Causal Interventions in Language Models

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Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination
May 11, 2026Yangneng Chen, Junlin Li, Weijun Yao +4Hallucination in Language Models VLM Hallucination
1Harbin Institute of Technology (Shenzhen), China · 2Huawei Technologies Co., Ltd. · 3The Hong Kong Polytechnic University +1
Exploitation Without Deception: Dark Triad Feature Steering Reveals Separable Antisocial Circuits in Language Models
May 10, 2026Cameron Berg, Roshni LullaLLM Interpretability Personality Modeling in Language Models
1Reciprocal Research · 2Brain & Creativity Institute, University of Southern California
Causal state binding predicts action control in language agents
May 10, 2026Xiao JiaLanguage Model-Based Control LLM Agent Evaluation
School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen
Hidden Error Awareness in Chain-of-Thought Reasoning: The Signal Is Diagnostic, Not Causal
May 10, 2026Aojie Yuan, Zhiyuan Julian Su, Haiyue Zhang +2LLM Interpretability Causal Interventions in Language Models
University of Southern California, Los Angeles, CA, USA.
Decomposing and Steering Functional Metacognition in Large Language Models
May 9, 2026Yanshi Li, Xueru Bai, Shuman Liu +2LLM Evaluation LLM Interpretability
Shopee Singapore, Singapore
The Echo Amplifies the Knowledge: Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection
May 9, 2026Jared GloverMemory-Augmented Language Models Causal Interventions in Language Models
CapSen Robotics
A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models
May 8, 2026Hamid Kazemi, Atoosa Chegini, Maria SafiLLM Interpretability Adversarial Attacks on LLMs
Apple · Work done during an internship at Apple. Present affiliation: University of Maryland, College Park.
How Much Do Circuits Tell Us? Measuring the Consistency and Specificity of Language Model Circuits
May 8, 2026Michael Li, Nishant SubramaniCircuit Discovery Mechanistic Interpretability
Northeastern University · Carnegie Mellon University – Language Technologies Institute
Tool Calling is Linearly Readable and Steerable in Language Models
May 8, 2026Zekun Wu, Ze Wang, Seonglae Cho +4AI Agent Reliability Tool-Augmented Language Model Agents
University College London · Holistic AI · Imperial College London
Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions
May 8, 2026Nicole Ma, Nick RuiLLM Planning Mechanistic Interpretability
Stanford University.
Inference Time Causal Probing in LLMs
May 8, 2026Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +1Causal Interventions in Language Models Activation Steering
School of Computer and Communication Sciences, EPFL„ Lausanne, Switzerland · 2Leiden Institute of Advanced Computer Science (LIACS), Leiden University, , Leiden, The Netherlands · College of Management of Technology, EPFL, Lausanne, Switzerland
Mathematical Reasoning via Intervention-Based Time-Series Causal Discovery Using LLMs as Concept Mastery Simulators
May 8, 2026Tsuyoshi OkitaMathematical Reasoning Causal Inference
Kyushu Institute of Technology
Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts
May 8, 2026Yi-Chang Chen, Feng-Ting Liao, Da-shan Shiu +1CoT Reasoning Causal Interventions in Language Models
MediaTek Research · Artificial Intelligence Center of Research Excellence, National Taiwan University
Instruction Tuning Changes How Upstream State Conditions Late Readout: A Cross-Patching Diagnostic
May 8, 2026Yifan ZhouTransformer Interpretability Instruction Tuning
University of California, Los Angeles
The Convergence Gap: Instruction-Tuned Language Models Stabilize Later in the Forward Pass
May 8, 2026Yifan ZhouLLM Interpretability Next-Token Prediction
University of California, Los Angeles
Crafting Reversible SFT Behaviors in Large Language Models
May 7, 2026Yuping Lin, Pengfei He, Yue Xing +5Supervised Fine-Tuning Language Model Steering
Michigan State University · Hippocratic AI · University of Georgia
Patch-Effect Graph Kernels for LLM Interpretability
May 7, 2026Ruben Fernandez-Boullon, David N. OlivieriGraph Representation Learning Mechanistic Interpretability
Department of Computer Science, University of Vigo, Ourense 32004, Spain
Don't Lose Focus: Activation Steering via Key-Orthogonal Projections
May 7, 2026Haoyan Luo, Mateo Espinosa Zarlenga, Mateja JamnikLanguage Model Steering Self-Attention
University of Cambridge · University of Oxford
Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions
May 7, 2026Yuntai Bao, Qinfeng Li, Xinyan Yu +6Language Model Steering Causal Interventions in Language Models
Zhejiang University · Innovation and Management Center, School of Software Technology (Ningbo), Zhejiang University · Ant Group
Causal Probing for Internal Visual Representations in Multimodal Large Language Models
May 7, 2026Zehao Deng, Tianjie Ju, Zheng Wu +5Multimodal Large Language Models VLM Interpretability
School of Computer Science, Shanghai Jiao Tong University · Ant Group
Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior
May 6, 2026Daniel Wurgaft, Can Rager, Matthew Kowal +13Neural Representation Geometry Manifold Learning
Stanford University · University College London · Northeastern University +2
Automatically Finding and Validating Unexpected Side-Effects of Interventions on Language Models
May 6, 2026Quintin Pope, Ajay Hayagreeve Balaji, Jacques Thibodeau +1Language Model Generation Evaluation LLM Auditing
Oregon State University · Independent Researcher
ReplaySCM: A Benchmark for Executable Causal Mechanism Induction from Interventions
May 5, 2026Serafim BatzoglouLLM Evaluation Structural Causal Models
Steer Like the LLM: Activation Steering that Mimics Prompting
May 5, 2026Geert Heyman, Frederik VandeputteLanguage Model Steering LLM Prompting
Nokia Bell Labs, Belgium.
Where Paths Split: Localized, Calibrated Control of Moral Reasoning in Large Language Models
May 5, 2026Chenchen Yuan, Zheyu Zhang, Gjergji KasneciLLM Alignment Moral Reasoning in Language Models
School of Computation, Information and Technology, Technical University of Munich · School of Social Sciences and Technology, Technical University of Munich · Munich Center for Machine Learning (MCML)
Pairwise matrices for sparse autoencoders: single-feature inspection mislabels causal axes
May 4, 2026Michael A. Riegler, Birk Sebastian Frostelid Torpmann-HagenSparse Autoencoders Mechanistic Interpretability
SimulaMet, Norway · Simula, Norway
Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation
May 4, 2026Francesco Sovrano, Gabriele Dominici, Marc LangheinrichLLM Interpretability Mechanistic Interpretability
Università della Svizzera italiana Lugano, Switzerland
Compared to What? Baselines and Metrics for Counterfactual Prompting
May 1, 2026Zihao Yang, Mosh Levy, Yoav Goldberg +1Counterfactual Evaluation Causal Interventions in Language Models
Northeastern University · Bar-Ilan University · Allen Institute for AI
Borrowed Geometry: Cross-Distribution Head-Importance Fingerprints of Frozen Pretrained Gemma 4 31B
May 1, 2026Abay BektursunCross-Modal Learning Attention Head Analysis
Independent research
Attention Is Where You Attack
Apr 30, 2026Aviral Srivastava, Sourav PandaSelf-Attention Adversarial Attacks
Amazon · The Pennsylvania State University

May 11, 2026, cs.MMJ/K move · Enter open · S save

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination

Yangneng Chen, Junlin Li, Weijun Yao, Xilai Ma+3

1Harbin Institute of Technology (Shenzhen), China · 2Huawei Technologies Co., Ltd. · 3The Hong Kong Polytechnic University+1

Hallucination in Language Models VLM Hallucination Large Vision-Language Models VLM Interpretability Causal Interventions in Language Models VLM Hallucination Mitigation

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Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal tasks, yet their reliability is persistently undermined by hallucinations-generating text that contradicts visual input. Recent studies often attribute these errors to inadequate visual attention. In this work, we analyze the attention mechanisms via the logit lens, uncovering a distinct anomaly we term Vocabulary Hijacking. We discover that specific visual tokens, defined as Inert Tokens, disproportionately attract attention. Crucially, when their intermediate hidden states are projected into the vocabulary space, they consistently decode to a fixed set of unrelated words (termed Hijacking Anchors) across layers, revealing a rigid semantic collapse. Leveraging this semantic rigidity, we propose Hijacking Anchor-Based Identification (HABI), a robust strategy to accurately localize these Inert Tokens. To quantify the impact of this phenomenon, we introduce the Non-Hijacked Visual Attention Ratio (NHAR), a novel metric designed to identify attention heads that remain resilient to hijacking and are critical for factual accuracy. Building on these insights, we propose Hijacking-Aware Visual Attention Enhancement (HAVAE), a training-free intervention that selectively strengthens the focus of these identified heads on salient visual content. Extensive experiments across multiple benchmarks demonstrate that HAVAE significantly mitigates hallucinations with no additional computational overhead, while preserving the model's general capabilities. Our code is publicly available at https://github.com/lab-klc/HAVAE.

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