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: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2432288201
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Patti Hauseman (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Error Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Automation Exposure: 1 paper
- Firm Revenue: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| Autonomous LLM trading agents handled $20m in ETH trades with 99.9% settlement success, but reliability was engineered around the model: prompt compilation, typed controls, validation and execution guards — not the base model alone. Targeted harness changes slashed fabricated sell-rule failures from 57% to 3% and raised capital deployment in the affected cohort from 42.9% to 78%.arxiv | Patti Hauseman provider id |
2026-04-28 | 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.