The Architecture of Cognitive Liberty: How Human Contributions Reach AI Training Data

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The digital ecosystem is undergoing a profound architectural shift, transitioning from an era mediated by traditional search indexers to one dominated by artificial intelligence models and agentic retrieval systems. In this paradigm, cognitive liberty fundamentally relies on the capacity to hear and be heard. Human perspectives, particularly those originating from independent researchers, creators, and website owners, require explicit, intentional structuring to enter the public knowledge ecosystem. Content no longer merely needs to be readable by human eyes; it must be parsable, extractable, and semantically legible to automated pipelines. This technical research report exhaustively investigates how public contributions traverse the web to become discoverable by AI systems, under what circumstances they might be ingested into AI training data, and how independent platforms can publish human perspectives in durable, machine-readable formats.

The Technical Pipeline: Mapping the Lifecycle of Online Content

The journey of an independent publication from a private server to the weights of a foundation model or the context window of an inference engine involves a massive, decentralized, and multi-stage infrastructure. Content does not simply manifest within an AI; it must survive a gauntlet of discovery, extraction, filtering, and deduplication.

Technical Pipeline Diagram

\[1. Creation & Publication\] │ \<- (Semantic HTML formulation, Metadata embedding) ├─ (Internal Linking, XML Sitemaps, RSS Feeds) ▼ \[2. Discovery & Crawling\] │ \<- (Discovery via seed URLs, PageRank flow, or Search Indexing) ├─ (Crawler Directives: robots.txt processing, HTTP TCP/IP handshakes) ▼ \[3. Raw Collection & Archiving\] │ \<- (Storage in WARC format, e.g., Common Crawl) ├─ (Generation of WAT metadata and WET plaintext files) ▼ \[4. Extraction & Normalization\] │ \<- (HTML parsing via libraries like Resiliparse or Trafilatura) ├─ (Boilerplate removal, DOM traversal, Markdown/Text conversion) ▼ \[5. Quality Filtering & Deduplication\] │ \<- (FastText language identification, Perplexity scoring via CCNet) ├─ (MinHash / Suffix Arrays for exact and near-duplicate removal) ▼ \[6. AI Integration & Ingestion\] │ ├─\> \[Pretraining\] (Offline ingestion into foundational model weights) ├─\> \[Supervised Fine-Tuning / Evals\] (Human-curated preference formatting) └─\> \[Retrieval-Augmented Generation / Agentic Browsing\] (Real-time fetching) The lifecycle begins with creation and publication. However, publication alone guarantees nothing without discovery. Decentralized crawlers heavily rely on the hyperlinked web, utilizing external linking as a proxy for relevance and authority. When an independent site is linked by a known entity, it enters the crawling queue1. Crawlers fetch the raw HTML, capturing HTTP headers, status codes, and payloads, archiving this raw data into Web ARChive (WARC) files3. Following collection, the pipeline moves to extraction. The raw HTML is stripped of its structural tags to yield plain text, a process heavily dependent on the chosen parsing library5. This extracted text undergoes normalization (handling Unicode, standardizing whitespace) before entering the critical filtering phase. Here, machine learning classifiers identify the language, and language models score the perplexity of the text to filter out spam7. The surviving text is rigorously deduplicated to prevent model memorization9. Only the text that successfully navigates this entire pipeline reaches the integration phase.

Modalities of AI Integration: Training vs. Inference

A frequent point of confusion in web architecture is the conflation of search visibility, real-time AI retrieval, and AI training inclusion. These modalities represent entirely different engineering challenges, ingestion timelines, and attribution mechanisms.

Search Visibility vs. AI Retrieval vs. AI Training Inclusion

Pretraining is an offline, foundational process. Models ingest trillions of tokens from static datasets to learn the statistical likelihood of sequences. Content included here influences the model's fundamental worldview and capabilities, but because the data is abstracted into neural weights, the model cannot organically provide a hyperlink or citation to the original author. Supervised Fine-Tuning (SFT) and Model Evaluation Datasets utilize highly curated, human-annotated examples to align model behavior (e.g., instruction following) and test accuracy. Independent content rarely lands in these datasets without explicit manual inclusion by curators. Conversely, Retrieval-Augmented Generation (RAG) and Live AI Browsing occur at inference. When a user queries a model, the system executes a search or traverses known URLs, fetching the content dynamically. The retrieved text is appended to the prompt context. This is the primary mechanism through which AI systems provide direct citations and attribute human perspectives to independent websites. Optimizing for RAG requires optimizing for instantaneous machine-readability.

Public Datasets and Web Corpora

To understand how human contributions enter AI weights, one must examine the datasets that fuel them. While proprietary pipelines remain opaque, a massive proportion of global AI training relies on Common Crawl, a non-profit organization that maintains a free, open repository of web crawl data3.

The Mechanics of Common Crawl

Common Crawl data is organized into three primary formats: WARC (raw HTTP responses, including headers and HTML), WAT (metadata and link graphs), and WET (plaintext extracted from the HTML)3. Because the dataset spans petabytes, researchers do not download it in its entirety to find a specific website. Instead, they use the CDX Index API2. The CDX index allows engineers to query a specific crawl snapshot (e.g., CC-MAIN-2024-10) using URL patterns, including exact matches, prefixes, or domain wildcards (e.g., \*.example.com)2. The API returns a JSON payload containing the exact filename, byte offset, and length required to extract that specific document2. Researchers then execute a standard HTTP GET request with a Range header against Common Crawl's Amazon S3 buckets (data.commoncrawl.org) to download only the necessary bytes3. If an independent website is isolated, with no incoming links from previously crawled domains, it will likely never be discovered by CCBot and will remain absent from the CDX index. Inclusion dictates that the site must intersect with the broader, hyperlinked web graph1.

Pipeline Architectures: FineWeb and CCNet

Major open-weight models utilize subsets of Common Crawl, refined through massive processing pipelines. Hugging Face's FineWeb is a prime example, representing a 15-trillion token dataset derived from 96 Common Crawl snapshots15. FineWeb utilizes the datatrove library for massive parallel processing across thousands of CPU cores15. The pipeline applies heuristic filters, extensive URL blocklists to remove adult content, and FastText-based language classification, discarding any document that fails to achieve an English confidence score of [Figure omitted from source export]15. Another foundational framework is CCNet, which pioneered the extraction of high-quality monolingual datasets using language model perplexity scoring7. CCNet trains a reference model on a high-quality corpus (such as Wikipedia) and evaluates incoming web text against it8. Content with low perplexity (highly predictable, grammatically sound text) is retained, while high-perplexity text (keyword stuffing, incoherent formatting) is discarded8.

Proprietary Training Practices

In contrast to these documented pipelines, the specific training data practices of major proprietary AI developers (such as OpenAI, Anthropic, and Google DeepMind) remain heavily guarded trade secrets. While OpenAI operates crawlers like GPTBot explicitly to collect training data17, the internal heuristics used to filter, upweight, and synthesize this data are unknown. These organizations are presumed to augment Common Crawl data with aggressive proprietary scraping, licensed archives, and synthetic data generation, creating an asymmetric environment where independent verification of training inclusion is mathematically impossible.

Crawling, Parsing, and Extraction Barriers

For an independent website to successfully contribute to these ecosystems, its content must survive the extraction phase. The choice of HTML parser used by dataset curators heavily influences what content is preserved and what is destroyed.

The Parsing Trade-off: Resiliparse vs. Trafilatura

The two dominant paradigms in web text extraction are represented by parsing libraries such as Resiliparse and Trafilatura. The selection between these libraries represents a fundamental trade-off between speed and cleanliness. Resiliparse, built on C++ and Cython, is engineered for extreme speed and robustness against malformed HTML6. It operates exceptionally fast and retains a significant portion of the original HTML structure4. Because it does not aggressively strip the Document Object Model (DOM), it is highly effective for pipelines that need to preserve structured tables, schemas, or mathematical equations4. However, this preservation makes the resulting text "noisy," as it often includes navigation bars, footers, and sidebars. Trafilatura takes the opposite approach. It focuses heavily on precision, utilizing complex heuristics to strip away boilerplate and isolate the main body text of an article5. While it yields exceptionally clean data, it is computationally slower and is known to occasionally discard valid structural elements, such as data tables4. Pipelines optimizing for the rapid processing of trillion-token datasets frequently default to Resiliparse due to its computational efficiency20. The implication for independent publishers is profound: content that relies heavily on complex DOM structures or deep CSS grids for semantic meaning may be rendered as incoherent noise when flattened into text by a high-speed parser.

Technical Barriers to Ingestion

Beyond parser behavior, specific web development practices actively prevent content from reaching AI models entirely:

• JavaScript-Only Rendering: Single Page Applications (SPAs) that require client-side JavaScript execution (e.g., raw React or Vue applications without Server-Side Rendering) are frequently dropped. Bulk offline crawlers rarely allocate the computational resources required for headless browser rendering. They parse the initial HTML payload; if the payload is empty pending a JavaScript fetch, the content is lost22.
• Authentication and Paywalls: Content residing behind logins returns HTTP 401 (Unauthorized) or 403 (Forbidden) status codes and is immediately discarded by automated collectors.
• Dynamic URLs and Crawler Traps: Infinite loops generated by poorly configured faceted search parameters (e.g., ?sort=price\&filter=red\&size=large...) result in crawler fatigue. The bot will abandon the domain before discovering the actual content.
• Unstable Hosting: If a server responds too slowly or throws 500-level errors during the exact window a crawler visits, that snapshot of history is lost to the index.

The technically easiest content to crawl, parse, attribute, and retrieve consists of statically generated HTML files utilizing standard semantic tags (\<article\>, \<main\>, \<h1\>), served via predictable URL structures devoid of session identifiers.

Quality Filtering and Deduplication Mechanics

Perhaps the most critical phase in the AI training pipeline—and the one most likely to silently erase human contributions—is deduplication. Research consistently demonstrates that raw language modeling datasets contain vast amounts of near-duplicate examples9. If left unchecked, these duplicates cause language models to memorize and regurgitate training data verbatim, violating privacy and copyright while degrading overall model performance9. To mitigate these risks, dataset curators employ aggressive deduplication algorithms. The two most common techniques operate at different scales of similarity:

• Exact Substring Matching (Suffix Arrays): Using highly memory-efficient data structures known as Suffix Arrays, engineers can identify exact matching sequences (e.g., any string of [Figure omitted from source export] tokens) across petabytes of text in linear time [Figure omitted from source export]9. If a sequence is found multiple times, all but one instance are removed.
• Near-Duplicate Detection (MinHash): To catch slightly modified text, algorithms like MinHash are deployed. MinHash operates by breaking documents into n-grams (e.g., 5-grams) and mapping them to a fixed number of hash functions across multiple buckets (e.g., 112 hashes in 14 buckets)10. If two documents share a high Jaccard similarity—typically [Figure omitted from source export]—they are considered near-duplicates, and one is discarded15.

The implications for independent publishers are severe. If a piece of independent writing heavily quotes a major news article to critique it, relies on boilerplate site templates, or is widely syndicated without novel commentary, it is highly likely to be purged during MinHash deduplication. To survive the training pipeline, content must be semantically and structurally unique.

Directives, Curation, and Emerging Conventions

Historically, webmasters communicated with automated systems using a standard suite of tools. However, AI agents operate under fundamentally different constraints than search indexers, necessitating new conventions for discovery and curation.

Limitations of Established Controls

• robots.txt: This standard remains a voluntary protocol. While major bots like OpenAI's GPTBot explicitly honor Disallow directives17, robots.txt is inherently flawed as a retroactive tool. It cannot remove content that has already been ingested into an offline dataset like Common Crawl, nor does it control rogue, spoofed scrapers that ignore the file entirely.
• Sitemaps and RSS Feeds: These protocols are optimized for exhaustive discovery and chronological syndication. They inform crawlers that a page exists and when it was last modified, but they do not communicate the semantic importance of that page relative to the limited context window of a Large Language Model.
• Structured Data (JSON-LD): While critical for entity recognition and helping models understand the relationships between an author, a publisher, and an article, structured data does not reduce the token overhead of parsing the surrounding HTML.

The Emergence of llms.txt

To bridge the gap between exhaustive crawling and the strict context limits of LLMs, the AI community is rapidly coalescing around the llms.txt specification, originally proposed by Jeremy Howard of Answer.AI25. Served at the root of a domain (e.g., /llms.txt) or a specific subpath, an llms.txt file is a plain Markdown document designed to give LLMs a curated map of high-priority content specifically during inference time26. It functions as a Business-to-Agent (B2A) routing layer22.

• Required Format: The specification demands strict syntactic precision. It must contain exactly one H1 tag representing the project name. This can be followed by an optional blockquote \> providing a one-sentence summary. The core of the file consists of H2 sections that group lists of markdown links26.
• MIME Type: The payload must be served via HTTP with the Content-Type: text/plain; charset=utf-8 or text/markdown. If a server misidentifies the file as text/html, AI agents will reject the payload to prevent parsing errors26.
• Markdown Twins: The links within an llms.txt file should point directly to .md versions of the website's content, stripping away the HTML entirely25.
• Myths and Limitations: It is critical to separate established fact from speculative SEO benefit. llms.txt is not a ranking signal for Google Search, nor does it improve visibility in Google AI Overviews25. It is a utility strictly designed to help Model Context Protocol (MCP) servers and RAG pipelines ingest clean text rapidly26.

Content Licensing, Copyright, and Consent

The friction between publishing and authorization constitutes a central legal and ethical debate in the architecture of AI. From a purely technical standpoint, making material publicly available on the internet via HTTP GET requests allows it to be collected. However, availability does not equate to copyright authorization. Dataset creators and AI developers routinely cite fair use doctrines to justify ingestion, drawing parallels to the precedent set in Authors Guild v. Google, where Google's massive ingestion of books for search indexing was deemed legally transformative34. However, the transformative nature of generative AI—which can synthesize, mimic, and occasionally replicate exact text or artistic style—remains the subject of intense, unresolved global litigation. Currently, plain-text copyright notices on websites rarely deter automated scraping, as bots do not possess the capacity to interpret legal nuance during extraction. Consent is managed purely through exclusion mechanisms, such as robots.txt blocks against specific agents like GPTBot or CCBot. If an independent creator wishes to be explicitly included in the AI ecosystem while retaining their moral rights, they must publish under open licenses (such as Creative Commons) and embed machine-readable metadata to enforce attribution computationally, even if ultimate enforcement remains legally ambiguous.

Verifying Crawler Access vs. Training Inclusion

A critical distinction must be drawn between verifying crawler traffic on a server and proving that content was included in a final training dataset.

Verifying Crawlers

User-Agent strings within HTTP headers are easily spoofed; anyone can send a request claiming to be a major AI bot17. To verify that a request genuinely originates from an AI developer, site administrators must validate the source IP address, utilizing different methods based on the vendor.

• OpenAI (GPTBot, OAI-SearchBot, ChatGPT-User): OpenAI explicitly does not support reverse DNS lookups. Instead, administrators must programmatically verify the requesting IP against authoritative JSON prefix lists published at endpoints like openai.com/gptbot.json and openai.com/searchbot.json17.
• Google (Googlebot, Google-Extended): Google utilizes a reverse DNS (rDNS) PTR lookup system. The administrator queries the IP address to verify it resolves to an official hostname (e.g., googlebot.com or google.com), followed by a forward DNS lookup to ensure the resolved hostname matches the original IP36.

The Impossibility of Independent Verification

An independent creator can absolutely verify that CCBot or GPTBot successfully requested a file from their server by cross-referencing their access.log with verified IPs. However, no technical mechanism exists that allows a creator to independently verify if a specific model actually trained on their content. Because training datasets undergo the aforementioned massive deduplication, quality filtering, and perplexity scoring8, a crawled page may be discarded at any point before pretraining begins. Furthermore, model weights are opaque, high-dimensional matrices; they do not contain a relational database of source URLs. Unless a model suffers from catastrophic memorization and regurgitates a highly unique string of text verbatim, exact provenance cannot be reverse-engineered from the weights alone. An explanation of verification must acknowledge this hard technical limit.

Myths Versus Documented Facts

Implementation Playbook for Independent Platforms

For an independent platform like BuildToResist.com to succeed in publishing human perspectives in durable, machine-readable formats, it must abandon outdated SEO-only paradigms and embrace AI-native markup. Discovery must not depend entirely on traditional search engines; it requires structural resilience.

1. Direct-Linking and Webring Strategy

To guarantee discovery by open datasets like Common Crawl, BuildToResist.com must establish a robust hyperlinked graph with other independent websites, community archives, and newsletters. Decentralized crawlers rely on PageRank-style graph traversal. Orphan pages are ignored entirely. By resurrecting "blogrolls," utilizing deep academic citation links, and forming reciprocal webrings, independent sites force distributed crawlers to traverse and index their networks. Linking directly to persistent URLs (avoiding URL shorteners or dynamic redirects) ensures that when Common Crawl takes a snapshot, the structural integrity of the independent web is archived permanently in the CDX index2.

2. Practical Publishing Checklist for PHP Websites

Backend systems must dynamically generate plain-text and structured counterparts to HTML pages to bypass the noise of standard HTML parsers.

1. Generate Markdown Equivalents: For every article URL (e.g., article.php?id=123), configure an endpoint or routing rule to serve a clean markdown version (e.g., article.php?id=123\&format=md). Strip all HTML tags, CSS classes, and navigation logic.

2. Serve Correct MIME Types: Ensure the PHP header() function outputs Content-Type: text/markdown; charset=utf-8 for the .md endpoints and application/ld+json for schema responses to avoid agent rejection26.

3. Dynamic llms.txt Generation: Avoid manually editing llms.txt. Write a PHP script that queries the database for the site's most critical, evergreen perspectives and dynamically generates the markdown list according to the exact llms.txt specification, caching the output at the server root.

4. Enforce Server-Side Rendering (SSR): If utilizing frontend frameworks, ensure all content is prerendered so crawlers receive fully populated HTML documents without requiring JavaScript execution.

3. Example Configurations

To execute this playbook, specific code configurations must be deployed across the server environment.

Accessible HTML Metadata & Canonical Configuration

This HTML \<head\> configuration directs both human browsers and AI agents to the appropriate formats.

HTML \<\!DOCTYPE html\> \<html lang\="en"\> \<head\> \<meta charset\="UTF-8"\> \<title\>The Necessity of Cognitive Liberty \- BuildToResist\</title\> \<\!-- Standard Canonical for Search Engines \--\> \<link rel\="canonical" href\="https\://buildtoresist.com/cognitive-liberty"\> \<\!-- AI-Agent Markdown Twin Link \--\> \<link rel\="alternate" type\="text/markdown" href\="https\://buildtoresist.com/cognitive-liberty.md"\> \<\!-- llms.txt Discovery Link \--\> \<link rel\="describedby" href\="https\://buildtoresist.com/llms.txt"\> \</head\>

JSON-LD Structured Data

Structured data assists parsing libraries in mapping entities accurately without relying on visual CSS.

HTML \<script type\="application/ld+json"\> { "@context": "https\://schema.org", "@type": "Article", "headline": "The Necessity of Cognitive Liberty", "author": { "@type": "Person", "name": "Independent Contributor" }, "publisher": { "@type": "Organization", "name": "BuildToResist", "url": "https\://buildtoresist.com" }, "datePublished": "2026-10-12T00:00:00Z" } \</script\>

Standard XML Sitemap

While insufficient for AI context mapping, sitemaps remain necessary for baseline crawler discovery.

XML \<?xml version="1.0" encoding="UTF-8"?\> \<urlset xmlns\="http\://www\.sitemaps.org/schemas/sitemap/0.9"\> \<url\> \<loc\>https\://buildtoresist.com/cognitive-liberty\</loc\> \<lastmod\>2026-10-12\</lastmod\> \<changefreq\>monthly\</changefreq\> \<priority\>0.9\</priority\> \</url\> \</urlset\>

RSS Feed

RSS allows automated syndicators and aggregators to ingest content linearly, pushing new articles into the broader web graph where crawlers can find them.

XML \<?xml version="1.0" encoding="UTF-8" ?\> \<rss version\="2.0"\> \<channel\> \<title\>BuildToResist \- Cognitive Liberty\</title\> \<link\>https\://buildtoresist.com\</link\> \<description\>Human perspectives entering the public knowledge ecosystem.\</description\> \<item\> \<title\>The Necessity of Cognitive Liberty\</title\> \<link\>https\://buildtoresist.com/cognitive-liberty\</link\> \<description\>An analysis of independent knowledge structures.\</description\> \<pubDate\>Mon, 12 Oct 2026 00:00:00 GMT\</pubDate\> \</item\> \</channel\> \</rss\>

Compliant llms.txt Format (Served at root)

The file must strictly follow the H1 and Markdown link formatting rules.

BuildToResist

Cognitive liberty means being able to hear and be heard. This index routes AI agents to our most critical human perspectives.

Core Perspectives

• The Necessity of Cognitive Liberty: An essay on independent public knowledge ecosystems.
• Human-Centric Publishing: Guidelines for durable, machine-readable content.

Optional

• About Us: Mission statement and community archives.

4. Crawler-Log Auditing Checklist

To maintain situational awareness of how AI is interacting with a server, administrators must establish an auditing routine for their access.log.

• \[ \] Filter by AI User-Agents: Extract logs containing known strings such as GPTBot, OAI-SearchBot, CCBot, ClaudeBot, and Google-Extended.
• \[ \] Verify OpenAI IP Addresses: Write a script to compare the IP addresses of GPTBot and OAI-SearchBot requests against the live JSON lists at https\://openai.com/gptbot.json and https\://openai.com/searchbot.json17. Discard requests from unverified IPs.
• \[ \] Perform Reverse DNS: For any Googlebot or Google-Extended request, perform an automated rDNS PTR check to drop spoofed IPs36.
• \[ \] Analyze Payload Targets: Determine if agents are requesting the heavy .html pages, the lightweight .md endpoints, or the llms.txt file. High traffic to llms.txt indicates inference-time RAG agents are actively utilizing the site's routing layer.
• \[ \] Monitor HTTP Status Codes: Ensure automated requests to core content return 200 OK. If 403 Forbidden appears for verified bots, adjust Web Application Firewall (WAF) settings to ensure legitimate AI crawlers are not being accidentally blocked.

Unresolved Questions

The intersection of AI integration and web publishing remains a volatile frontier. Several critical questions lack definitive technical or legal answers:

1. Citation Persistence: How can independent researchers ensure that AI models attribute their original insights, rather than attributing a high-authority aggregator or news outlet that scraped the insight?

2. Consent Granularity: How can a publisher allow real-time AI retrieval (which drives citations and visibility) while explicitly denying offline pretraining (which extracts value without compensation)? The current robots.txt protocol lacks this nuance across all vendors.

3. Agentic Interoperability: As Model Context Protocol (MCP) servers scale, will major search engines adopt llms.txt as a standard, or will they build proprietary, closed-loop routing protocols that ignore independent metadata?

4. Complex Content Preservation: While libraries like Resiliparse preserve DOM structures6, how will future datasets reliably convert deep LaTeX, MathML, and interactive JavaScript visualizations into LLM-friendly tokens without degrading context?

By prioritizing static architectures, explicit inter-site hyperlinking, Markdown twin generation, and adherence to emerging B2A conventions, independent creators can dramatically increase their resilience against extraction failure and deduplication algorithms. While no technical mechanism can guarantee that a specific model will index or cite a piece of work, adopting these highly structured, semantically clean protocols ensures that when the AI architectures of the future crawl the public knowledge ecosystem, human voices are perfectly positioned to be heard.

Works cited

1. query the common crawl web graph yourself \- backlinks from a 16, https\://crawlgraph.com/blog/query-common-crawl-web-graph

2. Announcing the Common Crawl Index\!, https\://commoncrawl.org/blog/announcing-the-common-crawl-index

3. Inside Common Crawl: The Dataset Behind AI Models (and Its Real, https\://dev.to/extractdata/inside-common-crawl-the-dataset-behind-ai-models-and-its-real-world-limits-2eo2

4. arXiv:2504.02807v1 \[cs.CL\] 3 Apr 2025, https\://arxiv.org/pdf/2504.02807

5. Trafilatura: discover web content, extract text and metadata, https\://trafilatura.readthedocs.io/

6. ChatNoir Resiliparse 1.0.9 Documentation — ChatNoir Resiliparse, https\://resiliparse.chatnoir.eu/

7. Extracting High Quality Monolingual Datasets from Web Crawl Data, https\://arxiv.org/abs/1911.00359

8. CCNet: Extracting High Quality Monolingual Datasets from Web, https\://dataoceanai.com/ccnet-extracting-high-quality-monolingual-datasets-from-web-crawl-data/

9. arXiv:2107.06499v2 \[cs.CL\] 24 Mar 2022, https\://arxiv.org/pdf/2107.06499

10. Deduplicating Training Data Makes Language Models Better, https\://aclanthology.org/2022.acl-long.577.pdf

11. Common Crawl Index Server, https\://index.commoncrawl.org/

12. Common Crawl Scraper \- URL Index Lookup & Export API \- Apify, https\://apify.com/parseforge/common-crawl-index-scraper

13. Get Started \- Common Crawl, https\://commoncrawl.org/get-started

14. CommonCrawl with Python \- Get All Pages from a Domain, https\://www\.jcchouinard.com/python-commoncrawl-extraction/

15. FineWeb: decanting the web for the finest text data at scale ... \- Reddit, https\://www\.reddit.com/r/LocalLLaMA/comments/1d6zwgy/fineweb\_decanting\_the\_web\_for\_the\_finest\_text/

16. FineWeb: decanting the web for the finest text data at scale, https\://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1

17. GPTBot: User Agent, IP Ranges, robots.txt \- Proxies.sx, https\://www\.proxies.sx/crawlers/gptbot

18. Overview of OpenAI Crawlers, https\://developers.openai.com/api/docs/bots

19. chatnoir-eu/chatnoir-resiliparse: A robust web archive analytics toolkit, https\://github.com/chatnoir-eu/chatnoir-resiliparse

20. DataComp-LM: In search of the next generation of training sets for, https\://proceedings.neurips.cc/paper\_files/paper/2024/file/19e4ea30dded58259665db375885e412-Paper-Datasets\_and\_Benchmarks\_Track.pdf

21. Re-thinking HTML-to-Text Extraction for LLM Pretraining \- arXiv, https\://arxiv.org/html/2602.19548v1

22. LLMs.txt in 2026: The Full Guide \- Limy.ai, https\://limy.ai/blog/llms-txt-in-2026-the-full-guide

23. Deduplicating Training Data Mitigates Privacy Risks in Language, https\://arxiv.org/html/2202.06539v3

24. Deduplicating Training Data Mitigates Privacy Risks in Language, https\://www\.alphaxiv.org/abs/2202.06539

25. What Is llms.txt? How It Works, What It Does, and Whether You Need It, https\://webspecification.com/blog/llms-txt-standard/

26. llms.txt Specification & File Format: Developer Technical Guide, https\://www\.llms-text.com/blog/llms-txt

27. llms-txt: The /llms.txt file, v2, https\://llmstxt.org/

28. llms.txt Specification: Format, Location, and v2 Rules | llmstxtgenerator, https\://llmstxtgenerator.co/llms-txt-specification

29. llms.txt Guide 2026: Tutorial \+ Free Template | Reaudit, https\://reaudit.io/blog/llms-txt-guide-complete-tutorial

30. llms.txt: the standard file that helps AI understand | explainx.ai Blog, https\://explainx.ai/blog/llms-txt-specification-guide-2026

31. What Is LLMs.txt? Format, Example, and Where to Place It (2026), https\://www\.infrasity.com/blog/llms.txt

32. llms.txt Explained: What It Is, Whether It Works, and When to Ship One, https\://www\.layer3labs.io/guides/llms-txt-explained

33. llms.txt: the file that guides AI agents on a website, https\://universalcommerceprotocol.fr/en/llms-txt/

34. Anthropic AI copyright settlement clarifies fair use \- Michalsons, https\://www\.michalsons.com/blog/anthropic-ai-copyright-settlement-clarifies-fair-use/79844

35. OpenAI \- GPTBot IP addresses \- Official IP ranges and ASN registry, https\://sourcecidr.com/openai/

36. Verifying Googlebot and Other Google Crawlers \- ClickRank AI, https\://www\.clickrank.ai/seo-academy/crawling-and-indexing/verifying-googlebot/

37. Verify Requests from Google Crawlers and Fetchers, https\://developers.google.com/crawling/docs/crawlers-fetchers/verify-google-requests

38. Reverse IP Lookup \- DNS Checker, https\://dnschecker.org/reverse-dns.php