The paper introduces a new method for measuring metacognitive abilities in large language models (LLMs) without relying on self-reports, instead testing how well models can use knowledge of their internal states. Using two experimental paradigms, the authors find that recent frontier LLMs can assess and use their own confidence when answering factual and reasoning questions, and can anticipate and appropriately employ the answers they would give. The study also shows that these abilities are limited in resolution, context-dependent, differ qualitatively from human metacognition, and vary across models with similar capabilities, suggesting post‑training processes influence metacognitive development.
By Christopher Ackerman
arXiv:2610.07186v1 Announce Type: new
Abstract: Large language models make claims about themselves that are both consequential and increasingly difficult to verify from behavior alone. How can we dis...
By David I. Atkinson, Dillon Plunkett, David Bau
The study investigates whether large language models (LLMs) can reliably detect when their own responses have been manipulated by adversarial prefill attacks. Across ten instruction‑tuned LLMs ranging from 3B to 70B parameters and four safety benchmarks, none consistently recognized compromised outputs, with models claiming intent on prefilled responses at an average of 25.3%. The research identifies that introspective signals mainly arise from safety reasoning and refusal, and that training to improve introspection can paradoxically increase attack success, underscoring the fragility of LLM self‑reporting in safety contexts.
By Quang Minh Nguyen, Uzair Ahmed, Taegyoon Kim
Whether large language models (LLMs) can control their own internal representations matters for both machine metacognition and AI safety. A recent study applied neurofeedback to LLMs and claimed that...
arXiv:2603.22161v3 Announce Type: replace
Abstract: Metacognition -- assessing the quality of one's own cognitive performance -- guides adaptive behavior across species. Substantial research demonstr...
By Dharshan Kumaran, Nathaniel Daw, Simon Osindero, Petar Veli\v{c}kovi\'c, Viorica Patraucean
The paper investigates whether large language models (LLMs) can control their own internal representations, a question relevant to machine metacognition and AI safety. Previous work claimed such control using neurofeedback, but the targets were not privileged, allowing third‑party inference from prompts. By redesigning the paradigm to enforce privileged access—mirroring human neurofeedback experiments—the authors find that LLMs fail to reliably control these internal representations, indicating that earlier claims may have relied on superficial mechanisms.
By Koshiro Aoki, Ryota Takatsuki, Gouki Minegishi, Yusuke Haruki, Daisuke Kawahara
arXiv:2607. 15495v1 Announce Type: cross Abstract: Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for verbal report, deliberate control, and flexible reasoning.
By Wes Gurnee, Nicholas Sofroniew, Adam Pearce, Mateusz Piotrowski, Isaac Kauvar, Runjin Chen, Anna Soligo, Paul Bogdan, Euan Ong, Rowan Wang, Ben Thompson, David Abrahams, Subhash Kantamneni, Emmanuel Ameisen, Joshua Batson, Jack Lindsey
arXiv:2606. 32038v1 Announce Type: cross Abstract: When does training language models (LMs) to generate explanations of their predictions yield faithful introspection, rather than superficial imitation?
By Zifan Carl Guo, Laura Ruis, Jacob Andreas, Belinda Z. Li
The study investigates whether open‑weight language models can introspect on their own internal computations. Using the Open‑Weight Masked Introspection (OWMI) framework, researchers intervened on various internal components of eight models and asked them to report whether changes had occurred. Across 78,000 measurements, none of the models reliably distinguished real interventions from sham ones, with AUROC values essentially at chance.
Why It Matters: The findings suggest that current open‑weight models lack the ability to audit their own internal states, highlighting a limitation for oversight that relies on a model’s self‑reporting.
By Emilio Ferrara
arXiv:2603. 01437v2 Announce Type: replace Abstract: As chain of thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a promising tool for interpretability, suggesting the opportunity to understand model decisions through verbalized reasoning.
By Kyle Cox, Darius Kianersi, Adri\`a Garriga-Alonso
arXiv:2608.21664v1 Announce Type: new
Abstract: Safe deployment of increasingly capable models will likely come to rely on latent-space monitoring as a complement to behavioral evaluations, especiall...
By Marek Mateusz Kowalski, Joshua Fonseca Rivera, Uzay Macar, David Demitri Africa
arXiv:2603. 21396v5 Announce Type: replace Abstract: Recent work has shown that LLMs can sometimes detect when steering vectors are injected into their residual stream and identify the injected concept -- a phenomenon termed "introspective awareness.
By Uzay Macar, Li Yang, Atticus Wang, Peter Wallich, Emmanuel Ameisen, Jack Lindsey