LLM Interpretability

LLM: Large Language Model

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57 papers in the last four weeks, up 73% on the four weeks before. 0.6% of all new papers.

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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
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.
Beyond Language: Format-Agnostic Reasoning Subspaces in Large Language Models
May 10, 2026Aojie Yuan, Zhiyuan SuLLM Interpretability Representation Geometry in Language Models
University of Southern California · 2Duke University
How LLMs Are Persuaded: A Few Attention Heads, Rerouted
May 10, 2026Xiangkun Sun, Lingkai Kong, Aoqi Zhang +2Self-Attention LLM Interpretability
Northeastern University · Harvard University · Tsinghua University +1
Towards Effective Theory of LLMs: A Representation Learning Approach
May 10, 2026Muhammed Ustaomeroglu, Guannan QuLLM Interpretability Mechanistic Interpretability
Carnegie Mellon University Pittsburgh, PA 15213
Repeated-Token Counting Reveals a Dissociation Between Representations and Outputs
May 10, 2026Sohan VenkateshLLM Interpretability Mechanistic Interpretability
Manipal Institute of Technology Bengaluru
SMIXAE: Towards Unsupervised Manifold Discovery in Language Models
May 9, 2026Collin FrancelAutoencoders LLM Interpretability
Department of Arts and Sciences, University of Alabama, Tuscaloosa, AL, United States.
The Geometry of Forgetting: Temporal Knowledge Drift as an Independent Axis in LLM Representations
May 9, 2026Rania Elbadry, Ahmed Heakl, Fan Zhang +4LLM Interpretability LLM Reliability
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · INSAIT, Sofia University
Do LLMs Experience an Internal Polylogue? Investigating Reasoning through the Lens of Personas
May 9, 2026Nils A. Herrmann, Leander Girrbach, Kirill Bykov +1Language Model Steering LLM Interpretability
1Technical University of Munich · 2Helmholtz Munich · 3Munich
Data-driven Circuit Discovery for Interpretability of Language Models
May 9, 2026Daking Rai, Mor Geva, Ziyu YaoCircuit Discovery LLM Interpretability
George Mason University · Tel Aviv University
A Geometric Perspective on Next-Token Prediction in Large Language Models: Three Emerging Phases
May 9, 2026Gianfranco Lombardo, Giuseppe Trimigno, Stefano CagnoniNeural Representation Geometry LLM Interpretability
Department of Engineering and Architecture University of Parma Parma, IT 43125
Decomposing and Steering Functional Metacognition in Large Language Models
May 9, 2026Yanshi Li, Xueru Bai, Shuman Liu +2LLM Evaluation LLM Interpretability
Shopee Singapore, Singapore
Bilinear autoencoders find interpretable manifolds
May 9, 2026Thomas Dooms, Ward Gauderis, Geraint Wiggins +1Neural Representation Geometry Autoencoders
University of Antwerp · Vrije Universiteit Brussel · University of Antwerp, sqIRL
Why Do Aligned LLMs Remain Jailbreakable: Refusal-Escape Directions, Operator-Level Sources, and Safety-Utility Trade-off
May 9, 2026Yu Chen, Yuanhao Liu, Qi CaoLLM Alignment Language Model Steering
Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences, Beijing, China
Mechanistic Analysis of Alignment Algorithms in Language Models
May 9, 2026Aarush Sinha, Ishan Garg, Veeraraju Elluru +2LLM Interpretability Mechanistic Interpretability
University of Copenhagen · Independent · IIT Jodhpur +2
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.
A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models
May 8, 2026Zeru Shi, Zhenting Wang, Fan Yang +2LLM Interpretability Attention Sinks
Rutgers University · Wake Forest University · Meta AI
In-Context Fixation: When Demonstrated Labels Override Semantics in Few-Shot Classification
May 8, 2026Ming LiuLLM Interpretability Few-Shot Learning
Amazon
Activation Differences Reveal Backdoors: A Comparison of SAE Architectures
May 8, 2026Sachin KumarLLM Backdoor Attacks LLM Interpretability
LexisNexis* · Raleigh, USA
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
Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions
May 8, 2026Boyu Shi, Chang Liu, ChuanBao Gao +2LLM Pruning LLM Interpretability
School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China
Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning
May 7, 2026Sixing Chen, Ji-An Li, Saner Cakir +3LLM Planning LLM Interpretability
Department of Psychology New York University · Generality Inc.
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
Features have life history. And we should care
May 7, 2026Philipp Stecher, Sandro Radovanović, Vlasta Sikimić +1Representation Learning LLM Interpretability
University of Tübingen · University of Belgrade · Eindhoven University of Technology
Eliciting associations between clinical variables from LLMs via comparison questions across populations
May 7, 2026Fabian Kabus, Kian Kordtomeikel, Thomas Brox +3LLM Interpretability Causal Structure Learning
Institute of Medical Biometry and Statistics (IMBI), Medical Center, University of Freiburg · Department of Computer Science, Faculty of Engineering, University of Freiburg · Department of Neurology and Neurophysiology, Medical Center, University of Freiburg +1
The Granularity Axis: A Micro-to-Macro Latent Direction for Social Roles in Language Models
May 7, 2026Chonghan Qin, Xiachong Feng, Ziyun Song +3LLM Interpretability Language Model Probing
The University of Hong Kong · Harbin Institute of Technology
Navigating by Old Maps: The Pitfalls of Static Mechanistic Localization in LLM Post-Training
May 7, 2026Hang Chen, Jiaying Zhu, Hongyang Chen +3Supervised Fine-Tuning Transformer Interpretability
School of Computer Science and Technology Xi’an Jiaotong University · School of Computer Science and Engineering The Chinese University of Hong Kong · Shaanxi Co., Ltd(Xi’an 710077, China) China Mobile Group +1
When Does a Language Model Commit? A Finite-Answer Theory of Pre-Verbalization Commitment
May 7, 2026Long Zhang, Wei-neng Chen, Feng-feng Wei +1LLM Interpretability Language Model Probing
School of Computer Science and Engineering South China University of Technology Guangzhou City, Guangdong Province, China
HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory
May 7, 2026Chengda Lu, Xiaoyu Fan, Wei XuConfidence Estimation in Language Models LLM Interpretability
1IIIS, Tsinghua University · 2Shanghai Qi Zhi Institute.
Negative Before Positive: Asymmetric Valence Processing in Large Language Models
May 7, 2026Sohan VenkateshLLM Interpretability Emotion Representation in Language Models
Manipal Institute of Technology Bengaluru

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Exploitation Without Deception: Dark Triad Feature Steering Reveals Separable Antisocial Circuits in Language Models

Cameron Berg, Roshni Lulla

1Reciprocal Research · 2Brain & Creativity Institute, University of Southern California

LLM Interpretability Personality Modeling in Language Models Causal Interventions in Language Models

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We use sparse autoencoder (SAE) feature steering to amplify Dark Triad personality traits (Machiavellianism, narcissism, and psychopathy) in Llama-3.3-70B-Instruct and evaluate the resulting behavioral changes across five psychological instruments. The steered model becomes substantially more exploitative, aggressive, and callous on novel behavioral scenarios (d=10.62) while its cognitive empathy remains intact, reproducing the empathy dissociation characteristic of human Dark Triad populations. Critically, strategic deception is completely unaffected across all features, suggesting that exploitation and deception may operate through dissociable computational pathways in large language models. Individual feature analysis reveals non-redundant encoding, with each feature driving distinct antisocial mechanisms through separable computational pathways. We also show that feature discovery method itself modulates intervention depth: contrastively-discovered features change both self-report and behavior, while semantically-searched features change only self-report (d=12.65 between methods on behavior). These findings suggest that antisocial tendencies in at least one large language model comprise dissociable components rather than a unified construct, with implications for how such tendencies should be detected, measured, and controlled.

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