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:
2455447522
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ravi Satya Durga Prasad Yenugula (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Output Quality: 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 lightweight, coordination-free work-stealing pipeline keeps GPU workers busy and tolerates preemption: on a 24GB A10G it sustains up to 3.4× the throughput of static sharding under heavy skew and recovers all tasks when half the workers are killed. The same pipeline shows flan-t5-base can label SST-2 at 94.7% agreement for about $0.0022 per 1,000 items, but performs poorly on irony (49.6%), underscoring task-dependent label quality.arxiv | Ravi Satya Durga Prasad Yenugula provider id |
2026-08-17 | 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.