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
5Unique collaborators
0/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
No provider ID is stored.
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Minseok Kim (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Firm Productivity: 1 paper
- Other: 1 paper
- Decision Quality: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| An LLM-guided validation pipeline makes factor discovery more interpretable and resilient: FaVOR enforces hypothesis-level checks before backtesting and delivers stronger out-of-sample performance in 2025—achieving cumulative excess returns of ~22.25% (IR 1.53) on the CSI 500 and ~11.23% (IR 1.13) on the S&P 500 after costs—while emphasizing structural, hypothesis-aligned factors over return-optimized black-box formulas.arxiv | Minseok Kim unresolved |
2026-08-31 | Missing, not zero |
Citation observation summary
Semantic Scholar supplied counts for 0 of 1 papers in this view; 1 are missing.