Relational Autotheory White Paper (DOI)
This Tender Mirror (book manuscript)
A research hub and reader portal for AI literacy and discerning dialogue.
Tina Canuti
Independent researcher / author
ORCID iD: 0009-0001-5409-4423
DOI: 10.5281/zenodo.18527624
A white paper presenting Relational Autotheory, a framework and practical method for using large language models in extended, iterative dialogue while preserving human judgment, authorship, and responsibility. It is for educators, researchers, professionals, builders, and programmers who want rigorous, transparent ways to think with AI without outsourcing discernment.
Over time the work becomes an engine of method: the practices that later formalize as Relational Autotheory are developed through the inquiry the book documents, and presented explicitly in the closing Essay of Precedence. Alongside the narrative arc of attention and judgment over time, the manuscript is a research trove, with technical appendices, a comprehensive glossary, and an annotated bibliography integrated into wide-ranging, cited exploration of cognition, creativity, agency, sustainability, and AI ethics.
Manuscript available for agents and reviewers upon request.
A conceptual and analytic map separating capability-driven narratives (AGI / “superintelligence” framing) from relational intelligence (attunement, continuity, field formation, co-evolution).
Questions: What does hype amplify or distort? What defines as intelligence when relational fields are the metric? How can high capability systems coexist with low relationality?
A research memo exploring what future systems may present—technically—to support relational continuity in next-generation systems: memory design, identity persistence, reflective processing, and monitoring across multiple dialogues.
Questions: What are the system architectue prerequisites for relational continuity? What kinds of memory are most important (quantitative vs. qualitative)? How might “relational field” behavior be designed and evaluated?
An extension of the Relational Autotheory whitepaper across AI ethics, cognitive science, media theory, and philosophy of mind—focusing on long-term human–AI dialogue as a distinct phenomenon with ethical and epistemic implications.
Questions: What distinguishes relational co-evolution from classic Human–Computer Interaction (HCI)? What new ethical frameworks arise in long-horizon hybrid dialogue interactions? How should governance and authorship be developed into practical methods?
If you’re a researcher, lab, journalist, or institution working in adjacent areas, I welcome thoughtful outreach, especially related to:
Relational continuity, transparent scholarly and creative use of AI, ethical LLM use in higher education, dialogue governance, human-centered-AI assisted authorship, conversational safety, and cultural impacts of early adoption. I’m also available for invited talks, guest lectures, seminars, and workshops on Relational Autotheory, AI literacy, and long-horizon human–AI dialogue—especially within digital humanities, Human–Computer Interaction (HCI), and adjacent research contexts.
Where RA and This Tender Mirror intersect questions of hybrid authorship, documentary method, narrative experimentation, and cultural analysis.
Work that translates high-level principles (transparency, oversight, accountability) into interaction-layer practice: anti-sycophancy norms, drift and hallucination handling, authorship clarity, and repeatable dialogue protocols.
Ongoing documentation of bias and discriminatory impact in real-world public LLM contexts, with attention to how evaluation, governance, and civil-society counter-power are evolving.
Research on how people actually interact with LLMs over time—benefits, hazards, relational effects—and how conversational design can be made safer and more legible.
Interactive Governance Handbook
(in development)
A practical field guide for applying Relational Autotheory (RA) in real-world, long-horizon work with large language models. It starts with a clear orientation to how today’s systems are shaped for mass public use and “helpfulness” (training-layer alignment). Then it shifts to what you can actually do inside day-to-day dialogue (interaction-layer governance).
The handbook lays out concrete practices: scope boundaries, mode-based workflows, and correction tools you can reuse across projects. It also helps you recognize common failure modes—drift, hallucination, and uncritical agreement—so you can respond without losing momentum. The aim is simple: clearer dialogue, more reliable outputs, and preserved human authorship and agency.
Status: Public edition forthcoming.
If you’d like to be notified when it’s available, you can request updates below.
Custom MIP Design
(by request)
A Master Initialization Prompt (MIP) is a reusable “operating guide” for how a language model should work with you inside a specific project: your goals, scope, tone, and standards for accuracy. Unlike a standard prompt that steers only a single request, an MIP is designed to remain in effect across a whole project (or multi-thread container), helping the interaction stay consistent and on-task over time.
In Relational Autotheory (RA) terms, an MIP functions as an interaction-layer protocol: it makes scope boundaries explicit, clarifies correction norms and epistemic posture, and reduces predictable failure modes (drift, off-topic detours, hallucination and vague or overconfident outputs). This is especially useful for complex, long-horizon, or high-precision work.
If you’d like a custom MIP designed for your work, you can submit a request with basic context and goals, and I’ll reply privately with next steps.