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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
145186673
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Tien Mai (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
- Organizational Efficiency: 1 paper
- Research Productivity: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| A hybrid of classical choice models and modern ML best advances mobility behavior modeling: preserve network feasibility and interpretable rewards, and use IRL/IL and graph/sequence models to scale, integrate data, and capture heterogeneity; but learned rewards or policies are not by themselves interchangeable with preferences or valid policy counterfactuals.arxiv | Tien Mai provider id |
2026-08-15 | 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.