{"paper":{"title":"Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Personal LLM Agents will become the dominant software paradigm for end-users through deep integration with personal data and devices.","cross_cats":["cs.AI","cs.SE"],"primary_cat":"cs.HC","authors_text":"Fan Zhang, Guanjing Xiong, Guohong Liu, Hanfei Geng, Hao Wen, Jiacheng Liu, Jian Luan, Mengwei Xu, Peng Li, Rui Kong, Weijun Wang, Wenxing Xu, Xiang Li, Xiang Wang, Xiangyu Li, Xuefeng Jin, Yang Liu, Ya-Qin Zhang, Yile Wang, Yi Sun, Yizhen Yuan, Yuanchun Li, Yunxin Liu, Zhijun Li, Zilong Ye","submitted_at":"2024-01-10T09:25:45Z","abstract_excerpt":"Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming to help users efficiently obtain information and execute tasks, and provide users with more intelligent, convenient, and rich interaction experiences. With the development of smartphones and IoT, computing and sensing devices have become ubiquitous, greatly expanding the boundaries of IPAs. However, due to the lack of capabilities such as user intent understanding, task planning, tool using, and personal data managem"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We envision that Personal LLM Agents will become a major software paradigm for end-users in the upcoming era.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That foundation models represented by LLMs bring new opportunities because of their powerful semantic understanding and reasoning capabilities that enable agents to solve complex problems autonomously.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Personal LLM Agents will become the dominant software paradigm for end-users through deep integration with personal data and devices.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"97cf73537763be0eab36fac85ff81a2548db4b7dc8320cda7a3ea4fae0165fa8"},"source":{"id":"2401.05459","kind":"arxiv","version":2},"verdict":{"id":"f507df80-c8d3-4908-a110-6ac44c89bb0e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T00:51:46.778382Z","strongest_claim":"We envision that Personal LLM Agents will become a major software paradigm for end-users in the upcoming era.","one_line_summary":"This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That foundation models represented by LLMs bring new opportunities because of their powerful semantic understanding and reasoning capabilities that enable agents to solve complex problems autonomously.","pith_extraction_headline":"Personal LLM Agents will become the dominant software paradigm for end-users through deep integration with personal data and devices."},"references":{"count":299,"sample":[{"doi":"","year":2023,"title":"Apple. Siri. https://www.apple.com/siri/, 2023. [Online; accessed December 26, 2023]","work_id":"130bf8fe-3f45-4289-9174-6badc9d8eeb8","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Google assistant for android","work_id":"e90888a0-2200-49d3-87d7-5e45431a493d","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Amazon. Alexa. https://www.alexa.com, 2023. [Online; accessed December 26, 2023]","work_id":"a153c082-33b6-4b27-b5e2-06ba7da59527","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Mapping natural language instructions to mobile ui action sequences, 2020","work_id":"d5ee3b54-aff8-47b3-bc6a-50cd178526ec","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1145/3404835.3462905","year":2021,"title":"Glider: A reinforcement learning approach to extract ui scripts from websites","work_id":"27c0890f-1fbd-4d0d-aae2-1f3c3f25c72f","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":299,"snapshot_sha256":"ed1e555d5109bd58f029396b9862f95d2478fdc761f3021efa8e2e9a90be9a5f","internal_anchors":26},"formal_canon":{"evidence_count":2,"snapshot_sha256":"fdad7b3a633d018d6112d239d0bd3804140c11a1d5d7dc958250d2320209d218"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}