Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2455449460
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Max Corbett (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Output Quality: 1 paper
- Ai Safety And Ethics: 1 paper
- Task Allocation: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Telling LLM code reviewers how costly mistakes are changes the risk numbers they give and makes decisions worse; separating probability elicitation from policy—combining a policy-free reviewer, an independent monitor, and a coded decision rule—improves calibration and reduces decision loss under equal-cost settings.arxiv | Max Corbett provider id |
2026-08-02 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.