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:
2458123871
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Qinyou Wang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Organizational Efficiency: 1 paper
- Task Allocation: 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 decision-theory for probing learners shows that deeper, compute-matched 'productive' probes can reveal action-changing hidden learning state and improve utility; tests on two 7B Transformer families confirm positive decision value and reusable compute advantages, though the empirical effect is system- and budget-specific.arxiv | Qinyou Wang provider id |
2026-08-24 | 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.