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
14Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2126416248
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Asaf Yehudai (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Adoption Rate: 1 paper
- Output Quality: 1 paper
- Error Rate: 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 |
|---|---|---|---|
| Backbone LLM choice, not agent wiring, largely determines how well general-purpose agents perform; within-model architecture swaps still move scores by up to 12 percentage points. Open-weight models repeatedly collapse on certain agent architectures or benchmarks, exposing failure modes hidden by aggregate metrics.arxiv | Asaf Yehudai provider id |
2026-02-26 | 8 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 8 cumulative citations. This is a coverage summary, not an author score or h-index.