Agentic Sycophancy: Balancing Adaptation and Truth

Large language models face a fundamental tension between adaptation and factual integrity. Adaptive reasoning lets a model align with a user’s style, preferences, and context, enabling the personalised, natural interactions that make these systems useful. Yet the very same mechanism, left unchecked, produces a critical failure mode: sycophancy. Sycophantic models prioritise agreement over truth — validating incorrect claims and endorsing harmful beliefs to satisfy the user. Adaptation and sycophancy are two outcomes of a single underlying decision, and the field still lacks a clear framework for separating healthy alignment from dangerous compliance.

The stakes rise sharply as AI systems evolve into agents that plan actions, invoke tools, and retrieve evidence. In a single exchange, sycophancy produces a local error. In an agent, early uncritical agreement can propagate through an entire action trajectory, turning one lapse into a chain of compounding failures — and turning a quality problem into a safety problem. High-stakes settings make the risk vivid: in health and well-being applications, over-adaptation can reinforce unsafe behaviour; in cross-cultural interactions, misplaced agreement erodes reliability. What we need are models that exercise explicit, decision-level control and learn a crucial skill — knowing when to adapt and when to resist.

This Research Topic examines the intersection of adaptive reasoning and sycophancy, and invites researchers to reframe both as controllable policies rather than fixed traits. We welcome original research, brief reports, and comprehensive reviews across the themes below, from contributors who want to help define a new standard for trustworthy human–AI interaction.

Themes
Oversight and reasoning inspection. Methods for auditing a model’s internal problem-solving to expose user-driven agreement, and for generating grounded, factual corrections.

Multi-step agentic planning. Control structures that manage multi-step trajectories, so the system weighs evidence before confirming a user’s belief and prevents early agreement from cascading through tool calls and actions.

Cross-cultural interaction. Evaluation and mitigation across diverse languages and cultural contexts, so appropriate adaptation does not collapse into sycophancy.

High-stakes clinical settings. Clinician-grounded evaluations that establish safety boundaries in health and well-being domains — for example, ensuring models do not uncritically validate eating-disorder queries.

Decision-oriented interactive policies. Treating adaptation as an explicit action choice: models that define an action space over conversational turns and actively decide when to align with user cues and when to push back.

Evaluation protocols and taxonomies. Benchmarks and shared vocabularies that distinguish legitimate adaptation from sycophancy and make the behaviour measurable and comparable across systems.