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:
2211583339
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Zihao Lu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Firm Revenue: 1 paper
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Organizational Efficiency: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| Integrating LLM-generated user profiles into DiDi's dispatcher raised prediction AUC by up to 6.14% and produced a measurable lift in platform revenue and service quality (≈+0.47% GMV, +0.33% completion rate) in a 14‑day production A/B test. The deployment also reduced cancel-before-accept rates, showing that utility-aligned LLM profiling can deliver real economic value when engineered for low-latency production use.arxiv | Zihao Lu provider id |
2026-06-17 | 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.