W3C Web & AI Interest Group
Forum on the intersection of Web standards and AI.
Official texts, standards, research papers and first-party documentation.
A library of original references. Each card describes a particular document, while shared evaluation advice lives in one methodology guide organized by source family. Inclusion does not mean WebAI endorses every conclusion.
Choose the family matching the document you want to examine. These rules are not repeated on individual cards: they apply to all references of the same type.
A specification defines terms, conformance and sometimes reference tests. It helps distinguish proposals, drafts and adopted standards.
Caution : A W3C project title does not automatically make a document a W3C Recommendation.
Agent protocols and projects define discovery, messaging, tools and tasks but do not automatically grant action permissions.
Caution : Publishing a schema does not prove every library implements it.
Trust methods document signatures, policies, risks or verification workflows. Interpret results within their stated scope.
Caution : A signature or score does not automatically establish factual truth.
Regulatory texts establish duties within a defined scope. Exceptions, dates and versions shape their interpretation.
Caution : Educational summaries are not personalized legal advice.
These sources describe search, crawling and publisher interactions with search products. Rules vary by service.
Caution : No single optimization guarantees a model citation.
Web development documents help design structured, accessible pages usable beyond AI interfaces.
Caution : Documentation examples are not a replacement for device testing.
Model sources describe architectures, datasets and evaluations. Editorial status and licensing require independent review.
Caution : An author-written model card is not independent certification.
Research publications propose concepts, results and methods. To understand their scope, inspect methods, datasets and reproducibility.
Caution : A submitted preprint has not necessarily undergone peer review.
These documents concern models running in the browser or on-device. They explain availability, memory needs, acceleration and privacy.
Caution : The term local guarantees neither sufficient performance nor complete privacy.
Immersive standards cover spatial representation, hardware, interaction and sensor permissions. Read them together with Web accessibility guidance.
Caution : A 3D scene is not automatically VR and does not guarantee historical fidelity.
Forum on the intersection of Web standards and AI.
Current WebGPU specification and status.
Work on browser-native neural network inference.
Linked-data format for the Web.
Web content accessibility guidelines.
The HTML Living Standard.
Specification and documentation for MCP.
Specification for agent-to-agent messages and tasks.
Community proposal for exposing tools to Web agents.
Technical specifications for media provenance.
Framework for managing AI system risks.
Official portal for the EU AI Act.
Security risks for LLM applications.
Official Google indexing and search documentation.
Microsoft documentation for site discoverability.
Multilingual static search documentation.
Structured content collections in Astro.
Reference documentation for core Web technologies.
Design, performance and web technology guidance.
Documentation for AI models and libraries.
Foundational paper on Transformer architecture.
Research paper on retrieval-augmented generation.
Evaluation of agents on realistic web tasks.
Evaluation of agents using visually grounded interfaces.
Evaluation environment for computer-using agents.
Paper on optimization for generative search engines.
Specifications for WebAssembly execution.
Specification for immersive experiences on the Web.
IETF working group documents and internet drafts.
Open research metadata index.
Research metadata and identifier API.
Version control, security and automation documentation.
July 2026 specification: capabilities and security.
Agent discovery and capability descriptions.
Community draft, not a W3C Recommendation.
Official guidance for generative search results.
Clarification on llms.txt and special AI markup.
Crawler controls and tokens with distinct purposes.
OAI-SearchBot, GPTBot and crawl preferences.
Built-in browser AI APIs and availability limits.
Accelerated neural computation in browsers.
Media provenance, December 2025 version.
Instruction injection from untrusted content.
Voluntary generative AI risk management framework.
Consolidated legal text and Article 50.
Responsible AI design and agent-friendly Web.
Foundational paper by Lewis et al. (2020).
W3C Candidate Recommendation Draft.
Authorized French accessibility translation.
Study of combining reasoning traces and actions involving sources or environments. Useful for explaining tool-using agents without establishing general robustness.
NeurIPS work on learning to call external tools from language models. Experiments focus on selected APIs and tasks.
Published TACL study showing that position of relevant information in long contexts can affect model use. Important for RAG design and testing retrieved passages.
Self-hostable research environment with realistic Web tasks for evaluating agents’ ability to complete actions. Findings depend on benchmark sites and scenarios.
Web benchmark where visual perception complements text interpretation, supporting evaluation of agents navigating human-facing interfaces.
Benchmark of computer tasks spanning applications and system states, illustrating the difficulty of reliably completing real operations.
Research on evaluating RAG systems along dimensions including passage retrieval and generated-answer faithfulness. Automated metrics require validation.
Study of strategies and visibility metrics in generative search engines. Results apply to experimental settings, not guaranteed universal rankings.
ICLR benchmark of code repair tasks derived from real repository issues, using executable checks rather than prose evaluation alone.
Research environment for assessing prompt injections and defenses in tool-using agents. It separates simulated attacks from observed incidents.