0. Abstract
FallacyTag is a lightweight reasoning aid designed to surface flawed argument structure in online discourse—gently, interpretably, and without moralizing. Built around modern LLM capabilities and constrained design, it offers sentence-level feedback in the form of informal fallacy cues: not judgments, but structural prompts. This paper presents the concept, sketches its architecture, explores its feasibility across technical, social, and conceptual dimensions, and proposes a framework for deployment in varied modalities (text, audio, video). The core bet is simple: by making reasoning structure visible—even imperfectly—we can foster better reflection without requiring cultural revolutions or cognitive overhauls. FallacyTag isn’t a moderator or a fact-checker. It’s a nudge toward clarity, designed to fit where logic matters and tone is fragile. The paper is exploratory in form, rigorous in ambition, and deliberately uneven in tone. It is meant as a public proposal—for critique, adaptation, or reinvention.