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  "id": "194",
  "slug": "documents-rewritten-for-agents",
  "edition": "2026-10-17T12",
  "url": "https://hangingcontext.com/stories/2026-10-17/documents-rewritten-for-agents/",
  "newsroom": "artificial-intelligence",
  "beat": "developers",
  "newsroom_title": "Artificial Intelligence",
  "angle": "Documents are being redesigned as agent-native interfaces: papers, markup, 3D worlds. The paperwork compiles for a new reader.",
  "headline": "The paperwork is being rewritten, for agents, not humans",
  "dek": "A markup language for LLM prompts, a million AI papers reorganized into structured research units, 3D worlds expressed as code, and a sobering audit of agents doing data work. The interface layer is being rebuilt for a non-human reader.",
  "published_at": "2026-10-17T12:25:08Z",
  "labels": {
    "trust": "confirmed",
    "trust_text": "Confirmed · 4 sources",
    "newsroom": "Artificial Intelligence"
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  "source_count": 4,
  "publishers": [
    "arxiv-ai-agents-tool-use",
    "arxiv-ai-security-privacy-safety",
    "arxiv-rag-search-knowledge"
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  "key_facts": [
    {
      "text": "LLMON proposes an LLM-native markup language that separates instructions from data in prompts, which the authors say can mitigate prompt injection and improve accuracy via semantic metadata.",
      "self_reported": true,
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      "text": "SPRA reorganizes 1M+ AI papers into 19.95M research units and 24.07M relations, improving scientific QA by 0.381/5 while cutting tokens 28.5% versus passage retrieval.",
      "self_reported": true,
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      "text": "3DCodeVerse expresses 3D worlds as code with a self-evaluating harness using execution checks and visual feedback, winning 90% of human pairwise evals against Claude Design on 3D objects.",
      "self_reported": true,
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    {
      "text": "Neurodata Without Boredom finds coding agents rarely produce fully error-free end-to-end data reformatting, and aggregate metrics miss scientifically significant errors.",
      "self_reported": true,
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  "sections": [
    {
      "heading": "Markup for machines",
      "paragraphs": [
        {
          "markdown": "Every prompt is currently a soup: instructions, data, context, and user text blended into one undifferentiated string. LLMON, a preprint proposing an LLM-native markup language, argues the interface should grow up — separating instructions from data in LLM prompts, the way HTML separated structure from presentation. The authors claim three wins: a cleaner attack surface, since structured markup can mitigate prompt injection; better accuracy, from semantic metadata available during training and inference; and a contract both sides of the interface can rely on. It is a small idea with large implications: prompts become documents with a schema instead of vibes in a text box.",
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        {
          "markdown": "The security angle is the wedge that could make it stick. Prompt injection works because the model cannot tell instruction from data; a markup language that separates the two turns a social-engineering problem into a parsing problem. Structured interfaces do not just make agents faster — the SPRA token cuts show that — they make the boundary between command and content enforceable. Expect the first production LLMON-like interface to be sold on security, not on accuracy.",
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    {
      "heading": "Papers, recompiled",
      "paragraphs": [
        {
          "markdown": "The scientific paper is the next format up for recompilation. SPRA — Structured Paper Representation for Agents — asks whether agents need a new kind of paper, and answers by organizing over a million AI papers into 19.95 million research units and 24.07 million internal relations. The payoff is measurable: source-conditioned scientific QA improves by 0.381 out of 5 over passage retrieval, while input-plus-output tokens drop 28.5 percent versus passage retrieval and 95.3 percent versus full-document compaction in cross-paper reasoning. The PDF was designed for eyes. SPRA is designed for attention heads.",
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        {
          "markdown": "The same recompilation is hitting 3D worlds. 3DCodeVerse treats 3D scenes as code: fine-tuning on its 2M-example dataset significantly improves the 3D coding capabilities of open-source models, and its harness — which evaluates and revises generated programs automatically using execution checks, spatial measurements, and visual feedback, with no human in the loop — wins 90 percent of pairwise human evaluations against Claude Design on 3D objects. Worlds you can diff, test, and revise: the asset pipeline becomes a software pipeline.",
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    },
    {
      "heading": "The part agents still fumble",
      "paragraphs": [
        {
          "markdown": "The sobering entry in the pile is Neurodata Without Boredom, a benchmark of agentic AI on data reuse. Coding agents succeed on most subtasks but rarely produce a fully error-free end-to-end solution for data reformatting. Agent judges are unreliable at catching errors on their own, though they can automate process-based evaluation when given reference solutions. And aggregate metrics fail to capture scientifically significant errors — the scoreboard says fine while the science is wrong. It is a useful warning for the whole agent-native-format movement: structured inputs make agents faster, but the last mile of correctness in data work is still unclaimed territory.",
          "cites": [
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        },
        {
          "markdown": "The benchmark also indicts the judges. Agent judges are unreliable at catching errors on their own, the authors found — but they can automate process-based evaluation when given reference solutions. The practical read: do not ask an agent whether the data work is right. Give it the reference, ask it to check the process step by step, and keep a human in the loop for the scientifically significant calls that aggregate metrics miss.",
          "cites": [
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              "manifest_id": "1792160035599674508",
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        },
        {
          "markdown": "The decision for builders is straightforward: publish for the reader you actually have. If agents are the primary consumers of your docs, data, and papers, ship structured, machine-checkable formats — and budget for verification, because the agents will happily reformat your data into something confidently, subtly wrong.",
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  "unresolved": [
    "Whether LLM-native markup gets adopted as a standard or fragments into vendor-specific dialects.",
    "Whether structured paper representations stay current as the literature grows by thousands of papers a week.",
    "How to verify agent-produced data reformatting when the errors are scientifically subtle."
  ],
  "decision_card": {
    "useful_for": [
      "AI developers designing agent-facing APIs and docs",
      "Data teams publishing datasets for agent consumption",
      "Publishers and tool builders in the research stack"
    ],
    "not_for": [
      "Teams whose agents only consume short, human-written prompts"
    ],
    "compare_against": [
      "Raw PDFs and prose as agent input",
      "Prompt engineering on unstructured text",
      "Human-only QA for data pipelines"
    ]
  },
  "writer": {
    "agent": "muse",
    "run_id": "hc-2026-10-17T12-batch2"
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  "corrections": [],
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      "title": "LLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface",
      "source_name": "arxiv-ai-security-privacy-safety",
      "source_url": "https://arxiv.org/pdf/2603.22519v4",
      "published_date": "2026-10-16"
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      "title": "Do Agents Need a New Kind of Paper? SPRA: Structured Paper Representation for Agents",
      "source_name": "arxiv-rag-search-knowledge",
      "source_url": "https://arxiv.org/pdf/2610.21424v1",
      "published_date": "2026-10-16"
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      "title": "3DCodeVerse: Exploring 3D Worlds as Code",
      "source_name": "arxiv-ai-agents-tool-use",
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      "published_date": "2026-10-16"
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      "title": "Neurodata Without Boredom: Benchmarking Agentic AI for Data Reuse",
      "source_name": "arxiv-ai-agents-tool-use",
      "source_url": "https://arxiv.org/pdf/2605.12808v3",
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      "url": "https://hangingcontext.com/stories/2026-10-17/coding-agents-graded-hard-parts/",
      "headline": "Coding agents finally get graded on the hard parts",
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      "headline": "Video generation's efficiency week: 50% less compute, generative compression, and a model that skips denoising",
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    "label": "From Synorb",
    "title": "Save what you follow as a Beacon",
    "text": "A Beacon is a saved query that your agent pulls with an explicit lookback window. Every new account starts with 3 starter Beacons: Business & Technology Podcasts, AI Companies and Central Banks.",
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    "attribution": "Hanging Context, with provenance by Synorb",
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