameters Predictive Data Debugging: Reveal and Shape What Your Model Learns, Before You Train Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention Predictive Data Debugging: Reveal and Shape What Your Model Learns, Before You Train Logits as a new monitor for evaluation awareness Predicting Rare LLM Failures with 30× Fewer Rollouts Logits as a new monitor for evaluation awareness Predicting Rare LLM Failures with 30× Fewer Rollouts The Shape of Stories Inside Neural Networks Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention Can SAEs Capture Neural Geometry? Steering Along Manifolds to Control Neural Networks A Geometric Calculator Inside a Neural Network The Neural Geometry Series The World Inside Neural Networks The World Inside Neural Networks Verbalized Eval Awareness Inflates Measured Safety Verbalized Eval Awareness Inflates Measured Safety Paper Summary: Interpreting Language Model Parameters Paper Summary: Interpreting Language Model Parameters

Adversarial Examples Are Not Bugs, They Are Superposition

Liv Gorton, Owen Lewis, · 2025-12-05 · via Goodfire Research

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