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
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2352720552
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- A. Gusak (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 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 |
|---|---|---|---|
| Schema-grounded 'xmemory' turns AI memory from fuzzy search into a verified system-of-record, lifting end-to-end F1 to 97.1% (versus 80–87% for retrieval baselines) and achieving 95.2% on an application task; the result suggests architecture and write-path verification matter more than retrieval scale or raw model strength for production memory workloads.arxiv | A. Gusak provider id |
2026-04-30 | 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.