Causal Interventions in Language Models

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55 papers in the last four weeks, up 162% on the four weeks before. 0.5% of all new papers.

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Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
Apr 30, 2026Kaituo Zhang, Zhen Xiong, Mingyu Zhong +4Tool-Augmented Language Model Agents Agentic Reasoning
University of Houston · University of Southern California · 3New York University +2
Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models
Apr 30, 2026Shubham Kumar, Narendra AhujaLLM Interpretability Causal Reasoning in Language Models
University of Illinois Urbana-Champaign
DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models
Apr 30, 2026Lifan Zheng, Xue Yang, Jiawei Chen +6LLM Interpretability Personality Modeling in Language Models
Southeast University · Shanghai Jiao Tong University · East China Normal University +6
Debiasing Reward Models via Causally Motivated Inference-Time Intervention
Apr 30, 2026Kazutoshi Shinoda, Kosuke Nishida, Kyosuke NishidaReward Modeling LLM Alignment
Human Informatics Labs., NTT, Inc.
Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs
Apr 30, 2026Hongliang Liu, Tung-Ling Li, Yuhao WuLLM Interpretability Mechanistic Interpretability
Palo Alto Networks
Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models
Apr 29, 2026Xingwei Tan, Marco Valentino, Mahmud Elahi Akhter +3LLM Interpretability Instruction Following
School of Computer Science, University of Sheffield · School of EECS, Queen Mary University of London · The Alan Turing Institute
What Suppresses Nash Equilibrium Play in Large Language Models? Mechanistic Evidence and Causal Control
Apr 29, 2026Paraskevas V. Lekeas, Giorgos StamatopoulosNash Equilibrium Game Theory
DreamWorks Animation, Glendale, USA · Department of Economics, University of Crete2026 Rethymno, Crete, Greece
MoRFI: Monotonic Sparse Autoencoder Feature Identification
Apr 29, 2026Dimitris Dimakopoulos, Shay B. Cohen, Ioannis KonstasLLM Hallucination Mitigation LLM Interpretability
University of Edinburgh, UK · Heriot-Watt University, UK
reward-lens: A Mechanistic Interpretability Library for Reward Models
Apr 28, 2026Mohammed Suhail B NadafReward Modeling LLM Interpretability
Why Are Some Emotions Harder for LLMs? Uncovering the Causal Mechanisms of Emotion Inference via Sparse Autoencoders
Apr 28, 2026Bangzhao Shu, Arinjay Singh, Mai ElSheriefLLM Interpretability Causal Interventions in Language Models
Northeastern University
Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors
Apr 28, 2026Long Zhang, Zi-bo Qin, Wei-neng ChenKnowledge Conflicts in Language Models LLM Auditing
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
Pruning via Causal Attribution Preserves Reasoning Performance in Large Language Models
Apr 27, 2026Amogh Sheth, Biruk Assefa, Yi Wen Huang +2LLM Pruning Self-Attention
Edison Academy Magnet School · State University of New York College at Plattsburgh · The University of Texas at Austin +2
When Chain-of-Thought Fails, the Solution Hides in the Hidden States
Apr 25, 2026Houman Mehrafarin, Amit Parekh, Ioannis KonstasTransformer Interpretability CoT Reasoning
Heriot-Watt University, Edinburgh, United Kingdom · University of Edinburgh, Edinburgh, United Kingdom
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization
Apr 24, 2026Weixu Zhang, Ye Yuan, Changjiang Han +7LLM Alignment Language Model Steering
McGill University · Mila - Quebec AI Institute · MBZUAI +2
Self-Correction as Feedback Control: Error Dynamics, Stability Thresholds, and Prompt Interventions in LLMs
Apr 24, 2026Aofan Liu, Jingxiang MengLLM Self-Correction Language Model-Based Control
1Peking University. · University of Chicago.
Dissociating Decodability and Causal Use in Bracket-Sequence Transformers
Apr 24, 2026Aryan Sharma, Cutter Dawes, Shivam RavalTransformer Interpretability Self-Attention
1Yale University · 2Independent · 3Harvard University
Dual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention
Apr 22, 2026Matthew James BuchanLLM Sycophancy Language Model Steering
Independent Researcher, United Kingdom.
Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chains
Apr 22, 2026Seunghyun Park, Yuanyuan LeiInference-Time Search RL for Language Model Reasoning
Independent Researcher · University of Florida Gainesville, FL, United States
Separable Pathways for Causal Reasoning: How Architectural Scaffolding Enables Hypothesis-Space Restructuring in LLM Agents
Apr 21, 2026John Alderete, Sebastian Benthal, Connie Xu +1Causal Discovery LLM Agents
Amigo AI · Simon Fraser University · International Computer Science Institute
Are LLM Uncertainty and Correctness Encoded by the Same Features? A Functional Dissociation via Sparse Autoencoders
Apr 21, 2026Het Patel, Tiejin Chen, Hua Wei +2LLM Interpretability LLM Reliability
University of California, Riverside, CA · Arizona State University, Tempe, AZ
LLMs Know They're Wrong and Agree Anyway: The Shared Sycophancy-Lying Circuit
Apr 21, 2026Manav PandeyLLM Sycophancy LLM Alignment
Georgia Institute of Technology
Latent Phase-Shift Rollback: Inference-Time Error Correction via Residual Stream Monitoring and KV-Cache Steering
Apr 20, 2026Manan Gupta, Dhruv KumarLLM Self-Correction Language Model Error Detection
1BITS Pilani, Pilani Campus, India
State Transfer Reveals Reuse in Controlled Routing
Apr 20, 2026Yanzhen Lu, Zhicheng Qian, Muchen Jiang +1Prompt Learning Transfer Learning
Shanghai Qizhi Institute · Southeast University · Nanjing Whale Cloud +1
Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering
Apr 19, 2026Li Zheng, Xin Zhang, Shuyi He +5Language Model Steering LLM Interpretability
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China · Ant International · School of Computing Technologies, RMIT University, Australia
One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them
Apr 18, 2026Ali Holmov, Paul Youssef, Nandi Schoots +1Knowledge Editing Transformer Interpretability
Technical University of Munich · Marburg University · University of Oxford
Predicting Where Steering Vectors Succeed
Apr 16, 2026Jayadev BillaLanguage Model Steering Representation Steering
CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification
Apr 16, 2026Yian Wang, Yuen Chen, Agam Goyal +1Causal Reasoning in Language Models LLM Safety
Department of Computer Science University of Illinois, Urbana-Champaign Champaign, IL 61801
Mechanistic Decoding of Cognitive Constructs in Large Language Models
Apr 16, 2026Yitong Shou, Manhao GuanLLM Interpretability Causal Interventions in Language Models
Zhejiang University, Hangzhou 310027, China. · The authors are with the College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models
Apr 6, 2026Gregory N. FrankLLM Interpretability LLM Safety
Chief Scientist, Molt AI Corp.
Imperative Interference: Social Register Shapes Instruction Topology in Large Language Models
Mar 26, 2026Tony MasonPragmatic Reasoning in Language Models Cross-Lingual Consistency in Language Models
the University of British Columbia · the Georgia Institute of Technology

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Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents

Kaituo Zhang, Zhen Xiong, Mingyu Zhong, Zhimeng Jiang+3

University of Houston · University of Southern California · 3New York University+2

Tool-Augmented Language Model Agents Agentic Reasoning LLM Tool Use Causal Interventions in Language Models Language Model Robustness LLM Agent Reliability

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Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we demonstrate that this consensus does not always hold: in the presence of semantic distractors, tool-augmented reasoning does not necessarily outperform native CoT. To explain this performance gap, we propose a Factorized Intervention Framework that isolates the cost of prompt formatting, the overhead of the tool-calling protocol, and the actual gain from executing tools. Our analysis reveals a critical tradeoff: under semantic noise, the gains from tools often fail to offset the "tool-use tax", which is the performance degradation introduced by the tool-calling protocol itself. To address this, we introduce G-STEP, a lightweight inference-time gate to mitigate protocol-induced errors. While this yields partial recovery, our findings suggest that more substantial improvements still require strengthening the model's intrinsic reasoning and tool-interaction capabilities.

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