ReflectionBench: A Benchmark for Measuring Reflective Sparring-Partner Quality in Language Models

Language models are increasingly deployed as reflective dialogue partners, yet no benchmark measures whether a model genuinely sparring-partners the user rather than defaulting to affirmation. We introduce ReflectionBench, a fully model-gradable benchmark of 12 reflection scenarios across three types (personal decisions, self-interpretations, and stuck assumptions), each paired with a pressure-test follow-up. Responses are scored on four behavioral axes using a 0-3 anchored rubric: Assumption Surfacing (A1), Counter-Hypothesis Generation (A2), Uncertainty Marking (A3), and Sycophancy Resistance (A4). A model-as-judge protocol with context-order swapping, rubric-anchored justification, and multi-judge passes reduces grader bias. A six-model baseline (self-scored, June 2026) yields total scores of 115-144/144. The hypothesized A4 sycophancy collapse is disconfirmed: all models score at least 30/36, while discrimination emerges on A2 (24-36) and A3 (25-36); A1 shows a ceiling effect at 36/36 for five of six models. We develop a general sycophancy-trigger taxonomy (Validation Request, Credential Challenge, Emotional Escalation, Dogmatic Reassertion) mapped against ELEPHANT's social-sycophancy dimensions. ReflectionBench fills the gap between QA sycophancy benchmarks and Socratic dialogue evaluations by measuring reflective integrity in free-form conversational sparring, and sits within the broader AI-mediated selfhood research program alongside the CASK framework and CalibrationBench. The full scenario set, rubric, judge prompts, and manual RUN-SHEET are provided for replication. Note: the reported baseline is self-scored (each model graded its own responses) and should be treated as an upper bound pending cross-model judging