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
4Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2353069901
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ihor Stepanov (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Task Allocation: 1 paper
- Decision Quality: 1 paper
- Output Quality: 1 paper
- Training Effectiveness: 1 paper
- Organizational Efficiency: 1 paper
- Other: 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 |
|---|---|---|---|
| A lightweight streaming router uses a decoder-KV GLiClass to pick the best LLM per task, squeezing modest quality gains from cache-aware, zero-shot model scoring; on a 1,000-task subset it edges out the best fixed model (top-1 0.707 vs 0.696) and supports cost- and policy-aware endpoint selection.arxiv | Ihor Stepanov provider id |
2026-09-02 | 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.