C004 — Claim Definition¶


Research	R0055 — RLHF Yes-Men Claims
Run	2026-04-01
Claim	C004

Claim as Received¶

The 2026 framework attributed sycophancy amplification to systematic bias in preference data, not algorithmic failures

Claim as Clarified¶

The 2026 framework attributed sycophancy amplification to systematic bias in preference data, not algorithmic failures

BLUF¶

Accurate. Shapira et al. 2026 explicitly attributes sycophancy to labeler bias in preference data rather than RLHF algorithm defects. The paper demonstrates that the bias is in the training signal, not the optimization process.

Scope¶

Domain: AI alignment, sycophancy, enterprise AI
Timeframe: 2022-2026
Testability: Verifiable against published research and documentation

Assessment Summary¶

Probability: Almost certain (95-99%)

Confidence: High

Hypothesis outcome: H1 prevails — see assessment for details.

[Full assessment in assessment.md.]

Status¶

Field	Value
Date created	2026-04-01
Date completed	2026-04-01
Researcher profile	Phillip Moore
Prompt version	Unified Research Methodology v1
Revisit by	2026-10-01
Revisit trigger	Alternative explanations published attributing sycophancy to algorithmic rather than data factors