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
1Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2152116547
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Avinash Agarwal (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Output Quality: 1 paper
- Ai Safety And Ethics: 1 paper
- Developer Productivity: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| Publicly reported benchmarks show Indian models score well on older saturated tests but underparticipate in newer agentic and domain-specific evaluations; the paper argues many apparent capability shortfalls may reflect gaps in benchmarking and disclosure rather than true technical weakness and proposes a Benchmark Maturity Index to guide national monitoring.arxiv | Avinash Agarwal provider id |
2026-08-12 | 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.