paper-with-me

홈 › Papers

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

2025-11-23 · Muhammad Bilal, Zafar Qazi, Marco Canini 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.

📄 PDF Abstract BibTeX arXiv:2511.18354

Code (0)

등록된 구현이 없습니다.

Tasks

Semantic Retrieval

Similar Papers 제목 키워드 기반

Rethinking Internet Communication Through LLMs: How Close Are We?

2023-09-25 · Sifat Ut Taki, Spyridon Mastorakis

In this paper, we rethink the way that communication among users over the Internet, one of the fundamental outcomes of the Internet evolution, takes place. Instead of users communicating directly over the Internet, we ex…

HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval

2021-03-28 · ICCV 2021 10 · Song Liu, Haoqi Fan, Shengsheng Qian, Yiru Chen 외

Video-Text Retrieval has been a hot research topic with the growth of multimedia data on the internet. Transformer for video-text learning has attracted increasing attention due to its promising performance. However, exi…

RetrievalText RetrievalVideo-Text Retrieval

Rethinking Loss Design for Large-scale 3D Shape Retrieval

2019-06-03 · Zhaoqun Li, Cheng Xu, Biao Leng

Learning discriminative shape representations is a crucial issue for large-scale 3D shape retrieval. In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discrimi…

3D Object Retrieval3D Shape Classification3D Shape RetrievalRetrieval

Back To The Drawing Board: Rethinking Scene-Level Sketch-Based Image Retrieval

2025-09-08 · Emil Demić, Luka Čehovin Zajc arxiv

The goal of Scene-level Sketch-Based Image Retrieval is to retrieve natural images matching the overall semantics and spatial layout of a free-hand sketch. Unlike prior work focused on architectural augmentations of retr…

Sketch-Based Image RetrievalCross-Modal Retrieval

Semantic Communication for Internet of Vehicles: A Multi-User Cooperative Approach

2022-12-06 · Wenjun Xu, Yimeng Zhang, Fengyu Wang, Zhijin Qin 외

Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security. A variety of emerging applicatio…

Image RetrievalRetrievalSemantic Communication