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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2301061413
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Haipeng Zhang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Innovation: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Other: 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 |
|---|---|---|---|
| Simulating investors as interacting agents and learning over co-investment networks materially improves prediction of which startups secure financing; on PitchBook data the model raises average precision@10 by about 25% versus standard baselines. While providing multiperspective, interpretable reasoning about group investment choices, the approach rests on simulated investor traits and a single data source, limiting immediate generalization.openalex | Haipeng Zhang provider id |
2025-12-27 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.