mlsanityData to 14 Oct 2026

Refit the Probe: Single-Direction Ablation Is Not a Necessity Test

arXiv 2606.00926 ↗First posted 30 May 2026cs.LG cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Probes are routinely paired with an intervention: ablate the direction the probe found, run the model, and read the change in task accuracy, taking a large drop as evidence that the computation depends on what the probe read and a near-zero drop as evidence that it does not. Either inference requires that the ablation have removed the target from the layer. We find that the ablation does not remove what it targets. A probe refitted on the ablated activations recovers its original accuracy in every cell we test, and keeps recovering when the probe's entire row space is deleted rather than a single axis, because the quantity survives in the orthogonal complement. Because a refitted probe recovers, neither a large task drop nor a near-zero one establishes whether the model needed the target, and one probe fit detects this. Replacing the ablation with iterative nullspace projection, scored against random subspaces of matched dimension, reverses the conclusion: representations that looked causally inert carry most of the task. The correction also separates where a variable is most readable from where deleting it does most damage, and those are not the same layer in any pretrained model we study. The erasure is defined by a linear probe family, so removing a nonlinearly encoded quantity remains open.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 49 of its 56 references that are in the map. First posted on arXiv 30 May 2026.

Builds on

49 of 56 references.

References · 49

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  5. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  6. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  7. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,786
  8. 2016Understanding intermediate layers using linear classifier probescited by 1,353
  9. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,336
  10. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,165
  11. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  12. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
37 more
  1. 2023Progress measures for grokking via mechanistic interpretabilitycited by 839
  2. 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 654
  3. 2019Designing and Interpreting Probes with Control Taskscited by 640
  4. 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projectioncited by 455
  5. 2021Causal Abstractions of Neural Networkscited by 422
  6. 2024Parallelizing Linear Transformers with the Delta Rule over Sequence Lengthcited by 396
  7. 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 359
  8. 2016Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependenciescited by 354
  9. 2019Theoretical Limitations of Self-Attention in Neural Sequence Modelscited by 288
  10. 2018Analysis Methods in Neural Language Processing: A Surveycited by 277
  11. 2022Transformers Learn Shortcuts to Automatacited by 268
  12. 2023Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Modelscited by 243
  13. 2022Neural Networks and the Chomsky Hierarchycited by 217
  14. 2022Exploring Length Generalization in Large Language Modelscited by 206
  15. 2023Zoology: Measuring and Improving Recall in Efficient Language Modelscited by 204
  16. 2024The Illusion of State in State-Space Modelscited by 187
  17. 2023What Formal Languages Can Transformers Express? A Surveycited by 124
  18. 2018On the Practical Computational Power of Finite Precision RNNs for Language Recognitioncited by 117
  19. 2021Self-Attention Networks Can Process Bounded Hierarchical Languagescited by 105
  20. 2024Unlocking State-Tracking in Linear RNNs Through Negative Eigenvaluescited by 101
  21. 2026Mamba-3: Improved Sequence Modeling using State Space Principlescited by 89
  22. 2023Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patchingcited by 80
  23. 2023Exposing Attention Glitches with Flip-Flop Language Modelingcited by 75
  24. 2020RNNs can generate bounded hierarchical languages with optimal memorycited by 59
  25. 2022Kernelized Concept Erasurecited by 48
  26. 2024The Expressive Capacity of State Space Models: A Formal Language Perspectivecited by 46
  27. 2019LSTM Networks Can Perform Dynamic Countingcited by 45
  28. 2024Towards Universality: Studying Mechanistic Similarity Across Language Model Architecturescited by 31
  29. 2023Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammarscited by 30
  30. 2025Understanding and Improving Length Generalization in Recurrent Modelscited by 23
  31. 2025Mamba Modulation: On the Length Generalization of Mambacited by 16
  32. 2025Structured Sparse Transition Matrices to Enable State Tracking in State-Space Modelscited by 16
  33. 2024On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languagescited by 15
  34. 2025Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanismcited by 15
  35. 2023Pre-registration for Predictive Modelingcited by 13
  36. 2025Mechanistic evaluation of Transformers and state space modelscited by 9
  37. 2025On Vanishing Variance in Transformer Length Generalizationcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026A Theory of Conditional Collapse under Low-Rank Weight-Space Ablations: I. The Single-Block Theory and Synthetic Validation
  2. 2026Decodability is Not Causality: Dissociating Probe Readouts from Behavioral Drivers via SAE Decomposition
  3. 2026Lagged Coupling: Internal Representations Become Readable Before They Become Causal
  4. 2026A Circuit, Not The Circuit: Non-Unique Causal Localisation of the Mamba-2 State Sink
  5. 2026Do Activation Monitors Survive Model Updates? Benchmarking, Predicting, and Repairing Activation-Monitor Staleness
  6. 2025Spectral Identifiability for Interpretable Probe Geometry
  7. 2026Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States
  8. 2026Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models