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
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2410032747
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Essam Wissam (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
- Task Completion Time: 1 paper
- Output Quality: 1 paper
- Adoption Rate: 1 paper
- Research Productivity: 1 paper
- Skill Obsolescence: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| RL-tuned GPT-5 dramatically improves GPU kernel coding: single-attempt correctness jumps from 43.7% to 77.0%, and an integrated agent beats TorchInductor on 72.9% of problems with a 2.12× geometric mean speedup. The result shows targeted RL post-training can unlock LLM performance in specialized accelerator programming domains where supervised data are scarce.arxiv | Essam Wissam provider id |
2026-02-11 | 4 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 4 cumulative citations. This is a coverage summary, not an author score or h-index.