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
12Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2188952800
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Pranav C Mehta (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 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 |
|---|---|---|---|
| Frontier LLMs can autonomously solve a substantial share of realistic software-engineering problems: Claude Opus 4.6 completes 40.5% of tasks on the APEX-SWE benchmark, narrowly ahead of Opus 4.5. The models that perform best are those that clearly separate assumptions from verified facts and apply systematic verification before acting.arxiv | Pranav C Mehta provider id |
2026-01-13 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.