{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/egonormia-benchmarking-physical-social-norm","title":"EgoNormia: Benchmarking Physical Social Norm Understanding","arxiv_id":"2502.20490","date":"2025-02-27","proceeding":null,"authors":["MohammadHossein Rezaei","Yicheng Fu","Phil Cuvin","Caleb Ziems","Yanzhe Zhang","Hao Zhu","Diyi Yang"],"abstract":"Human activity is moderated by norms. However, machines are often trained without explicit supervision on norm understanding and reasoning, particularly when norms are physically- or socially-grounded. To improve and evaluate the normative reasoning capability of vision-language models (VLMs), we present \\dataset{} $\\|\\epsilon\\|$, consisting of 1,853 challenging, multi-stage MCQ questions based on ego-centric videos of human interactions, evaluating both the prediction and justification of normative actions. The normative actions encompass seven categories: safety, privacy, proxemics, politeness, cooperation, coordination/proactivity, and communication/legibility. To compile this dataset at scale, we propose a novel pipeline leveraging video sampling, automatic answer generation, filtering, and human validation. Our work demonstrates that current state-of-the-art vision-language models lack robust norm understanding, scoring a maximum of 54\\% on \\dataset{} (versus a human bench of 92\\%). Our analysis of performance in each dimension highlights the significant risks of safety, privacy, and the lack of collaboration and communication capability when applied to real-world agents. We additionally show that through a retrieval-based generation (RAG) method, it is possible to use \\dataset{} to enhance normative reasoning in VLMs.","url_abs":"https://arxiv.org/abs/2502.20490v3","url_pdf":"https://arxiv.org/pdf/2502.20490v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"egonormia-benchmarking-physical-social-norm","repo_url":"https://github.com/open-social-world/egonormia","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"answer-generation","task_name":"Answer Generation"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"rag","task_name":"RAG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2502.20490","atlas_url":"https://app.syntology.ai/?focus=2502.20490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.20490"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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