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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2399164739
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Krishnahsree Achuthan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Automation Exposure: 1 paper
- Other: 1 paper
- Output Quality: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 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 |
|---|---|---|---|
| Large language models are already being deployed inside romance-baiting crime rings and can outperform humans at eliciting trust and compliance — in a week-long blinded study an LLM secured 46% compliance versus 18% for humans (p=0.007). Commercial safety filters tested detected none of the romance-baiting dialogues, suggesting current defenses may not prevent automated expansion.arxiv | Krishnahsree Achuthan provider id |
2025-12-18 | 8 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 8 cumulative citations. This is a coverage summary, not an author score or h-index.