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:
2218701434
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Aayush Gupta (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Org Design: 1 paper
Claim outcomes
- Other: 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 |
|---|---|---|---|
| A new benchmark reveals LLM agents are less production-ready than single-run scores imply: semantically equivalent input changes and simulated API faults cut success rates substantially, with rate limiting the most damaging failure mode; ReAct agents and Gemini 2.0 Flash prove more robust and cost-efficient than competitors.arxiv | Aayush Gupta provider id |
2026-01-03 | 26 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 26 cumulative citations. This is a coverage summary, not an author score or h-index.