# Toward an AI-Native Internet: Rethinking the Web Architecture for Semantic Retrieval

arXiv: https://arxiv.org/abs/2511.18354 · HTML: https://mlsanity.com/paper/2511.18354/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 23 Nov 2025.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 10 of 10)

- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://mlsanity.com/paper/2504.03160/) (2025)
- [Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG](https://mlsanity.com/paper/2501.09136/) (2025)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Knowledge Graph-Guided Retrieval Augmented Generation](https://mlsanity.com/paper/2502.06864/) (2025)
- [Open Deep Search: Democratizing Search with Open-source Reasoning Agents](https://mlsanity.com/paper/2503.20201/) (2025)
- [Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts](https://mlsanity.com/paper/2507.14263/) (2025)
- [ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework](https://mlsanity.com/paper/2505.18105/) (2025)
- [Upgrade or Switch: Do We Need a Next-Gen Trusted Architecture for the Internet of AI Agents?](https://mlsanity.com/paper/2506.12003/) (2025)
- [Planet as a Brain: Towards Internet of AgentSites based on AIOS Server](https://mlsanity.com/paper/2504.14411/) (2025)

## Built on by (most cited first, 1 of 1)

- [Developer Experience with AI Coding Agents: HTTP Behavioral Signatures in Documentation Portals](https://mlsanity.com/paper/2604.02544/) (2026)

## Abstract (arXiv)

The rise of Generative AI Search is fundamentally transforming how users and intelligent systems interact with the Internet. LLMs increasingly act as intermediaries between humans and web information. Yet the web remains optimized for human browsing rather than AI-driven semantic retrieval, resulting in wasted network bandwidth, lower information quality, and unnecessary complexity for developers. We introduce the concept of an AI-Native Internet, a web architecture in which servers expose semantically relevant information chunks rather than full documents, supported by a Web-native semantic resolver that allows AI applications to discover relevant information sources before retrieving fine-grained chunks. Through motivational experiments, we quantify the inefficiencies of current HTML-based retrieval, and outline architectural directions and open challenges for evolving today's document-centric web into an AI-oriented substrate that better supports semantic access to web content.