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
8Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2363328315
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Wei Xu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Error Rate: 1 paper
- Other: 1 paper
- Task Completion Time: 1 paper
- Adoption Rate: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Large language models drastically reduce false alarms in enterprise static-analysis: hybrid LLM+SAT methods remove 94–98% of false positives in Tencent’s advertising codebase and cut per-alarm review from 10–20 minutes to seconds at negligible cost. Results are promising for developer productivity but are drawn from one company, one customized tool and three bug types, so broader applicability remains to be tested.arxiv | Wei Xu provider id |
2026-01-26 | 11 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 11 cumulative citations. This is a coverage summary, not an author score or h-index.