Writing with AI agents turns a paragraph into the outcome of many requests, yet the finished document rarely explains which request produced which change. We introduce Reactant, an interaction paradigm in which authors place typed inline annotations in their...
VQV Signal
RESEARCH
SOURCE-BACKED
TECHNICAL
Who Asked for This? Inline Annotations as Authoring Transactions for Provenance in Agentic Authoring
Writing with AI agents turns a paragraph into the outcome of many requests, yet the finished document rarely explains which request produced which change. We introduce Reactant, an interaction paradigm in which authors place typed inline annotations in their...
Writing with AI agents turns a paragraph into the outcome of many requests, yet the finished document rarely explains which request produced which change. We introduce Reactant, an interaction paradigm in which authors place typed inline annotations in their original documents. A verified transaction protocol...
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength 95%
Technical label SOURCE-BACKED
Public Interest 22
Category RESEARCH
Reader Depth TECHNICAL
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
Public Interest components
Recognizable Entity Score 0
Practical Impact Score 0
Novelty Interest Score 70
Consequence Score 18
Curiosity Score 16
Shareability Score 41
Topics
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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