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The Emotion-Memory Link: Do Memorability Annotations Matter for Intelligent Systems?
Maria Tsfasman, Ramin Ghorbani, Catholijn M. Jonker, Bernd Dudzik
https://arxiv.org/abs/2507.14084

The Emotion-Memory Link: Do Memorability Annotations Matter for Intelligent Systems?
Humans have a selective memory, remembering relevant episodes and forgetting the less relevant information. Possessing awareness of event memorability for a user could help intelligent systems in more accurate user modelling, especially for such applications as meeting support systems, memory augmentation, and meeting summarisation. Emotion recognition has been widely studied, since emotions are thought to signal moments of high personal relevance to users. The emotional experience of situation…

The AI Memory Gap: Users Misremember What They Created With AI or Without
Tim Zindulka, Sven Goller, Daniela Fernandes, Robin Welsch, Daniel Buschek
https://arxiv.org/abs/2509.11851

The AI Memory Gap: Users Misremember What They Created With AI or Without
As large language models (LLMs) become embedded in interactive text generation, disclosure of AI as a source depends on people remembering which ideas or texts came from themselves and which were created with AI. We investigate how accurately people remember the source of content when using AI. In a pre-registered experiment, 184 participants generated and elaborated on ideas both unaided and with an LLM-based chatbot. One week later, they were asked to identify the source (noAI vs withAI) of t…

Remembering Yvonne Choquet-Bruhat
Thibault Damour
https://arxiv.org/abs/2509.00597 https://arxiv.org/pdf/2509.00597…

Remembering Yvonne Choquet-Bruhat
I describe the impact of some of the mathematical results of Yvonne Choquet-Bruhat on gravitational physics, as well as the evolution of my interactions with her over the years.

SpeechIQ: Speech Intelligence Quotient Across Cognitive Levels in Voice Understanding Large Language Models
Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
https://arxiv.org/abs/2507.19361

SpeechIQ: Speech Intelligence Quotient Across Cognitive Levels in Voice Understanding Large Language Models
We introduce Speech-based Intelligence Quotient (SIQ) as a new form of human cognition-inspired evaluation pipeline for voice understanding large language models, LLM Voice, designed to assess their voice understanding ability. Moving beyond popular voice understanding metrics such as word error rate (WER), SIQ examines LLM Voice across three cognitive levels motivated by Bloom's Taxonomy: (1) Remembering (i.e., WER for verbatim accuracy); (2) Understanding (i.e., similarity of LLM's interpreta…

Crosslisted article(s) found for physics.hist-ph. https://arxiv.org/list/physics.hist-ph/new
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- Remembering Yvonne Choquet-Bruhat
Thibault Damour