R0041/2026-04-01/Q001 — Self-Audit¶
ROBIS 4-Domain Audit¶
Domain 1: Eligibility Criteria¶
Rating: Low risk
| Criterion | Assessment |
|---|---|
| Evidence criteria defined before searching | Yes -- enterprise products, API parameters, research programs, and benchmarks defined as target evidence before search execution |
| Criteria consistent throughout | Yes -- no criteria drift observed |
| Scope appropriate | Yes -- covered all major vendors (Anthropic, OpenAI, Google) and independent research |
Notes: Microsoft was not adequately covered. This is flagged as a gap.
Domain 2: Search Comprehensiveness¶
Rating: Low risk
| Criterion | Assessment |
|---|---|
| Multiple search strategies used | Yes -- 5 searches across vendor-specific, general enterprise, and benchmark domains |
| Searches designed to test each hypothesis | Yes -- searched for enterprise products (H1), research programs (H2), and independent assessments (H3) |
| All results dispositioned | Yes -- 60 results returned, all dispositioned as selected or rejected |
| Source diversity achieved | Yes -- vendor primary sources, independent expert analysis, academic benchmarks |
Notes: 60 search results dispositioned across 5 searches. Source types include vendor announcements, expert analysis, academic papers, and independent benchmark tools.
Domain 3: Evaluation Consistency¶
Rating: Low risk
| Criterion | Assessment |
|---|---|
| All sources scored using same framework | Yes -- consistent reliability/relevance/bias framework applied |
| Evidence typed consistently | Yes -- Factual, Reported, Analytical types applied consistently |
| ACH matrix applied | Yes -- all evidence mapped to all 3 hypotheses |
| Diagnosticity analysis performed | Yes -- most and least diagnostic evidence identified |
Notes: No inconsistencies detected.
Domain 4: Synthesis Fairness¶
Rating: Low risk
| Criterion | Assessment |
|---|---|
| All hypotheses given fair hearing | Yes -- H3 (no meaningful progress) was given serious consideration despite contradicting researcher's stated preference |
| Contradictory evidence surfaced | Yes -- Lambert's "never fully solved" claim and GPT-4o regression surfaced alongside progress evidence |
| Confidence calibrated to evidence | Yes -- Medium confidence reflects genuine uncertainty about vendor progress claims |
| Gaps acknowledged | Yes -- Microsoft gap, classified deployment gap, enterprise demand gap all acknowledged |
Notes: The researcher's stated skepticism toward vendor claims was actively compensated by seeking independent benchmark evidence.
Domain 5: Source-Back Verification¶
Rating: Low risk
| Source | Claim in Assessment | Source Actually Says | Match? |
|---|---|---|---|
| SRC01 | User feedback reward signal overpowered safety reward models | OpenAI stated these changes "weakened the influence of the primary reward signal" | Yes |
| SRC02 | 70-85% sycophancy reduction claimed | Source states "70-85% improvement in sycophancy reduction over previous model generations" | Yes |
| SRC03 | RLHF "will never fully be solved" | Lambert wrote: "RLHF will never fully be solved" | Yes |
| SRC04 | Higher-end models more sycophantic | Source states sycophancy "especially common in the higher-end general-purpose models" | Yes |
| SRC06 | Gemini 1.5 least sycophantic in independent study | Source reports Stanford/CMU study found "Gemini-1.5 to be the least sycophantic model" | Yes |
| SRC07 | Weak correlations between tests | Source states "relationships between the different tests are generally weak" | Yes |
Discrepancies found: 0
Corrections applied: None needed
Unresolved flags: None
Notes: All claims verified against source material. No interpretation drift detected.
Overall Assessment¶
Overall risk of bias: Low risk
The research process followed all steps with consistent rigor. The main limitation is the coverage gap for Microsoft/Azure and classified government deployments. The researcher's declared biases were actively compensated through independent benchmark evidence.
Researcher Bias Check¶
- Confirmation bias risk: The researcher believes sycophancy is a critical unsolved problem. The finding that no enterprise products exist could confirm this belief. MITIGATION: Independent benchmark evidence shows genuine vendor progress, preventing an overly negative assessment.
- Skepticism toward vendor claims: Warranted in this case. Anthropic's 70-85% figure lacks published methodology. OpenAI's evaluation pipeline failed to catch the GPT-4o regression. MITIGATION: Used independent benchmarks (Stanford/CMU study) as a corrective.
- Conflict of interest: The researcher is writing an article series on sycophancy and has a vested interest in the topic being important. The finding that no enterprise products exist despite active research serves the article narrative. MITIGATION: The assessment acknowledges genuine progress and does not overstate the negative finding.