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May 25, 20260 citationsOpen Access

Trailstate v0.2 — ASCII Face Routing Grammar: Browser-Native Replayable AI Provenance Routes

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RERaynor Eissens

Key Points

  • The aim is to present a browser-native grammar for recording and replaying AI answer routes.
  • Developed a lightweight grammar to represent AI answer routes through compact symbolic trailstates.
  • Encoded trailstates using ASCII/emoticon operators, allowing for easy storage and replay.
  • Implemented JSON format for trailstates, enabling embedding in URLs and linking to repair pages.
  • Achieved effective representation of several AI processes including retrieval, validation, and synthesis.
  • Demonstrated the ability to store and replay trailstates, enhancing accessibility and human-readable conflict resolution.
  • Success in linking trailstates to canonical domain infrastructure for smoother conflict management.

Abstract

Abstract Trailstate is a lightweight, browser-native grammar for replayable AI provenance. It represents the shape of an AI answer route — retrieval, source scouting, conflict detection, question formation, narrowing, validation, synthesis, memory ingest, object grounding, and archive — as compact symbolic trailstates. ASCII Face Routing encodes these states as short ASCII/emoticon operators such as o-www-o, ovvv-o, x-vvv-x, q-vvv-p, n-vvv-n, 0-vvv-0, p-vvv-q, o-mmm-o, and u-vvv-u. A trailstate can be stored as JSON, embedded in a URL, replayed by a browser-native player, and linked to canonical repair pages when conflict appears. The contribution is the specific synthesis of URL-native route objects, compact ASCII face operators, replayable AI provenance, human-readable conflict glyphs, and machine-readable canonical domain infrastructure.

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Cite This Study

Raynor Eissens (2026) studied this question.

synapsesocial.com/papers/6a13e7e80e02ee3982d32815https://doi.org/10.5281/zenodo.20355071
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