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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2303849884
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Kunihiro Miyazaki (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Firm Revenue: 1 paper
- Other: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Fine-grained task decomposition makes LLM trading agents more profitable in backtests on Japanese stocks, improving risk-adjusted returns versus coarse-grained agent designs; improved alignment of intermediate analyses with decision rules appears to drive the gain.openalex | Kunihiro Miyazaki provider id |
2026-02-26 | 5 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 5 cumulative citations. This is a coverage summary, not an author score or h-index.