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
12Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2347540442
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Koushik Sen (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Adoption Rate: 1 paper
- Innovation Output: 1 paper
- Organizational Efficiency: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| A general-purpose LLM-driven optimizer outperforms specialized tools across six domains—tripling ARC-AGI accuracy (32.5% to 89.5%), cutting cloud scheduling costs by ~40%, and generating CUDA kernels that match or beat PyTorch in 87% of cases; multi-task search and actionable side information materially accelerate and improve outcomes.arxiv | Koushik Sen provider id |
2026-05-19 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.