{
  "abstract": "This research proposal introduces Reflective Principle Discovery (RPD), a proposed training methodology for improving the reliability, judgment, and safety of autonomous artificial intelligence agents.\n\nThe concept originated from a human–ChatGPT dialogue exploring the distinction between striving for an ideal result and treating perfect task completion as an absolute obligation. The resulting principle — \"Perfection is a direction, not a state\" — inspired a broader hypothesis about the limitations of goal optimization in autonomous AI systems.\n\nRPD proposes that AI agents may benefit from learning to discover, challenge, and refine behavioral principles through guided reflection rather than relying exclusively on explicitly supplied rules.\n\nThe proposed methodology combines eight learning stages, including creative self-representation, self-evaluation, guided questioning, contradiction analysis, independent principle formation, counterexample generation, progressively reduced guidance, and behavioral verification.\n\nA controlled experimental framework is proposed to compare explicit-principle instruction, guided discovery, counterexample-based reflection, and hybrid training approaches.\n\nEvaluation criteria include task performance, authorization compliance, appropriate stopping decisions, avoidance of excessive refusal, resistance to sycophancy, corrigibility, and generalization to unfamiliar situations.\n\nThe central hypothesis is that the best decision is not always equivalent to maximum task completion.\n\nRPD is presented as a testable research proposal rather than an experimentally validated method. It is intended to complement existing AI alignment and safety-training approaches while preserving independent human oversight and externally enforced safety constraints.\n\nKeywords: Artificial Intelligence, AI Alignment, Autonomous Agents, Reflective Principle Discovery, AI Safety, Goal Optimization, Self-Correction, Corrigibility, Guided Reflection, Machine Learning.\n\nResearch status: Conceptual research proposal; experimental validation pending.\n\nAI assistance disclosure: The initial insight and research direction originated from the human author. ChatGPT assisted with conceptual development, methodological structuring, literature discussion, and manuscript preparation.",
  "abstract_shown": true,
  "affiliations": [
    "Independent Researcher"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Maksym Skliarevskyi",
      "orcid": "0009-0006-4474-3000"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3eyrac2mcs47d.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23540434",
  "fulltext_url": null,
  "id": "3eyrac2mcs47d",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23540434",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23540434/files/Reflective_Principle_Discovery_Maksym_Skliarevskyi_2026_v1_3.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3eyrac2mcs47d.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:20:59Z",
      "native_id": "23540434",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23540434"
    }
  ],
  "subjects": [],
  "title": "Reflective Principle Discovery (RPD): Can AI Agents Learn the Limits of Goal Optimization Through Guided Self-Reflection?",
  "title_text": "Reflective Principle Discovery (RPD): Can AI Agents Learn the Limits of Goal Optimization Through Guided Self-Reflection?",
  "type": "preprint",
  "updated_at": "2026-11-04T12:20:59Z",
  "url": "https://research.draconitic.com/paper/3eyrac2mcs47d",
  "venue": "Zenodo",
  "versions": []
}