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:
2286705715
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Pavel Laskov (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Other: 1 paper
- Governance And Regulation: 1 paper
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Firm Revenue: 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 |
|---|---|---|---|
| Invisible manipulations of news headlines can steer LLM-based trading signals and materially erode returns—backtests show single-day attacks reducing annual returns by as much as 17.7 percentage points; common scraping libraries and platforms appear susceptible, raising urgent governance and security concerns for AI-enabled trading.openalex | Pavel Laskov provider id |
2026-01-19 | 4 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 4 cumulative citations. This is a coverage summary, not an author score or h-index.