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:
2411006140
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Zulekha Bibi (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
- Organizational Efficiency: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| Making predictive models first-class tools for LLM agents speeds up work: a pilot system that calls a small pricing model inside an LLM workflow generated priced proposals in under 10 minutes versus multiple hours. The pricing tool—trained on 70 real and human-verified synthetic examples—shows strong in-sample predictive performance, but narrow data and a single pilot limit claims about wider productivity gains.arxiv | Zulekha Bibi provider id |
2026-02-15 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.