{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:53:00Z","timestamp":1781545980936,"version":"3.54.5"},"reference-count":73,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and communications Technology Promotion","doi-asserted-by":"publisher","award":["RS-2024-00428780"],"award-info":[{"award-number":["RS-2024-00428780"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and communications Technology Promotion","doi-asserted-by":"publisher","award":["RS-2024-00434743"],"award-info":[{"award-number":["RS-2024-00434743"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2026,6,15]]},"abstract":"<jats:p>\n                    Question-answering (QA) interfaces powered by large language models (LLMs) present a promising direction for improving interactivity with HVAC system insights, particularly for non-expert users. However, enabling accurate, real-time, and context-aware interactions with HVAC systems introduces unique challenges, including the integration of frequently updated long-term sensor data, domain-specific knowledge grounding, and coherent multi-stage reasoning. In this paper, we present\n                    <jats:italic toggle=\"yes\">JARVIS<\/jats:italic>\n                    , a two-stage LLM-based QA framework tailored for sensor data-driven HVAC system interaction.\n                    <jats:italic toggle=\"yes\">JARVIS<\/jats:italic>\n                    employs an\n                    <jats:bold>Expert-LLM<\/jats:bold>\n                    to translate high-level user queries into structured execution instructions, and an\n                    <jats:bold>Agent<\/jats:bold>\n                    that performs SQL-based data retrieval, statistical processing, and final response generation. To address HVAC-specific challenges,\n                    <jats:italic toggle=\"yes\">JARVIS<\/jats:italic>\n                    integrates (1) an adaptive context injection strategy for efficient HVAC and deployment-specific information integration, (2) a parameterized SQL builder and executor to improve data access reliability, and (3) a bottom-up planning scheme to ensure consistency across multi-stage response generation. We evaluate\n                    <jats:italic toggle=\"yes\">JARVIS<\/jats:italic>\n                    using real-world data collected from two commercial HVAC systems and a ground truth QA dataset curated by HVAC experts to demonstrate its effectiveness in delivering accurate and interpretable responses across diverse queries. Results show that\n                    <jats:italic toggle=\"yes\">JARVIS<\/jats:italic>\n                    outperforms baseline implemented with modern off-the-shelf LLMs and ablation variants in both automated and user-centered assessments, achieving high response quality and accuracy.\n                  <\/jats:p>","DOI":"10.1145\/3810210","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:06:41Z","timestamp":1781543201000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["JARVIS for HVAC: LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1711-5969","authenticated-orcid":false,"given":"Sungmin","family":"Lee","sequence":"first","affiliation":[{"name":"School of Integrated Technology and BK21 Graduate Program in Intelligent Semiconductor Technology, College of Computing, Yonsei University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0112-8503","authenticated-orcid":false,"given":"Joonhee","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Integrated Technology and BK21 Graduate Program in Intelligent Semiconductor Technology, College of Computing, Yonsei University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9639-130X","authenticated-orcid":false,"given":"Minju","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Integrated Technology and BK21 Graduate Program in Intelligent Semiconductor Technology, College of Computing, Yonsei University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7560-7691","authenticated-orcid":false,"given":"Seungyong","family":"Lee","sequence":"additional","affiliation":[{"name":"LG Electronics Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6076-6962","authenticated-orcid":false,"given":"Dongju","family":"Kim","sequence":"additional","affiliation":[{"name":"LG Electronics Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8861-4356","authenticated-orcid":false,"given":"Jingi","family":"Hong","sequence":"additional","affiliation":[{"name":"LG Electronics Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1021-6342","authenticated-orcid":false,"given":"Jun","family":"Shin","sequence":"additional","affiliation":[{"name":"LG Electronics Inc., Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8512-1615","authenticated-orcid":false,"given":"Pei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of EECS, University of Michigan, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0799-4039","authenticated-orcid":false,"given":"JeongGil","family":"Ko","sequence":"additional","affiliation":[{"name":"School of Integrated Technology, College of Computing, Yonsei Univeristy, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","first-page":"2680","DOI":"10.3390\/buildings13112680","article-title":"Alternative approaches to hvac control of chat generative pre-trained transformer (chatgpt) for autonomous building system operations","volume":"13","author":"Ahn Ki Uhn","year":"2023","unstructured":"Ki Uhn Ahn, Deuk-Woo Kim, Hyun Mi Cho, and Chang-U Chae. 2023. 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