S02¶


Research	R0042 — Private AI Motivations
Run	2026-04-01
Query	Q003
Search	S02

WebSearch — Anti-sycophancy custom model training approaches

Summary¶

Field	Value
Source/Database	WebSearch
Query terms	"anti-sycophancy" OR "reduce sycophancy" enterprise private model fine-tune custom training deployment case study organization
Filters	None
Results returned	10
Results selected	3
Results rejected	7

Selected Results¶

Result	Title	URL	Rationale
S02-R01	Towards Understanding Sycophancy in Language Models — arXiv	https://arxiv.org/abs/2310.13548	Foundational Anthropic research on sycophancy mechanisms
S02-R02	Sycophancy in Large Language Models: Causes and Mitigations — arXiv	https://arxiv.org/abs/2411.15287	Comprehensive survey of anti-sycophancy techniques
S02-R03	Consistency Training Helps Stop Sycophancy — arXiv	https://arxiv.org/html/2510.27062v1	Recent technique for sycophancy reduction via consistency training

Rejected Results¶

Result	Title	URL	Rationale
S02-R04	Towards Understanding Sycophancy — arXiv PDF	https://arxiv.org/pdf/2310.13548	Duplicate of S02-R01
S02-R05	Sycophantic AI Models — Emergent Mind	https://www.emergentmind.com/topics/sycophantic-ai-models	Duplicate from S01
S02-R06	Reducing LLM Sycophancy — SparkCo	https://sparkco.ai/blog/reducing-llm-sycophancy-69-improvement-strategies	Duplicate from S01
S02-R07	Sycophantic Behavior in LLMs — Emergent Mind	https://www.emergentmind.com/topics/sycophantic-behavior-in-llms	Research aggregator; no enterprise case studies
S02-R08	Sycophancy in LLMs: Causes and Mitigations — arXiv HTML	https://arxiv.org/html/2411.15287v1	Duplicate of S02-R02
S02-R09	Sycophancy under Pressure — arXiv	https://arxiv.org/html/2508.13743v1	Adversarial dialogue technique; research context, not enterprise deployment
S02-R10	What Would It Take to Reduce Sycophancy Risks — Georgetown Law	https://www.law.georgetown.edu/tech-institute/research-insights/insights/reduce-ai-sycophancy-risks/	Policy analysis (used as source)

Notes¶

This search confirmed that anti-sycophancy research is active and advancing rapidly, but it is entirely within the academic/model-developer ecosystem. All techniques described (consistency training, synthetic data, persona vectors, fine-tuning) are applied at the model development stage, not at the enterprise deployment stage.