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:
2365040080
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Cameron Tice (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Other: 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 |
|---|---|---|---|
| Adjusting pretraining discourse changes model behaviour: training a 6.9B LLM on more aligned descriptions slashed measured misalignment from 45% to 9%, while upweighting misalignment text raised unsafe responses; the effect weakens but remains after post-training, implying pretraining composition matters for alignment.arxiv | Cameron Tice provider id |
2026-01-15 | 20 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 20 cumulative citations. This is a coverage summary, not an author score or h-index.