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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2333852025
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Tieying Zhang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Organizational Efficiency: 1 paper
- Task Allocation: 1 paper
- Training Effectiveness: 1 paper
- Worker Satisfaction: 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 proactive LLM assistant, Vigil, embeds into on-call customer–analyst dialogues on ByteDance's cloud platform and assists human support throughout the entire case lifecycle rather than only before escalation. Deployed for over ten months, Vigil also auto-extracts lessons from human-resolved cases to update its capabilities, demonstrating practical operational gains in a large-scale production environment.arxiv | Tieying Zhang provider id |
2026-02-25 | 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.