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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2370784341
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jayanta Choudhury (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
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 |
|---|---|---|---|
| Converting telecom KPIs into images for vision-language models halves-or-more inference energy while boosting anomaly-detection accuracy, making VLMs a practical, energy-efficient alternative to text-based LLM inference at the edge; tested across three architectures, VLMs cut token counts dramatically (3.6–10.4x) and deliver 1.8–2.5x measured energy savings with substantial F1/precision gains.arxiv | Jayanta Choudhury provider id |
2026-08-07 | 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.