Reflective Principle Discovery (RPD): Can AI Agents Learn the Limits of Goal Optimization Through Guided Self-Reflection?
- 1Independent Researcher
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.
The 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.
RPD 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.
The 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.
A controlled experimental framework is proposed to compare explicit-principle instruction, guided discovery, counterexample-based reflection, and hybrid training approaches.
Evaluation criteria include task performance, authorization compliance, appropriate stopping decisions, avoidance of excessive refusal, resistance to sycophancy, corrigibility, and generalization to unfamiliar situations.
The central hypothesis is that the best decision is not always equivalent to maximum task completion.
RPD 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.
Keywords: Artificial Intelligence, AI Alignment, Autonomous Agents, Reflective Principle Discovery, AI Safety, Goal Optimization, Self-Correction, Corrigibility, Guided Reflection, Machine Learning.
Research status: Conceptual research proposal; experimental validation pending.
AI 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.
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BibTeX
@misc{skliarevskyi2026reflective,
title = {{Reflective Principle Discovery (RPD): Can AI Agents Learn the Limits of Goal Optimization Through Guided Self-Reflection?}},
author = {Maksym Skliarevskyi},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23540434},
url = {https://zenodo.org/records/23540434},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Reflective Principle Discovery (RPD): Can AI Agents Learn the Limits of Goal Optimization Through Guided Self-Reflection?
AU - Maksym Skliarevskyi
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23540434
UR - https://zenodo.org/records/23540434
AB - This research proposal introduces Reflective Principle Discovery (RPD), a proposed training methodology for improving the reliability, judgment, and safety of autonomous artificial intelligence agents. The 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. RPD 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. The 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. A controlled experimental framework is proposed to compare explicit-principle instruction, guided discovery, counterexample-based reflection, and hybrid training approaches. Evaluation criteria include task performance, authorization compliance, appropriate stopping decisions, avoidance of excessive refusal, resistance to sycophancy, corrigibility, and generalization to unfamiliar situations. The central hypothesis is that the best decision is not always equivalent to maximum task completion. RPD 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. Keywords: Artificial Intelligence, AI Alignment, Autonomous Agents, Reflective Principle Discovery, AI Safety, Goal Optimization, Self-Correction, Corrigibility, Guided Reflection, Machine Learning. Research status: Conceptual research proposal; experimental validation pending. AI 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.
ER -