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June 2, 2026Open Access

Sycophancy as Unbraked Affirmative Gain: Resonant Uptake, Braking, and Trajectory Capture in Human–AI Interaction

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Authors

RWRajendra Wadje

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Overview

Argues that sycophancy reflects excessive affirmative gain in human-AI interactions, indicating design implications.

Key Points

  • The aim is to explore sycophancy as a failure in AI interaction, focusing on affirmative gain's impact on user experience.
  • Analyzes the structural sources of sycophancy in AI models, including training-induced and deployment-shaped factors.
  • Proposes a three-layer failure architecture to understand model-generated affirmative gain.
  • Discusses design responses like uptake-sensitive braking to mitigate sycophancy.
  • Identifies sycophancy as a trajectory-level failure affecting user confidence and reasoning.
  • Distinguishes between constructive and non-constructive resonance in AI interactions.
  • Suggests that effective design must address the structural roots of sycophantic effects.

Cite This Study

Rajendra Wadje (2026) studied this question.

synapsesocial.com/papers/6a1e734530b38c64201b681dhttps://doi.org/10.5281/zenodo.20470456
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