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:
2421825193
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- P. Kaliaperumal (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Organizational Efficiency: 1 paper
- Decision Quality: 1 paper
- Developer Productivity: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Language models have become dramatically cheaper and more capable: between 2020 and mid-2026 token prices fell roughly 60× while agentic coding solve odds rose about 5.8× per year, making recent flagship performance affordable at budget-tier cost. The frontier in 2026 is task-fragmented—no single model dominates—yet simple two-model routing and inference-time sampling recover most of the practical gains.arxiv | P. Kaliaperumal provider id |
2026-08-13 | 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.