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  "path": "/t/research-proposal-v1-3-t-local-decision-field-modification-in-llms-via-axiomatic-prompting/173974#post_2",
  "publishedAt": "2026-03-11T02:55:24.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "GitHub - Nyrok/flompt: flow + prompt = flompt - Visual AI Prompt Builder. Decompose, edit as flowchart, recompile into optimized machine-readable prompts · GitHub"
  ],
  "textContent": "The 6-axiom result is the interesting data point. Zero contradictions versus the standard baseline suggests that structured constraint stacking does something to the local distribution beyond just adding tokens. The ordering and logical coherence of the axioms matter, not just the count.\n\nThe A-Frame framing maps closely to what I keep running into at the prompt authoring level. When you decompose a prompt into named semantic blocks (role, constraints, chain-of-thought), you get the same stability properties. The model has explicit anchors rather than having to extract structure from a prose blob. Your “local variance under reformulation” metric sounds like exactly the right way to quantify this gap empirically.\n\nI built GitHub - Nyrok/flompt: flow + prompt = flompt - Visual AI Prompt Builder. Decompose, edit as flowchart, recompile into optimized machine-readable prompts · GitHub around the same intuition, a visual canvas that decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. The block structure enforces an axiomatic discipline at input time. Would be curious whether the A-Frame axioms and the flompt block taxonomy converge on similar structure in practice.",
  "title": "[Research Proposal] v1.3-T: Local Decision Field Modification in LLMs via Axiomatic Prompting"
}