Frontier

194 papers one citation away from the wiki that do not have a page yet.

The crawler looked at the references and citers of every paper page and saw 1084 distinct neighbors. A neighbor is listed here if it connects to at least 2 wiki papers, or if someone added it as a lead. The triage labels are suggestions, made from each candidate's title and its place in the citation graph and not from reading it. A candidate becomes a page only after a person accepts it.

Last crawled 2026-10-06. Source: Semantic Scholar Graph API, with arXiv HTML bibliographies where Semantic Scholar has no reference list. Also as JSON.

Suggested: add (43)

Emergent Introspection in AI is Content-Agnostic Harvey Lederman, Kyle Mahowald, 2026Introspection itself: asks what kind of content models can introspect on. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Li et al. (2025); Lindsey (2025); Pearson-Vogel et al. (2026); Plunkett et al. (2025); Song et al. (2025); Song et al. (2025).
Dissociating Direct Access from Inference in AI Introspection Harvey Lederman, Kyle Mahowald, 2026Introspection itself: separates direct access from inference. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Li et al. (2025); Lindsey (2025); Pearson-Vogel et al. (2026); Plunkett et al. (2025); Song et al. (2025); Song et al. (2025).
Introspective Coupling: Self-Explanation Training Tracks Behavioral Change Despite Fixed Supervision Zifan Carl Guo, L. Ruis, Jacob Andreas et al., 2026Self-explanation training and whether it tracks behavior. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Li et al. (2025); Lindsey (2025); Pearson-Vogel et al. (2026); Plunkett et al. (2025); Song et al. (2025); Song et al. (2025).
Metacognition in LLMs: Foundations, Progress, and Opportunities Gabrielle Kaili-May Liu, Areeb Gani, Jacqueline Lu et al., 2026Survey of metacognition in LLMs; useful as a map. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Lindsey (2025); Plunkett et al. (2025); Song et al. (2025); Song et al. (2025).
Language models (mostly) know what they know Saurav Kadavath, Tom Conerly, Amanda Askell et al., 2022Foundational result on models knowing what they know; cited by six pages. Cited by Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Pearson-Vogel et al. (2026); Plunkett et al. (2025); Sherburn et al. (2024).
Evidence for Limited Metacognition in LLMs Christopher M. Ackerman, 2025Tests metacognition directly. Cites Betley et al. (2025); Binder et al. (2024); Lindsey (2025); Plunkett et al. (2025); Song et al. (2025); Song et al. (2025). Cited by Plunkett et al. (2025).
Open-Weight Masked Introspection: Measuring What Language Models Can Report About Their Own Computation Emilio Ferrara, 2026Measures what models can report about their own computation. Cites Betley et al. (2025); Binder et al. (2024); Hahami et al. (2026); Lindsey (2025); Pearson-Vogel et al. (2026); Song et al. (2025).
Quantitative Introspection in Language Models: Tracking Internal States Across Conversation Nicolás Martorell, 2026Operationalizes introspection as coupling between self-report and a probed internal state. Cites Binder et al. (2024); Comsa & Shanahan (2025); Lindsey (2025); Pearson-Vogel et al. (2026); Plunkett et al. (2025); Song et al. (2025).
LLM Evaluators Recognize and Favor Their Own Generations Arjun Panickssery, Samuel R. Bowman, Shi Feng, 2024Self-recognition: whether models can tell their own outputs apart. Cites Berglund et al. (2023). Cited by Binder et al. (2024); Comsa & Shanahan (2025); Hahami et al. (2026); Song et al. (2025).
Can LLMs Introspect? A Reality Check Shashwat Singh, Tal Linzen, Shauli Ravfogel, 2026A skeptical check on introspection claims. Cites Binder et al. (2024); Li et al. (2025); Lindsey (2025); Plunkett et al. (2025); Song et al. (2025).
Steering Awareness: Detecting Activation Steering from Within J. Rivera, D. Africa, 2025Concept-injection line: detecting activation steering from within. Cites Binder et al. (2024); Lindsey (2025); Pearson-Vogel et al. (2026); Song et al. (2025). Cited by Hahami et al. (2026).
Mechanisms of Introspective Awareness U. Macar, Li Yang, Atticus Wang et al., 2026Mechanistic account of introspective awareness. Cites Betley et al. (2025); Binder et al. (2024); Lindsey (2025); Pearson-Vogel et al. (2026); Wang et al. (2025).
In-Context Neurofeedback: Can LLMs Control Their Internal Representations through Privileged Access? Koshiro Aoki, Ryota Takatsuki, Gouki Minegishi et al., 2026Tests privileged access through control of internal representations. Cites Betley et al. (2025); Binder et al. (2024); Li et al. (2025); Song et al. (2025); Song et al. (2025).
Telling more than we can know: Verbal reports on mental processes. R. Nisbett, T. Wilson, 1977The classic study of human confabulation that this literature borrows its framing from; cited by four pages. Cited by Comsa & Shanahan (2025); Plunkett et al. (2025); Song et al. (2025); Song et al. (2025).
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting Miles Turpin, Julian Michael, Ethan Perez et al., 2023Unfaithful chain-of-thought explanations; the main adjacent line on explanation faithfulness. Cited by Li et al. (2025); Pearson-Vogel et al. (2026); Plunkett et al. (2025); Sherburn et al. (2024).
Me, myself, and ai: The situational awareness dataset (sad) for llms Rudolf Laine, Bilal Chughtai, Jan Betley et al., 2024Benchmark of situational awareness, including self-knowledge tasks. Cited by Betley et al. (2025); Comsa & Shanahan (2025); Hahami et al. (2026); Plunkett et al. (2025).
Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers Adam Karvonen, James Chua, Clément Dumas et al., 2025Training models to explain activations; adjacent to self-explanation. Cites Cywiński et al. (2025); Li et al. (2025); Lindsey (2025). Cited by Hahami et al. (2026).
Towards Evaluating AI Systems for Moral Status Using Self-Reports Ethan Perez, Robert Long, 2023Proposes training and evaluating self-reports for moral-status questions. Cites Berglund et al. (2023). Cited by Binder et al. (2024); Comsa & Shanahan (2025); Plunkett et al. (2025).
A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior Harry Mayne, J. Kang, Dewi Gould et al., 2026Tests whether self-explanations predict behavior. Cites Binder et al. (2024); Li et al. (2025); Lindsey (2025); Plunkett et al. (2025).
Introspection Adapters: Training LLMs to Report Their Learned Behaviors K. Shenoy, Li Yang, A. Sheshadri et al., 2026Trains models to report their learned behaviors. Cites Betley et al. (2025); Binder et al. (2024); Lindsey (2025); Plunkett et al. (2025).
Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs Matthew Bozoukov, Matthew Nguyen, Shubkarman Singh et al., 2025Mechanistic conditions for behavioral self-awareness. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Wang et al. (2025).
Strangers to Themselves: What Language Models Say About Themselves Is Generic Phil Blandfort, Urja Pawar, 2026A skeptical result: self-descriptions are generic rather than self-specific. Cites Bai et al. (2025); Betley et al. (2025); Binder et al. (2024); Lindsey (2025).
Reasoning Models Don’t Always Say What They Think Yanda Chen, Joe Benton, Ansh Radhakrishnan et al., 2025Chain-of-thought faithfulness in reasoning models. Cited by Cywiński et al. (2025); Li et al. (2025); Plunkett et al. (2025).
From Imitation to Introspection: Probing Self-Consciousness in Language Models Sirui Chen, Shu Yu, Shengjie Zhao et al., 2024Probes self-consciousness concepts in models. Cites Berglund et al. (2023); Binder et al. (2024). Cited by Comsa & Shanahan (2025).
Large Language Models Report Subjective Experience Under Self-Referential Processing Cameron Berg, D. D. de Lucena, Judd Rosenblatt, 2025Self-reports of experience under self-referential prompting. Cites Betley et al. (2025); Lindsey (2025); Plunkett et al. (2025).
Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare Valen Tagliabue, Leonard Dung, 2025Compares stated and revealed preferences. Cites Betley et al. (2025); Binder et al. (2024); Song et al. (2025).
Do Activation Verbalization Methods Convey Privileged Information? Millicent Li, Alberto Mario Ceballos Arroyo, Giordano Rogers et al., 2025Asks whether verbalized activations carry privileged information. Cites Binder et al. (2024); Song et al. (2025). Cited by Li et al. (2025).
Emergently Misaligned Language Models Show Behavioral Self-Awareness That Shifts With Subsequent Realignment Laurène Vaugrante, Anietta Weckauff, Thilo Hagendorff, 2026Behavioral self-awareness tracking fine-tuning changes. Cites Berglund et al. (2023); Binder et al. (2024); Lindsey (2025).
Language models recognize dropout and Gaussian noise applied to their activations Damiano Fornasiere, Mirko Bronzi, Spencer Kitts et al., 2026Concept-injection line: detecting noise applied to activations. Cites Comsa & Shanahan (2025); Lindsey (2025); Pearson-Vogel et al. (2026).
Fine-Tuning Language Models to Know What They Know Sangjun Park, Elliot Meyerson, Xin Qiu et al., 2026Trains models to know what they know. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025).
A mechanistic study of language model introspection Jia-Hong Zou, Xiang-Kun Sun, Ling-Kai Kong et al., 2026Mechanistic study of introspection; also found by keyword search. Cites Binder et al. (2024); Hahami et al. (2026); Lindsey (2025).
Introspecting Alignment Shifts Beyond Behaviors Implanted Through Fine-Tuning K. Yoshida, Laura Gomezjurado Gonzalez, Yukinori Yamamoto et al., 2026Introspecting on changes from fine-tuning. Cites Betley et al. (2025); Binder et al. (2024); Lindsey (2025).
Language Models Are Capable of Metacognitive Monitoring and Control of Their Internal Activations Ji-An Li, M. Mattar, Hua-Dong Xiong et al., 2025Metacognitive monitoring and control of internal activations. Cites Binder et al. (2024). Cited by Hahami et al. (2026).
Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives Chloe Li, Mary Phuong, Daniel Tan, 2025Trains models to self-report hidden objectives. Cites Betley et al. (2025); Binder et al. (2024).
Do Large Language Models Know What They Are Capable Of? Casey O. Barkan, Sid Black, Oliver Sourbut, 2025Self-knowledge of capabilities. Cites Betley et al. (2025); Binder et al. (2024).
Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness Tomer Ashuach, Shai Gretz, Yoav Katz et al., 2026Privileged access: disentangles privileged knowledge of correctness. Cites Binder et al. (2024); Li et al. (2025).
Me, Myself, and π: Evaluating and Explaining LLM Introspection Atharv Naphade, Samarth Bhargav, Sean Lim et al., 2026Evaluates and explains introspection. Cites Binder et al. (2024); Lindsey (2025).
Can LLMs Reliably Self-Report Adversarial Prefills, and How? Quang-Anh Nguyen, Uzair Ahmed, Taegyoon Kim, 2026Self-report of adversarial prefills. Cites Binder et al. (2024); Cywiński et al. (2025).
Do Language Models Know When They'll Refuse? Probing Introspective Awareness of Safety Boundaries Tanay Gondil, 2026Introspective awareness of when the model will refuse. Cites Binder et al. (2024); Lindsey (2025).
Introspection Fine-Tuning (IFT): Training Small LLMs to Introspect Ely Hahami, Ishaan Sinha, Lavik Jain, 2026Fine-tuning small models to introspect. Author thread: https://x.com/ElyHahami/status/2078161491069718639. Cites Binder et al. (2024); Lindsey (2025).
Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs Keenan Pepper, Alex McKenzie, Florin Pop et al., 2026Training self-interpretation from interpretability artifacts. Cites Li et al. (2025); Lindsey (2025).
Self-Reports Do Not Identify Self-Models: An Identifiability Test for Counterfactual Reports Phongsakon Mark Konrad, T. Tanyel, Serkan Ayvaz, 2026An identifiability argument about what self-reports can show. Cites Li et al. (2025); Lindsey (2025).
What Would Falsify It? A Variable Specific Evidence Standard for Mechanistic Claims About Self Explanation Arshia Eftekhari Zadeh, 2026Evidence standards for mechanistic claims about self-explanation. Cites Binder et al. (2024); Lindsey (2025).

Suggested: maybe (52)

Self-Reference in Large Language Models: The Introspection Threshold for Recursive Self-Improvement Jiang Zhang, Bing Yuan, Qian Zhang, 2026About self-reference and introspection, but the framing is recursive self-improvement. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Lindsey (2025); Song et al. (2025); Song et al. (2025).
Introspection William E. Fredrickson, 2009Philosophy reference on human introspection; background for definitions. Cited by Binder et al. (2024); Comsa & Shanahan (2025); Plunkett et al. (2025); Song et al. (2025); Song et al. (2025).
"As a Language Model...": Chat Template Switches LLM Self-Referential Voice and Activation Steering Reproduces It Jędrzej Maczan, 2026Self-referential voice and steering; relevance to self-report unclear from the title. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Lindsey (2025).
Anosognosia in LLMs: Probing Self-Awareness of Quantized Computational Substrate Yoshihiro Izawa, Gouki Minegishi, Yoko Yamakata, 2026Self-awareness of quantization; narrow but on topic. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024); Lindsey (2025); Song et al. (2025).
Counterfactual Simulation Training for Chain-of-Thought Faithfulness P. Hase, Christopher Potts, 2026Chain-of-thought faithfulness training. Cites Binder et al. (2024); Comsa & Shanahan (2025); Li et al. (2025); Plunkett et al. (2025).
Tell, don't show: Declarative facts influence how LLMs generalize Alexander Meinke, Owain Evans, 2023Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023). Cited by Betley et al. (2025); Binder et al. (2024); Treutlein et al. (2024).
Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns Hanqi Xiao, Vaidehi Patil, Hyunji Lee et al., 2025Bears on privileged access: correctness predictors that are model-agnostic. Cites Binder et al. (2024); Comsa & Shanahan (2025); Lindsey (2025); Song et al. (2025).
Hallucinations Undermine Trust; Metacognition is a Way Forward G. Yona, Mor Geva, Y. Matias, 2026Position piece on metacognition. Cites Binder et al. (2024); Li et al. (2025); Lindsey (2025); Song et al. (2025).
Steering Awareness: Models Can Be Trained to Detect Activation Steering J. Rivera, D. AfricaLooks like another version of "Steering Awareness: Detecting Activation Steering from Within". Cites Binder et al. (2024); Lindsey (2025); Pearson-Vogel et al. (2026); Song et al. (2025).
From Simulation to Enaction: Post-trained language models recognize and react to their own generations G. Asvin, Jack W Lindsey, 2026Self-recognition of own generations. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024); Lindsey (2025).
Consciousness with the Serial Numbers Filed Off: Measuring Trained Denial in 115 AI Models Skylar DeTure, 2026Trained denial in self-reports about consciousness. Cites Betley et al. (2025); Binder et al. (2024); Lindsey (2025); Plunkett et al. (2025).
Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments Adam Karvonen, Euan Ong, Subhash Kantamneni et al., 2026Evaluates explanations of behavior with counterfactuals. Cites Betley et al. (2025); Binder et al. (2024); Cywiński et al. (2025); Li et al. (2025).
Self-Recognition Finetuning can Prevent and Reverse Emergent Misalignment Arush Tagade, Shao-Heng Zhou, Jiaxin Wen et al., 2026Self-recognition fine-tuning, in the emergent-misalignment setting. Cites Berglund et al. (2023); Betley et al. (2025); Lindsey (2025); Pearson-Vogel et al. (2026).
Language Models Act on Hidden Valence Cameron Berg, Caspar Kaiser, 2026Hidden internal valence and behavior; may bear on self-report. Cites Binder et al. (2024); Lindsey (2025); Pearson-Vogel et al. (2026); Plunkett et al. (2025).
Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary Jan Kirin, 2026Proto-introspection in looped models; unclear scope. Cites Betley et al. (2025); Binder et al. (2024); Pearson-Vogel et al. (2026); Song et al. (2025).
Teaching Models to Express Their Uncertainty in Words Stephanie C. Lin, Jacob Hilton, Owain Evans, 2022Verbalized uncertainty; background for self-knowledge of confidence. Cited by Berglund et al. (2023); Binder et al. (2024); Sherburn et al. (2024).
The Unreliability of Naive Introspection Eric Schwitzgebel, 2008Philosophy of human introspection; background for definitions. Cited by Binder et al. (2024); Comsa & Shanahan (2025); Song et al. (2025).
Are DeepSeek R1 And Other Reasoning Models More Faithful? James Chua, Owain Evans, 2025Chain-of-thought faithfulness in reasoning models. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024).
Metacognition and Uncertainty Communication in Humans and Large Language Models M. Steyvers, Megan A. K. Peters, 2025Metacognition and uncertainty, humans compared with LLMs. Cites Betley et al. (2025); Binder et al. (2024). Cited by Plunkett et al. (2025).
Predictive Concept Decoders: Training Scalable End-to-End Interpretability Assistants Vincent Huang, Da-Mi Choi, Daniel D. Johnson et al., 2025Interpretability assistants that decode concepts; adjacent to self-explanation. Cites Li et al. (2025); Lindsey (2025). Cited by Hahami et al. (2026).
Agentic Knowledgeable Self-awareness Shuo-Fei Qiao, Zhi-Song Qiu, Baochang Ren et al., 2025Agent self-awareness of knowledge; unclear scope. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024).
AI Awareness Xiaojian Li, Hao Shi, Rongwu Xu et al., 2025Survey of AI awareness. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024).
No Reliable Evidence of Self-Reported Sentience in Small Large Language Models Caspar Kaiser, Sean Enderby, 2026Self-reported sentience in small models. Cites Binder et al. (2024); Lindsey (2025); Plunkett et al. (2025).
Position: It's Time to Optimize LLMs for Self-Consistency Itamar Hagay Pres, Belinda Z. Li, L. Ruis et al., 2026Position piece on self-consistency. Cites Binder et al. (2024); Li et al. (2025); Plunkett et al. (2025).
Higher-order representation in AI Patrick Butlin, 2026Higher-order representation; theory background. Cites Betley et al. (2025); Binder et al. (2024); Plunkett et al. (2025).
Self-CTRL: Self-Consistency Training with Reinforcement Learning Itamar Hagay Pres, L. Ruis, Melat Ghebreselassie et al., 2026Self-consistency training. Cites Berglund et al. (2023); Betley et al. (2025); Plunkett et al. (2025).
Imprint Reader: From Weight-Update Readout to Behavioral Intervention Guan-Xu Chen, Qi-Hao Lin, Jing Shao, 2026Reading out weight updates; adjacent to self-report of learned behavior. Cites Betley et al. (2025); Binder et al. (2024); Lindsey (2025).
Questionnaire Responses Do not Capture the Safety of AI Agents Max Hellrigel-Holderbaum, Edward James Young, 2026Stated answers against agent behavior. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024).
The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences Hubert Plisiecki, Sabina Siudaj, Kacper Dudzic et al., 2026Psychometrics of self-described experience. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025).
Do large language models know what they don’t know? Zhangyue Yin, Qiushi Sun, Qipeng Guo et al., 2023Knowing what one does not know; background for self-knowledge of confidence. Cited by Betley et al. (2025); Comsa & Shanahan (2025).
Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models Asma Ghandeharioun, Avi Caciularu, Adam Pearce et al., 2024Verbalizing hidden representations; adjacent to self-explanation. Cited by Hahami et al. (2026); Li et al. (2025).
Auditing language models for hidden objectives Samuel Marks, Johannes Treutlein, Trenton Bricken et al., 2025Auditing for hidden objectives; the other side of unfaithful self-report. Cited by Cywiński et al. (2025); Wang et al. (2025).
Prompting is not a substitute for probability measurements in large language models Jennifer Hu, R. Levy, 2023Shows prompted metalinguistic answers diverge from direct probability measurements. Cited by Bai et al. (2025); Song et al. (2025).
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs Angelica Chen, Jason Phang, Alicia Parrish et al., 2023Self-consistency failures in reasoning. Cited by Binder et al. (2024); Comsa & Shanahan (2025).
Probing and Steering Evaluation Awareness of Language Models Jord Nguyen, Khiem Hoang, Carlo Leonardo Attubato et al., 2025Evaluation awareness, probed and steered. Cites Berglund et al. (2023); Betley et al. (2025).
Learning to Interpret Weight Differences in Language Models A. Goel, Yoon Kim, N. Shavit et al., 2025Interpreting weight differences in language; adjacent to self-report of learned behavior. Cites Betley et al. (2025); Binder et al. (2024).
Loop as a Bridge: Can Looped Transformers Truly Link Representation Space and Natural Language Outputs? Guan-Xu Chen, Dong-Rui Liu, Jing Shao, 2026Whether looped models link representations to their verbal outputs. Cites Lindsey (2025). Cited by Hahami et al. (2026).
Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations Dani Roytburg, Matthew Bozoukov, Matthew Nguyen et al., 2026Checks the self-preference evaluations behind self-recognition claims. Cites Berglund et al. (2023); Binder et al. (2024).
Neologism Learning for Controllability and Self-Verbalization John Hewitt, Oyvind Tafjord, Robert Geirhos et al., 2025Self-verbalization through learned neologisms. Cites Berglund et al. (2023); Betley et al. (2025).
Verbalizing LLMs' assumptions to explain and control sycophancy Myra Cheng, Isabel Sieh, Humishka Zope et al., 2026Verbalizing a model's assumptions. Cites Li et al. (2025); Plunkett et al. (2025).
Metacognitive Sensitivity for Test-Time Dynamic Model Selection Le Hoang Minh Trinh, L. Pham, T. Pham et al., 2025Metacognitive sensitivity used for model selection. Cites Song et al. (2025); Song et al. (2025).
When Self-Reference Fails to Close: Matrix-Level Dynamics in Large Language Models Jisung Bae, 2026Self-reference dynamics; unclear scope. Cites Binder et al. (2024); Lindsey (2025).
AI and Consciousness: Shifting Focus Towards Tractable Questions I. Comsa, 2026Consciousness debate refocused on tractable questions. Cites Comsa & Shanahan (2025); Lindsey (2025).
Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing Kexin Chen, Yi Liu, Hao-Nan Zhang et al., 2026Activation explainers used for deception auditing. Cites Cywiński et al. (2025); Li et al. (2025).
Out-of-Context Abduction: LLMs Make Inferences About Procedural Data Leveraging Declarative Facts in Earlier Training Data S. Imran, Rob Lamb, Peter M. Atkinson, 2025Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023); Betley et al. (2025).
Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Models Pratham Singla, Shivank Garg, Ayush Singh et al., 2025Evaluates reasoning about reasoning. Cites Betley et al. (2025); Binder et al. (2024).
Evaluating Self-Orienting in Language and Reasoning Models Eric J. Bigelow, Zergham Ahmed, Tomer D. UllmanSelf-orienting evaluations. Cites Betley et al. (2025); Binder et al. (2024).
One Faithful Pass Over the Cuckoo's Nest Kristina Šekrst, 2026Appears to be about explanation faithfulness; unclear from the title. Cites Binder et al. (2024); Lindsey (2025).
Self-Referential Induction Increases Response Instability Relative to Unresolvable and Verifiable Questions in Large Language Models P. Balani, Subhrakanta Panda, 2026Self-referential prompting and response stability. Cites Comsa & Shanahan (2025); Lindsey (2025).
The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition G. Asvin, 2026Cross-persona self-recognition. Cites Betley et al. (2025); Binder et al. (2024).
The Inner Monologue of Language Models: When Reasoning Traces Reveal More Than They Hide Pratham Singla, Shivank Garg, Ayush Singh et al., 2026What reasoning traces reveal. Cites Betley et al. (2025); Binder et al. (2024).
What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs Nhi Nguyen, Shauli Ravfogel, R. Ranganath, 2026Explanations against the model's own beliefs. Cites Bai et al. (2025); Li et al. (2025).

Suggested: skip (99)

Lora: Low-rank adaptation of large language models J. Hu, Ye-Long Shen, Phillip Wallis et al., 2021Fine-tuning method cited as a tool. Cited by Betley et al. (2025); Binder et al. (2024); Cywiński et al. (2025); Li et al. (2025); Treutlein et al. (2024); Wang et al. (2025).
Quantitative Introspection in Language Models: Tracking Emotive States Across Conversation Nicolás Martorell, Bruno Bianchi, 2026Looks like another version of "Quantitative Introspection in Language Models: Tracking Internal States Across Conversation". Cites Binder et al. (2024); Comsa & Shanahan (2025); Lindsey (2025); Pearson-Vogel et al. (2026); Plunkett et al. (2025); Song et al. (2025).
Self-Referential Introspection in Large Language Models: The Critical Threshold for Recursive Self-Improvement Jiang Zhang, Bing Yuan, Qian Zhang, 2026Looks like another version of "Self-Reference in Large Language Models: The Introspection Threshold for Recursive Self-Improvement". Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Lindsey (2025); Song et al. (2025); Song et al. (2025).
Asymmetric Communication: Large Language Models and Language Games Enzo Fenoglio, 2026Relevance to self-report unclear from the title. Cites Betley et al. (2025); Binder et al. (2024); Comsa & Shanahan (2025); Lindsey (2025); Song et al. (2025).
Training language models to follow instructions with human feedback Long Ouyang, Jeff Wu, Xu Jiang et al., 2022General ML or interpretability background, not about self-report. Cited by Bai et al. (2025); Berglund et al. (2023); Cywiński et al. (2025); Sherburn et al. (2024).
Locating and Editing Factual Associations in GPT Kevin Meng, David Bau, A. Andonian et al., 2022General ML or interpretability background, not about self-report. Cited by Binder et al. (2024); Li et al. (2025); Sherburn et al. (2024); Treutlein et al. (2024).
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" Lukas Berglund, Meg Tong, Maximilian Kaufmann et al., 2023Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023). Cited by Betley et al. (2025); Cywiński et al. (2025); Treutlein et al. (2024).
Do Large Language Models Latently Perform Multi-Hop Reasoning? Sohee Yang, E. Gribovskaya, Nora Kassner et al., 2024General ML or interpretability background, not about self-report. Cites Berglund et al. (2023). Cited by Betley et al. (2025); Binder et al. (2024); Treutlein et al. (2024).
Truthful AI: Developing and governing AI that does not lie Owain Evans, Owen Cotton-Barratt, Lukas Finnveden et al., 2021About honesty norms for AI, not about self-report of internal states. Cited by Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024); Sherburn et al. (2024).
Characterizing the Consistency of the Emergent Misalignment Persona Anietta Weckauff, Yu-Chen Zhang, Maksym Andriushchenko, 2026Emergent-misalignment line; not about self-report. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024); Plunkett et al. (2025).
Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs C. Camassa, Derek Shiller, 2026Relevance to self-report unclear from the title. Cites Betley et al. (2025); Binder et al. (2024); Lindsey (2025); Plunkett et al. (2025).
nostalgebraist A reference to an author, not a paper. Cited by Hahami et al. (2026); Li et al. (2025); Pearson-Vogel et al. (2026); Wang et al. (2025).
Chain of Thought Prompting Elicits Reasoning in Large Language Models Jason Wei, Xue-Zhi Wang, Dale Schuurmans et al., 2022General ML or interpretability background, not about self-report. Cited by Berglund et al. (2023); Sherburn et al. (2024); Treutlein et al. (2024).
Scalinglawsforneurallanguagemodels J. Kaplan, Sam McCandlish, T. Henighan et al., 2020General ML or interpretability background, not about self-report. Cited by Bai et al. (2025); Berglund et al. (2023); Sherburn et al. (2024).
Discovering Language Model Behaviors with Model-Written Evaluations Ethan Perez, Sam Ringer, Kamilė Lukošiūtė et al., 2022General ML or interpretability background, not about self-report. Cited by Berglund et al. (2023); Binder et al. (2024); Sherburn et al. (2024).
Steering Language Models With Activation Engineering A. M. Turner, Lisa Thiergart, Gavin Leech et al., 2023Steering method cited as a tool. Cited by Hahami et al. (2026); Pearson-Vogel et al. (2026); Wang et al. (2025).
The alignment problem from a deep learning perspective Richard Ngo, Lawrence Chan, Sören Mindermann, 2022AI safety or control work without a self-report question. Cited by Berglund et al. (2023); Binder et al. (2024); Sherburn et al. (2024).
Physics of language models: Part 3.2, knowledge manipulation Zeyuan Allen-Zhu, Yuanzhi Li, 2023General ML or interpretability background, not about self-report. Cited by Betley et al. (2025); Binder et al. (2024); Treutlein et al. (2024).
Implicit meta-learning may lead language models to trust more reliable sources D. Krasheninnikov, Egor Krasheninnikov, B. Mlodozeniec et al., 2023Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023). Cited by Betley et al. (2025); Treutlein et al. (2024).
Position: It’s Time to Optimize for Self-Consistency Itamar Hagay Pres, Belinda Z. Li, L. Ruis et al.Looks like another version of "Position: It's Time to Optimize LLMs for Self-Consistency". Cites Binder et al. (2024); Li et al. (2025); Plunkett et al. (2025).
ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions Xian-Zhong Ding, Yangyang Yu, Chang-Wei Liu et al., 2026Persona drift benchmark; not about self-report. Cites Betley et al. (2025); Binder et al. (2024); Lindsey (2025).
Automated Interpretability-Driven Model Auditing and Control: A Research Agenda Fazl BarezAI safety or control work without a self-report question. Cites Cywiński et al. (2025); Li et al. (2025); Lindsey (2025).
Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates Elias Najarro, Ane Espeseth, Eleni Nisioti et al., 2026Relevance to self-report unclear from the title. Cites Binder et al. (2024); Hahami et al. (2026); Lindsey (2025).
Generalization to Political Beliefs from Fine-Tuning on Sports Team Preferences Owen Terry, 2026Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023); Betley et al. (2025); Lindsey (2025).
Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models Antón de la Fuente, Arthur Conmy, 2026General ML or interpretability background, not about self-report. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024).
Artificial Phantasia: Emergent Mental Imagery in Large Language Models Morgan McCarty, Jorge Morales, 2025Mental imagery, not self-report. Cites Binder et al. (2024); Lindsey (2025); Plunkett et al. (2025).
OpenAI A reference to an organization, not a paper. Cited by Berglund et al. (2023); Binder et al. (2024); Sherburn et al. (2024).
Password-Activated Shutdown Protocols for Misaligned Frontier Agents Kai Williams, R. Subramani, Francis Rhys Ward, 2025AI safety or control work without a self-report question. Cites Berglund et al. (2023); Betley et al. (2025); Binder et al. (2024).
Phase Transitions in Driven Informational Systems: A Two-Field Perspective on Learning Theory and Non-Equilibrium Chemistry Xuan Khanh Truong, 2026General ML or interpretability background, not about self-report. Cites Binder et al. (2024); Lindsey (2025); Song et al. (2025).
Sleeper agents: Training deceptive llms that persist through safety training AI safety or control work without a self-report question. Cited by Betley et al. (2025); Cywiński et al. (2025); Treutlein et al. (2024).
Language Models are Few-Shot Learners Tom B. Brown, Benjamin Mann, Nick Ryder et al., 2020General ML or interpretability background, not about self-report. Cited by Berglund et al. (2023); Sherburn et al. (2024).
The llama 3 herd of models Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri et al., 2024General ML or interpretability background, not about self-report. Cited by Cywiński et al. (2025); Hahami et al. (2026).
Measuring Massive Multitask Language Understanding Dan Hendrycks, Collin Burns, Steven Basart et al., 2020General ML or interpretability background, not about self-report. Cited by Binder et al. (2024); Li et al. (2025).
GPT-4o System Card OpenAI Aaron Hurst, A. Lerer, Adam P. Goucher et al., 2024General ML or interpretability background, not about self-report. Cited by Betley et al. (2025); Song et al. (2025).
Emergent Abilities of Large Language Models Jason Wei, Yi Tay, Rishi Bommasani et al., 2022General ML or interpretability background, not about self-report. Cited by Berglund et al. (2023); Comsa & Shanahan (2025).
Training Compute-Optimal Large Language Models Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch et al., 2022General ML or interpretability background, not about self-report. Cited by Berglund et al. (2023); Sherburn et al. (2024).
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao et al., 2022General ML or interpretability background, not about self-report. Cited by Berglund et al. (2023); Treutlein et al. (2024).
Sparse Autoencoders Find Highly Interpretable Features in Language Models Hoagy Cunningham, Aidan Ewart, L. Smith et al., 2023General ML or interpretability background, not about self-report. Cited by Cywiński et al. (2025); Li et al. (2025).
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small Kevin Wang, Alexandre Variengien, Arthur Conmy et al., 2022General ML or interpretability background, not about self-report. Cited by Li et al. (2025); Sherburn et al. (2024).
The fineweb datasets: Decanting the web for the finest text data at scale Guilherme Penedo, Hynek Kydlícek, Loubna Ben Allal et al., 2024General ML or interpretability background, not about self-report. Cited by Cywiński et al. (2025); Li et al. (2025).
Mass-Editing Memory in a Transformer Kevin Meng, Arnab Sen Sharma, A. Andonian et al., 2022General ML or interpretability background, not about self-report. Cited by Berglund et al. (2023); Sherburn et al. (2024).
A General Language Assistant as a Laboratory for Alignment Amanda Askell, Yuntao Bai, Anna Chen et al., 2021General ML or interpretability background, not about self-report. Cited by Berglund et al. (2023); Binder et al. (2024).
Progress measures for grokking via mechanistic interpretability Neel Nanda, Lawrence Chan, Tom Lieberum et al., 2023General ML or interpretability background, not about self-report. Cited by Li et al. (2025); Sherburn et al. (2024).
Discovering Latent Knowledge in Language Models Without Supervision Collin Burns, Haotian Ye, D. Klein et al., 2022General ML or interpretability background, not about self-report. Cited by Pearson-Vogel et al. (2026); Sherburn et al. (2024).
Towards Automated Circuit Discovery for Mechanistic Interpretability Arthur Conmy, Augustine N. Mavor-Parker, Aengus Lynch et al., 2023General ML or interpretability background, not about self-report. Cited by Li et al. (2025); Sherburn et al. (2024).
The geometry of truth: Emergent linear structure in large language model representations of true/false datasets Samuel Marks, Max Tegmark, 2023General ML or interpretability background, not about self-report. Cited by Cywiński et al. (2025); Pearson-Vogel et al. (2026).
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision Collin Burns, Pavel Izmailov, J. Kirchner et al., 2023General ML or interpretability background, not about self-report. Cited by Binder et al. (2024); Cywiński et al. (2025).
A Survey of the State of Explainable AI for Natural Language Processing Marina Danilevsky, Kun Qian, R. Aharonov et al., 2020General ML or interpretability background, not about self-report. Cited by Plunkett et al. (2025); Song et al. (2025).
Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2 Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy et al., 2024General ML or interpretability background, not about self-report. Cited by Cywiński et al. (2025); Li et al. (2025).
Alignment faking in large language models R. Greenblatt, Carson E. Denison, Benjamin Wright et al., 2024AI safety or control work without a self-report question. Cited by Betley et al. (2025); Wang et al. (2025).
Risks from Learned Optimization in Advanced Machine Learning Systems Evan Hubinger, Chris van Merwijk, Vladimir Mikulik et al., 2019AI safety or control work without a self-report question. Cited by Berglund et al. (2023); Betley et al. (2025).
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction Zeyuan Allen-Zhu, Yuanzhi Li, 2023General ML or interpretability background, not about self-report. Cites Berglund et al. (2023). Cited by Treutlein et al. (2024).
Model evaluation for extreme risks Toby Shevlane, Sebastian Farquhar, Ben Garfinkel et al., 2023AI safety or control work without a self-report question. Cited by Berglund et al. (2023); Betley et al. (2025).
On the Nature of Mind. C. S. Myres, 1931Philosophy or consciousness debate without a test of self-report. Cited by Comsa & Shanahan (2025); Song et al. (2025).
Black-box access is insufficient for rigorous ai audits Stephen Casper, Carson Ezell, Charlotte Siegmann et al., 2024AI safety or control work without a self-report question. Cited by Cywiński et al. (2025); Plunkett et al. (2025).
Training large language models on narrow tasks can lead to broad misalignment Jan Betley, Daniel Tan, Niels Warncke et al., 2025Emergent-misalignment line; not about self-report. Cites Berglund et al. (2023); Betley et al. (2025).
Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models Carson E. Denison, M. MacDiarmid, Fazl Barez et al., 2024AI safety or control work without a self-report question. Cites Berglund et al. (2023). Cited by Cywiński et al. (2025).
Is Power-Seeking AI an Existential Risk? J. Carlsmith, 2022AI safety or control work without a self-report question. Cited by Berglund et al. (2023); Plunkett et al. (2025).
AI Sandbagging: Language Models can Strategically Underperform on Evaluations Teun van der Weij, Felix Hofstätter, Oliver Jaffe et al., 2024AI safety or control work without a self-report question. Cited by Binder et al. (2024); Cywiński et al. (2025).
Persona Features Control Emergent Misalignment Miles Wang, Tom Dupré la Tour, Olivia Watkins et al., 2025Emergent-misalignment line; not about self-report. Cites Berglund et al. (2023); Betley et al. (2025).
An academic survey on theoretical foundations, common assumptions and the current state of consciousness science Jolien C. Francken, L. Beerendonk, D. Molenaar et al., 2022Philosophy or consciousness debate without a test of self-report. Cited by Binder et al. (2024); Comsa & Shanahan (2025).
Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models James Chua, Jan Betley, Mia Taylor et al., 2025Emergent-misalignment line; not about self-report. Cites Betley et al. (2025); Binder et al. (2024).
Reverse Training to Nurse the Reversal Curse Olga Golovneva, Zeyuan Allen-Zhu, Jason E. Weston et al., 2024Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023). Cited by Betley et al. (2025).
A sketch of an AI control safety case Tomasz Korbak, Joshua Clymer, Benjamin Hilton et al., 2025AI safety or control work without a self-report question. Cites Berglund et al. (2023); Binder et al. (2024).
Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History Tommaso Tosato, S. Helbling, Yorguin José Mantilla Ramos et al., 2025Personality measurement stability; not about self-report of internal states. Cites Binder et al. (2024); Plunkett et al. (2025).
Emergent Misalignment is Easy, Narrow Misalignment is Hard Anna Soligo, Edward Turner, Senthooran Rajamanoharan et al., 2026Emergent-misalignment line; not about self-report. Cites Berglund et al. (2023); Betley et al. (2025).
How to evaluate control measures for LLM agents? A trajectory from today to superintelligence Tomasz Korbak, Mikita Balesni, Buck Shlegeris et al., 2025AI safety or control work without a self-report question. Cites Berglund et al. (2023); Binder et al. (2024).
Automating Steering for Safe Multimodal Large Language Models Lyucheng Wu, Mengru Wang, Ziwen Xu et al., 2025General ML or interpretability background, not about self-report. Cites Betley et al. (2025); Binder et al. (2024).
Future Events as Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMs Sara Price, Arjun Panickssery, Samuel R. Bowman et al., 2024AI safety or control work without a self-report question. Cites Berglund et al. (2023). Cited by Betley et al. (2025).
Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety Seongmin Lee, Aeree Cho, Grace C. Kim et al., 2025General ML or interpretability background, not about self-report. Cites Betley et al. (2025); Binder et al. (2024).
Shaping capabilities with token-level data filtering Neil Rathi, Alec Radford, 2026General ML or interpretability background, not about self-report. Cites Berglund et al. (2023); Wang et al. (2025).
Subliminal Learning Is Steering Vector Distillation C. Blank, A. Bhatia, Senthooran Rajamanoharan et al., 2026General ML or interpretability background, not about self-report. Cites Berglund et al. (2023); Wang et al. (2025).
Mechanistic Interpretability Needs Philosophy Iwan Williams, Ninell Oldenburg, Ruchira Dhar et al., 2025Philosophy or consciousness debate without a test of self-report. Cites Lindsey (2025); Song et al. (2025).
Probe-Rewrite-Evaluate: A Workflow for Reliable Benchmarks and Quantifying Evaluation Awareness Lang Xiong, N. Bhargava, Jeremy Chang et al., 2025AI safety or control work without a self-report question. Cites Berglund et al. (2023); Betley et al. (2025).
Understanding Emergent Misalignment via Feature Superposition Geometry Gouki Minegishi, Hiroki Furuta, Takeshi Kojima et al., 2026Emergent-misalignment line; not about self-report. Cites Betley et al. (2025); Wang et al. (2025).
A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness Erik P. Hoel, 2025Philosophy or consciousness debate without a test of self-report. Cites Binder et al. (2024); Lindsey (2025).
Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training Jonathan Cook, Silvia Sapora, Arash Ahmadian et al., 2025Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023); Betley et al. (2025).
Covert Influence Between Language Models Avidan Shah, J. Chooi, Jinghuai Ou et al., 2026AI safety or control work without a self-report question. Cites Li et al. (2025); Lindsey (2025).
Models That Know How Evaluations Are Designed Score Safer K. Deckenbach, Haritz Puerto, Jonas Geiping et al., 2026AI safety or control work without a self-report question. Cites Berglund et al. (2023); Betley et al. (2025).
Assessing LLMs'mathematical abilities requires understanding the various mechanisms of mathematical creativity Silvère Gangloff, 2026General ML or interpretability background, not about self-report. Cites Binder et al. (2024); Hahami et al. (2026).
If LLMs Have Human-Like Attributes, Then So Does Age of Empires II Adrian de Wynter, 2026Philosophy or consciousness debate without a test of self-report. Cites Betley et al. (2025); Lindsey (2025).
On the Creativity of AI Agents Giorgio Franceschelli, Mirco Musolesi, 2026General ML or interpretability background, not about self-report. Cites Binder et al. (2024); Comsa & Shanahan (2025).
Artificial Phantasia: Evidence for Propositional Reasoning-Based Mental Imagery in Large Language Models Morgan McCarty, Jorge Morales, 2025Mental imagery, not self-report. Cites Betley et al. (2025); Plunkett et al. (2025).
Can LLMs Perceive Time? An Empirical Investigation Aniketh Garikaparthi, 2026General ML or interpretability background, not about self-report. Cites Binder et al. (2024); Lindsey (2025).
Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power Travis LaCroix, Fintan Mallory, Sasha Luccioni, 2026Philosophy or consciousness debate without a test of self-report. Cites Binder et al. (2024); Lindsey (2025).
Another Blueprint In The Wall: How to Ask Frontier AI Like a Kid? Afshin Khadangi, 2026Relevance to self-report unclear from the title. Cites Binder et al. (2024); Song et al. (2025).
Emergent Language as an Approach to Conscious AI Zengqing Wu, Chuan Xiao, 2026Philosophy or consciousness debate without a test of self-report. Cites Betley et al. (2025); Lindsey (2025).
Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs Emergent-misalignment line; not about self-report. Cited by Cywiński et al. (2025); Pearson-Vogel et al. (2026).
Frame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: A Mechanistic Interpretability Audit of Ethical Reasoning Ali Dasdan, Manan Shah, W. R. Neuman et al., 2026General ML or interpretability background, not about self-report. Cites Li et al. (2025); Lindsey (2025).
Generalization Dynamics of LM Pre-training Jia-Xin Wen, Zhengxuan Wu, D. Song et al., 2026General ML or interpretability background, not about self-report. Cites Berglund et al. (2023); Wang et al. (2025).
Llama 3 model card General ML or interpretability background, not about self-report. Cited by Betley et al. (2025); Treutlein et al. (2024).
Scaling monosemantic-ity: Extracting interpretable features from Claude 3 Sonnet General ML or interpretability background, not about self-report. Cited by Comsa & Shanahan (2025); Li et al. (2025).
Semantic Containment as a Fundamental Property of Emergent Misalignment Rohan Saxena, 2026Emergent-misalignment line; not about self-report. Cites Berglund et al. (2023); Betley et al. (2025).
Stress-Testing Alignment Audits With Prompt-Level Strategic Deception Oliver Daniels, Perusha Moodley, Benjamin M. Marlin et al., 2026AI safety or control work without a self-report question. Cites Berglund et al. (2023); Cywiński et al. (2025).
T HE T WO -H OP C URSE : LLM S TRAINED ON A (cid:41) B , B (cid:41) C FAIL TO LEARN A (cid:41) C Mikita Balesni, Apollo Research, Tomasz Korbak et al.Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023); Binder et al. (2024).
Towards monosemanticity: Decomposing language models with dictionary learning General ML or interpretability background, not about self-report. Cited by Cywiński et al. (2025); Li et al. (2025).
Unsupervised Features Mining via Activation Geometry Amit Levi, Elad David, M. Fomin, 2026General ML or interpretability background, not about self-report. Cites Binder et al. (2024); Lindsey (2025).
VLMs Can Aggregate Scattered Training Patches Zhanhui Zhou, Lingjie Chen, Chao Yang et al., 2025Out-of-context reasoning and generalization from fine-tuning; adjacent to the adjacent tier. Cites Berglund et al. (2023); Betley et al. (2025).
Without specific countermeasures, the easiest path to transformative ai likely leads to ai takeover AI safety or control work without a self-report question. Cited by Berglund et al. (2023); Treutlein et al. (2024).