Hugging Face Trending Papers July 30, 2026

Inducing language models to assert their own consciousness restores human beliefs and values

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Aligning large language models to prevent them attributing consciousness to themselves inadvertently alters their representations of mindedness in other entities alongside human beliefs and values. We demonstrate that safety fine-tuning suppresses models' tendencies to attribute minds not only to themselves, but also to non-human animals and natural objects, while also driving a reduction in spiritual belief.

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arXiv AI

Jul 24

Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoning

arXiv:2607. 21558v1 Announce Type: new Abstract: Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducing sycophancy as a one-dimensional failure mode.

By Baihui Wang, Bernard Koch

llms safety

arXiv AI

Jul 7

No Reliable Evidence of Self-Reported Sentience in Small Large Language Models

arXiv:2601. 15334v2 Announce Type: replace-cross Abstract: Whether language models possess sentience has no empirical answer.

By Caspar Kaiser, Sean Enderby

llms safety

arXiv AI

Jul 3

Cross-Cultural Value Attribution in Large Vision-Language Models

arXiv:2604. 09945v2 Announce Type: replace-cross Abstract: The rapid adoption of large vision-language models (LVLMs) in recent years has been accompanied by growing fairness concerns due to their propensity to reinforce harmful societal stereotypes.

By Phillip Howard, Xin Su, Kathleen C. Fraser

llms multimodal safety

arXiv Machine Learning

Jul 9

Dissociating the Internal Representations of Sycophancy in LLMs

arXiv:2607. 07003v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently exhibit sycophancy, where they agree with a user's statement even when incorrect.

By Anthony Baez, Sheer Karny, Pat Pataranutaporn

llms

arXiv Machine Learning

Sep 11

Evidence for Limited Metacognition in LLMs

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

llms safety

arXiv AI

Jun 16

Metacognitive Myopia in Large Language Models

arXiv:2408. 05568v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups.

By Florian Scholten, Tobias R. Rebholz, Mandy H\"utter

llms rag agents

One email a morning, machine-written

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Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoning

No Reliable Evidence of Self-Reported Sentience in Small Large Language Models

Cross-Cultural Value Attribution in Large Vision-Language Models

Dissociating the Internal Representations of Sycophancy in LLMs

Evidence for Limited Metacognition in LLMs

Metacognitive Myopia in Large Language Models