arXiv AI July 21, 2026 By Pilsung Kang

Auditing Question-Order Effects in Large Language Models with the QQ Equality: Mechanism Characterization and a Saturation Caveat

arXiv:2607. 17219v1 Announce Type: cross Abstract: Human survey respondents exhibit question-order effects that satisfy the QQ (quantum question) equality, an a priori, parameter-free prediction of the projective quantum question-order model.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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arXiv Machine Learning

Jun 8

Predictable Compression Failures: Order Sensitivity and Information Budgeting for Evidence-Grounded Binary Adjudication

arXiv:2509. 11208v3 Announce Type: replace-cross Abstract: Transformers used for evidence-grounded binary adjudication (e.

By Leon Chlon, Ahmed Karim, Maggie Chlon, MarcAntonio Awada

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arXiv AI

1d ago

Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIs

arXiv:2608. 16391v1 Announce Type: cross Abstract: As large language models become increasingly widespread, third-party providers that deploy open-weight models have become an important part of the ecosystem.

By Xiangfan Wu, Zonghao Ying, Huiyu Wu, Xing Zheng, Huangsheng Cheng, Xiaorong Shi, Jing Guo

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arXiv AI

Aug 11

A Statistical Framework for Auditing Behavioral Dependence and Induced Bias in LLM Judges

arXiv:2604. 07650v2 Announce Type: replace Abstract: The rapid growth of the large language model (LLM) ecosystem raises a critical question: are seemingly diverse models truly independent?

By Chenchen Kuai, Jiwan Jiang, Zihao Zhu, Hao Wang, Keshu Wu, Zihao Li, Yunlong Zhang, Chenxi Liu, Zhengzhong Tu, Zhiwen Fan, Yang Zhou

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arXiv AI

Jun 16

Constitutional Value Potentials: reading and steering internal priority margins in language models

arXiv:2606. 15420v1 Announce Type: cross Abstract: A constitution tells a language model what to value, but little tells us whether it does.

By Tong Che, Rui Wu

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arXiv Machine Learning

6d ago

What Iterated Self-Feeding Probes of Language Models Measure, and a test that separates the construction from the model

arXiv:2608. 10986v1 Announce Type: cross Abstract: A growing class of methods probes a language model by feeding it its own output: self-consistency, iterated refinement, agentic loops.

By Nicol\'as Vera Z\'u\~niga

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arXiv AI

Jul 10

When Thinking Hurts: Epistemic Signals in the Reasoning Chains of Visual Language Models

arXiv:2607. 08059v1 Announce Type: cross Abstract: Uncertainty quantification for visual language models (VLMs) conventionally targets the answer token distribution.

By Mayank Singal

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Related stories

Predictable Compression Failures: Order Sensitivity and Information Budgeting for Evidence-Grounded Binary Adjudication

Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIs

A Statistical Framework for Auditing Behavioral Dependence and Induced Bias in LLM Judges

Constitutional Value Potentials: reading and steering internal priority margins in language models

What Iterated Self-Feeding Probes of Language Models Measure, and a test that separates the construction from the model

When Thinking Hurts: Epistemic Signals in the Reasoning Chains of Visual Language Models