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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2400425103
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Alexandros Christoforos (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Error Rate: 1 paper
- Firm Revenue: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Output Quality: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| LogicGaze finds state-of-the-art vision-language models routinely hallucinate causal links in images and short videos, accepting plausible-but-false statements; such grounding failures pose tangible deployment, trust and regulatory risks for multimodal AI products.arxiv | Alexandros Christoforos provider id |
2026-01-30 | 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.