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
8Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
91287124
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Aaron Karlsberg (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Ai Safety And Ethics: 1 paper
- Other: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| Compiling LLM outputs into deterministic code cuts token costs massively and preserves task accuracy: compiled AI reduced token consumption by up to ~57x at scale while matching or improving document- and function-level accuracy. The approach also boosts auditability and detects common prompt-injection and code-safety issues, making it attractive for reliability- and compliance-sensitive enterprise settings such as healthcare.arxiv | Aaron Karlsberg provider id |
2026-04-06 | 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.