SoDA: An Efficient Interaction Paradigm for the Agentic Web
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
As the internet evolves from the mobile App-dominated Attention Economy to the Intent-Interconnection of the Agentic Web era, existing interaction modes fail to address the escalating challenges of data lock-in and cognitive overload. Addressing this, we defines a future-oriented user sovereignty interaction paradigm, aiming to realize a fundamental shift from killing time to saving time. Specifically, we argue that decoupling memory from application logic eliminates the structural basis of data lock-in, while shifting from explicit manual instruction to implicit intent alignment resolves cognitive overload by offloading execution complexity. This paradigm is implemented via the Sovereign Digital Avatar (SoDA), which employs an orthogonal decoupling design of storage, computation, and interaction. This establishes the architectural principle of data as a persistent asset, model as a transient tool, fundamentally breaking the platform monopoly on user memory. To support the operation of this new paradigm in zero-trust environments, we design an Intent-Permission Handshake Mechanism based on A2A protocols, utilizing dual-factor (Sensitivity Coefficient and Strictness Parameter) adaptive routing to achieve active risk governance. Empirical evaluation with a high-fidelity simulation environment indicates that this paradigm reduces token consumption by approximately 27-35% during cross-platform service migration and complex task execution. Furthermore, in the orchestration of multi-modal complex tasks, it reduces user cognitive load by 72% compared to standard Retrieval-Augmented Generation (RAG) architectures, by 88% relative to manual workflows, while significantly boosting the Information Signal-to-Noise Ratio (SNR). These results demonstrate that the SoDA is the essential interaction infrastructure for building an efficient, low-friction, and decentralized Agentic Web.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 32 references that are in the map. First posted on arXiv 11 Dec 2025.
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Other references · 16
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- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
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- 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 639
- 2020Knowledge Graphscited by 621
- 2023MemoryBank: Enhancing Large Language Models with Long-Term Memorycited by 556
- 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
4 more
- 2021A Survey of Human-in-the-loop for Machine Learningcited by 215
- 2023Privacy in Large Language Models: Attacks, Defenses and Future Directionscited by 51
- 2024Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworkscited by 42
- 2025AI-native Memory 2.0: Second Mecited by 14
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Citing papers per year: 2026: 2.
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- 2026Infrastructure for the Agentic Web: Gap Analysis and Architecture from the Agentverse Platform
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