{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:13:01Z","timestamp":1774645981069,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>The increasing integration of intelligent systems into everyday life has amplified concerns about privacy in Human-Computer Interaction, particularly with the advent of Large Language Models (LLMs) capable of multimodal reasoning. These models, while powerful, have demonstrated vulnerabilities to privacy attacks and unintentional data leakage, especially in contexts involving visual data interpretation. In this work, we propose a novel methodology for privacy-aware planning in autonomous agents by combining image-based analysis with narrative-driven inference. Our approach is embodied in IMMAGENE, a Belief-Desire-Intention (BDI) framework that employs meta-reasoning over textual descriptions of images to inhibit the execution of agent plans when privacy risks are detected. Leveraging a cross-cultural dataset annotated for privacy sensitivity, IMMAGENE learns to identify privacy-threatening content in a cognitively grounded way. We demonstrate that, for the mentioned tasks, the proposed method largely outperforms standalone LLM-based classification in zero-shot settings. Our approach paves the way to safer data handling practices for agents that not only reason effectively in multimodal settings but also incorporate privacy-preserving mechanisms at the core of their decision-making processes.<\/jats:p>","DOI":"10.3233\/faia250965","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:47:20Z","timestamp":1761126440000},"source":"Crossref","is-referenced-by-count":1,"title":["Meta-Reasoning Agents Based on Narrative-Driven Inference for Mitigating Privacy Risks in Multimodal Settings"],"prefix":"10.3233","author":[{"given":"Carmelo Fabio","family":"Longo","sequence":"first","affiliation":[{"name":"National Research Council, Institute of Sciences and Technologies of Cognition, Italy"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Misael","family":"Mongiov\u00ec","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Sciences and Technologies of Cognition, Italy"},{"name":"Department of Mathematics and Computer Science, University of Catania, Italy"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Daniele Francesco","family":"Santamaria","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Catania, Italy"}],"role":[{"role":"author","vocab":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250965","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:47:21Z","timestamp":1761126441000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250965"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250965","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}