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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
21859049
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- L. Lu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Task Allocation: 1 paper
- Output Quality: 1 paper
- Error Rate: 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 |
|---|---|---|---|
| Agentic AI trims customer-service handling time but chips away at satisfaction: Taobao's experiment shows faster chats and improved attention to non-AI cases, yet customer ratings fall for AI-resolved conversations. Human interventions patch technical failures effectively, but struggle to assuage emotionally escalated customers unless engaged early and intensively.arxiv | L. Lu provider id |
2026-05-14 | 3 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.