Causal Interventions in Language Models

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Altered Thoughts, Altered Actions: Reasoning Chain as Control Surface for a Vision-Language-Action Policy
Mar 13, 2026Tuan Duong Trinh, Basim Azam, Mohammed Ishaq Ansari +2Language-Conditioned Robot Manipulation Vision-Language-Action Models
University of Melbourne Parkville, VIC, Australia
From Refusal Tokens to Refusal Control: Discovering and Steering Category-Specific Refusal Directions
Mar 9, 2026Rishab Alagharu, Ishneet Sukhvinder Singh, Shaibi Shamsudeen +2Language Model Steering LLM Refusal Behavior
Algoverse AI Research · Carnegie Mellon University · University of Maryland
Patches of Nonlinearity: Instruction Vectors in Large Language Models
Feb 8, 2026Irina Bigoulaeva, Jonas Rohweder, Subhabrata Dutta +1Transformer Interpretability LLM Interpretability
Ubiquitous Knowledge Processing Lab (UKP Lab) · Department of Computer Science, Technical University of Darmstadt · National Research Center for Applied Cybersecurity ATHENE, Germany
Towards Isolated Interventions via Almost Orthogonal Features in Language Models
Feb 4, 2026Moritz Miller, Florent Draye, Bernhard SchölkopfDisentangled Representation Learning Representation Learning
Max Planck Institute for Intelligent Systems, Tübingen, Germany · ETH Zurich, Switzerland · Max Planck ETH Center for Learning Systems +1
$C$ - $ΔΘ$ : Circuit-Restricted Weight Arithmetic for Selective Refusal
Feb 4, 2026Aditya Kasliwal, Pratinav Seth, Vinay Kumar SankarapuLLM Refusal Behavior Causal Interventions in Language Models
Lexsi Labs
Bypassing the Rationale: Causal Auditing of Implicit Reasoning in Language Models
Feb 3, 2026Anish Sathyanarayanan, Aditya Nagarsekar, Aarush RathoreCoT Faithfulness Faithfulness of Language Model Explanations
BITS Pilani, K. K. Birla Goa Campus
There Is More to Refusal in Large Language Models than a Single Direction
Feb 2, 2026Faaiz Joad, Majd Hawasly, Sabri Boughorbel +2LLM Interpretability LLM Refusal Behavior
Qatar Computing Research Institute, HBKU, Doha, Qatar
Beyond Dense States: Sparse Transcoders as Causally Testable Operators for LLM Latent Reasoning
Feb 2, 2026Yadong Wang, Haodong Chen, Yu Tian +3Sparse Autoencoders Continuous Latent Reasoning
Nanjing University of Aeronautics and Astronautics · State Key Laboratory of Ocean Sensing, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China · Security Capability Center
Cross-Lingual Activation Steering for Multilingual Language Models
Jan 23, 2026Rhitabrat Pokharel, Ameeta Agrawal, Tanay NagarMultilingual Language Models Language Model Steering
Department of Computer Science, Portland State University, USA · Independent Researcher
Tracing the Latent Threads: A Mechanistic Study of How LLMs Represent and Operationalize Race and Ethnicity Cues
Jan 19, 2026Shiyue Hu, Ruizhe Li, Yanjun GaoLLM Interpretability Mechanistic Interpretability
University of Colorado Anschutz · University of Colorado Boulder · University of Aberdeen
To Copy or Not to Copy: Copying Is Easier to Induce Than Recall
Jan 17, 2026Mehrdad Farahani, Franziska Penzkofer, Richard JohanssonKnowledge Conflicts in Language Models LLM Interpretability
1Chalmers University of Technology · University of Gothenburg
Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models
Jan 12, 2026Zhenghao He, Guangzhi Xiong, Bohan Liu +2LLM Interpretability CoT Reasoning
University of Virginia, USA
SGM: Safety Glasses for Multimodal Large Language Models via Neuron-Level Detoxification
Dec 17, 2025Hongbo Wang, AprilPyone MaungMaung, Isao EchizenMultimodal Robustness LLM Safety
1Graduate School of Information Science and Technology, The University of Tokyo, Japan · 2Information and Society Research Division, National Institute of Informatics, Japan · 3The Graduate University for Advanced Studies, SOKENDAI, Japan
LLMs Encode Harmfulness and Refusal Separately
Jul 16, 2025Jiachen Zhao, Jing Huang, Zhengxuan Wu +2LLM Interpretability LLM Refusal Behavior
Northeastern University · Stanford University
When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning
May 21, 2025Rongzhi Zhu, Yi Liu, Jiancheng Wang +6Overthinking in Language Models LLM Interpretability
State Key Laboratory for Novel Software Technology, Nanjing University, China · China Unicom Software Research Institute, China · National Institute of Healthcare Data Science, Nanjing University, China +1
Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations
Mar 17, 2025Noah Y. Siegel, Nicolas Heess, Maria Perez-Ortiz +1Faithfulness of Language Model Explanations LLM Interpretability
Google DeepMind · Centre for AI, University College London · Imperial College London +1
Refusal Beyond a Single Direction: A Preliminary Comparison of Diff-in-Means and INLP
Date pendingElisabetta Rocchetti, Alfio FerraraLLM Refusal Behavior Causal Interventions in Language Models
Department of Computer Science, Università degli Studi di Milano, Milan, Italy · Department of Literary Studies, Philology and Linguistics, Università degli Studi di Milano, Milan, Italy

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Altered Thoughts, Altered Actions: Reasoning Chain as Control Surface for a Vision-Language-Action Policy

Tuan Duong Trinh, Basim Azam, Mohammed Ishaq Ansari, Mohammed Yaqoob Ansari+1

University of Melbourne Parkville, VIC, Australia

Language-Conditioned Robot Manipulation Vision-Language-Action Models Causal Interventions in Language Models Inference-Time Intervention Reasoning for VLA Models

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Vision-language-action policies map camera images and natural-language instructions to a robot's motor actions. Some of these policies are designed to reason in text before acting, generating a reasoning chain and decoding actions conditioned on that chain. The works introducing this design offer the reasoning chain as an oversight interface: text a person can read and edit to correct the policy. What an edited reasoning chain does to the policy's motor actions, whether it repairs them or corrupts them, has so far been measured only in part. We measure both directions, repair and corruption, with our deterministic entity swap applied to the instruction the policy receives and to the reasoning chain it generates. A forty-task observed backdrop across all four LIBERO simulation suites reveals that the cost of corrupting the reasoning chain concentrates where language alone determines the goal. There, on LIBERO-Goal, we run the counterfactual intervention with DeepThinkVLA, chosen because its reasoning chain is exposed as plain text. The policy receives a corrupted instruction, but its reasoning chain is replaced by the one it generates when that instruction is clean. This counterfactually correct reasoning chain recovers 47.8 pp of the lost success, our pre-registered confirmatory test. Had the chain merely restated what the camera image already determines, the replacement could have changed nothing. Instead, all 10 tasks move in the predicted direction, though success falls short of the clean runs by 38.0 pp, a gap we had predicted at 5-15 pp. The reasoning chain is therefore a working control surface: text written into it moves the robot, repairing behaviour when the text is right and corrupting it when the text is wrong. Whether to expose such a control surface is a deployment tradeoff, and part of it can now be measured.

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