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 →
2Distinct papers
13Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2405569615
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Dequan Wang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 2 papers
- Productivity: 2 papers
Claim outcomes
- Other: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Task Allocation: 1 paper
- Team Performance: 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 |
|---|---|---|---|
| Counting compute flips leaderboard outcomes: when latency, tokens and API calls are penalized, models that buy small factuality gains via brute-force tactics (e.g., Best-of-N) lose to leaner agents—MAS-HQ exposes this deployment-relevant trade-off with a reproducible Q-Score.arxiv | Dequan Wang provider id |
2026-07-27 | 0 |
| A new large-scale benchmark finds proprietary autonomous agents outperform open-source counterparts on complex, long-horizon real-world tasks, while exposing wide variation in resource efficiency, self-correction, and tool use — underscoring the need to co-design models and agent frameworks.arxiv | Dequan Wang provider id |
2026-01-16 | 17 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 papers in this view; 0 are missing. The observed paper counts sum to 17 cumulative citations. This is a coverage summary, not an author score or h-index.