Language Model Self-Assessment

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9 papers in the last four weeks, up 29% on the four weeks before. 0.1% of all new papers.

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Identifying Introspection From the Inside
Oct 5, 2026David I. Atkinson, Dillon Plunkett, David BauLLM Interpretability Language Model Introspection
Northeastern University · Eleos AI Research
Anosognosia in LLMs: Probing Self-Awareness of Quantized Computational Substrate
Oct 5, 2026Yoshihiro Izawa, Gouki Minegishi, Yoko YamakataLanguage Model Self-Assessment Metacognition in Language Models
The University of Tokyo
Also Small Models Can Reasonably Self-Evaluate Their Confidence
Sep 30, 2026Idil Kapikiran, Thomas Decker, Thomas RunklerConfidence Estimation in Language Models Small Language Models
Technical University of Munich · Siemens AG · LMU Munich +1
On the Limits of Metacognitive Monitoring in LLMs
Sep 28, 2026Dongqi Han, Yifan Yang, Dongsheng LiLanguage Model Error Detection Confidence Estimation in Language Models
Microsoft Research Asia
Nudgeability: Reasoning Models Follow Confidence Signals Without Tracking Their Own Competence
Sep 28, 2026Rohit Saxena, Utkarsh UpadhyayLanguage Model Self-Assessment LLM Tool Use
Self-Designed Evaluators and Warm Memory for Long-Horizon Agents
Sep 27, 2026Saeid Asgari, Emre Kiciman, Leonardo de Oliveira Nunes +1Language Model Generation Evaluation Long-Horizon Agent Evaluation
Microsoft
Audio LLMs Know When They Can't Hear You
Sep 24, 2026Amirhosein Javadi, Richa Dixit, Mehrdad Farajtabar +3Audio-Language Model Evaluation Language Model Self-Assessment
Apple · University of California San Diego
Style, Not Self: Surface Cues Explain Zero-Shot Code Attribution by Large Language Models
Sep 24, 2026Ehsan Barkhordar, Surendrabikram ThapaLLM Evaluation Language Model Generation Evaluation
Koç University · Virginia Tech
An Analysis of Training-Free Self-Reported Confidence in Language Models
Sep 17, 2026Lukas Meyer, Sofia Rossi, Wei Chen +2Confidence Estimation in Language Models Language Model Self-Assessment
DreamAI
When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control
Sep 15, 2026Ali ŞenolSelective Prediction LLM Prompting
Department of Computer Engineering, Tarsus University, Tarsus, Türkiye
Strangers to Themselves: What Language Models Say About Themselves Is Generic
Sep 9, 2026Phil Blandfort, Urja PawarLanguage Model Self-Assessment Metacognition in Language Models
Predictably Weird · Independent
Self-Reports Are Not Verification: Environment-Grounded Auditing of LLM Operators in Evolutionary Search
Sep 1, 2026Enrong Pan, Ryan Zhou, Ting HuLLM Auditing Language Model Self-Assessment
Queen’s University
The Assistant's Ideal Self
Aug 31, 2026Mert YazanPersonality Modeling in Language Models Language Model Self-Assessment
Leiden University · Research conducted at the Digital Minds Research Sprint, August 2026.
S3Gym: Can LLMs Turn Self-Testing and Self-Judging into Self-Improvement?
Aug 31, 2026Jiajun Shi, Siyuan Tao, Yuhao Wu +18LLM Agent Evaluation Language Model Self-Assessment
ByteDance Seed
Evaluating and Improving LLM Self-Modeling
Aug 31, 2026Siqi Zeng, Andre N. Assis, Rowan WangLLM Evaluation Language Model Introspection
University of Illinois · Constellation · Anthropic
Sleight of Word Benchmark: Can Language Models Notice If Their Own Output Was Tampered With?
Aug 30, 2026Alberto CetoliLanguage Model Generation Evaluation Language Model Self-Assessment
Cleo
Explanatory Engagement Under Rare Anomalous Failure: Asymptotic Rarity in Model Behavior (or: The Asymptotic AI)
Aug 13, 2026Sam MaoLanguage Model Self-Assessment LLM Reliability
New York University | Interactive Media Arts
Reinforcing Step-level Reasoning for Effective Self-Correction in LLMs
Aug 12, 2026Vu Duc Anh, Nhat M. Hoang, Do Xuan Long +3LLM Self-Correction Direct Preference Optimization
Nanyang Technological University, Singapore · National University of Singapore · Institute for Infocomm Research (I2R), A*STAR +1
The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleads
Aug 5, 2026Yushi Sun, Yanjie Zhang, Rui ShengLLM Evaluation LLM Personalization
LIGHTSPEED, Shenzhen, China · The Hong Kong University of Science and Technology, Hong Kong, China · Tencent LIGHTSPEED
How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models
Aug 3, 2026Andres Algaba, Francesca Carlon, Lynn Delcon +3LLM Evaluation LLM Interpretability
1Data Analytics Lab, Vrije Universiteit Brussel, Pleinlaan 5, 1050 Brussel, Belgium · 2imec-SMIT, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Brussel, Belgium · School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeeds
Jul 31, 2026Yefan Tao, Gerald Friedland, Madhusudhanan Chandrasekaran +1LLM Self-Correction LLM Self-Refinement
Amazon
Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B
Jul 30, 2026Iliya MirzaeiLLM Self-Refinement Language Model Generation Evaluation
Department of Computer Science Stony Brook University
SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute
Jul 30, 2026Hongyu Chen, Liang Lin, Guangrun WangRL for Language Model Reasoning Language Model Self-Assessment
1Sun Yat-sen University · 2Guangdong Key Laboratory of Big Data Analysis and Processing · 3X-Era AI Lab
One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
Jul 30, 2026Cesare Zavattari, Alessandro Tommasi, Giuseppe PrencipeScalable Oversight AI Agent Reliability
Dipartimento di Informatica, Università di Pisa, Pisa, Italy
Rehearse: Stepping Back from the Confidence Cliff in Self-Improving Autoresearch
Jul 30, 2026Jiazhen Ji, Shouhong DingLanguage Model Self-Assessment Autonomous Experimentation
Tencent
When Do Agent Loops Mistake Stagnation for Progress? Self-Evaluation Bias and Externally Grounded Verification in Long-Running Autonomous LLM Agent Loops
Jul 27, 2026Hyundoo Park, Byungho ChoiAI Agent Reliability LLM Self-Refinement
AsyncSite
Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory
Jul 27, 2026Saurabh Ranjan, Konstantina Sokratous, Brian OdegaardHallucination in Language Models Language Model Self-Assessment
Department of Psychology, University of Florida, Gainesville, Florida, USA. · University of Missouri, Missouri, USA.
The Two-Process Theory of Machine Self-Report
Jul 22, 2026Hubert Plisiecki, Filip Chmielewski, Kacper Dudzic +3Personality Modeling in Language Models Language Model Self-Assessment
IDEAS Research Institute · Faculty of Physics and Applied Informatics, University of Lodz
The Knowing-Saying Gap: When Probes See Errors that Confidence Misses
Jul 21, 2026Jyotin Goel, Ipshita Bandyopadhyay, Justin ShenkLanguage Model Error Detection Confidence Estimation in Language Models
Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary
Jul 20, 2026Jan KirinLanguage Model Introspection Language Model Self-Assessment

Oct 5, 2026, cs.CLJ/K move · Enter open · S save

Identifying Introspection From the Inside

David I. Atkinson, Dillon Plunkett, David Bau

Northeastern University · Eleos AI Research

LLM Interpretability Language Model Introspection Language Model Self-Assessment

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Large language models make claims about themselves that are both consequential and increasingly difficult to verify from behavior alone. How can we distinguish plausible confabulations from genuine introspection? In this paper, we identify mechanistic signatures of faithful self-report in a controlled setting. Using low-rank adapters, we train models to make decisions on behalf of fictitious characters, according to latent linear preference functions. We find sustained fine-tuning on an implicit decision task can lead to the emergence of accurate self-reporting of models' learned preferences, even without explicit self-report supervision. We ask two research questions about this emergent phenomenon. First: is the emergence of accurate self-reporting accompanied by a measurable structural change in the model? Weight ablations and frozen-layer experiments together indicate that preference representations shift to earlier layers over training, consistent with the hypothesis that faithful self-report requires preferences to be located where pre-existing verbalization mechanisms can access them. Second: can these structural differences distinguish faithful models from unfaithful ones? Using attribution patching, we find that faithful models exhibit significantly higher attribution similarity between the decision-making and self-report tasks -- a mechanistic signature of faithful self-report that does not require us to understand the content of the report itself. Previous work on self-report has observed behaviorally that models can be faithful or unfaithful; our work proposes that, at least in our restricted setting, it is possible to distinguish between the two patterns of computation by examining the structure of the networks themselves.

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