The Next Paradigm Is User-Centric Agent, Not Platform-Centric Service
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Abstract (arXiv)
Modern digital services have evolved into indispensable tools, driving the present large-scale information systems. Yet, the prevailing platform-centric model, where services are optimized for platform-driven metrics such as engagement and conversion, often fails to align with users' true needs. While platform technologies have advanced significantly-especially with the integration of large language models (LLMs)-we argue that improvements in platform service quality do not necessarily translate to genuine user benefit. Instead, platform-centric services prioritize provider objectives over user welfare, resulting in conflicts against user interests. This paper argues that the future of digital services should shift from a platform-centric to a user-centric agent. These user-centric agents prioritize privacy, align with user-defined goals, and grant users control over their preferences and actions. With advancements in LLMs and on-device intelligence, the realization of this vision is now feasible. This paper explores the opportunities and challenges in transitioning to user-centric intelligence, presents a practical device-cloud pipeline for its implementation, and discusses the necessary governance and ecosystem structures for its adoption.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2026.
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Citing papers per year: 2026: 8.
8 papers.
- 2026Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendationcited by 9
- 2026SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generationcited by 6
- 2026Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibrationcited by 4
- 2026Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillationcited by 3
- 2026IE as Cache: Information Extraction Enhanced Agentic Reasoningcited by 3
- 2026AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMscited by 1
- 2026Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Rankingcited by 0
- 2026The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shapecited by 0
- 2026AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs26 Aug 2026
- 2026The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape28 Jul 2026
- 2026Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking17 Apr 2026
- 2026IE as Cache: Information Extraction Enhanced Agentic Reasoning16 Apr 2026
- 2026Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration11 Apr 2026
- 2026SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation17 Mar 2026
- 2026Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation7 Mar 2026
- 2026Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation18 Feb 2026
Similar, not cited
Close in wording, no citation either way
- 2026LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
- 2025LLM Agents Are the Antidote to Walled Gardens
- 2026SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
- 2025Build Agent Advocates, Not Platform Agents
- 2026From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management
- 2026PAAC: Privacy-Aware Agentic Device-Cloud Collaboration
- 2026DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environments
- 2026Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents