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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2409589304
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jorge L. Ruiz Williams (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Output Quality: 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 mathematical theorem and implementation show transformers' attention concentrates on an identifiable manifold, enabling a lossless compression of attention; a Topological Attention kernel delivers measured 159x speedups at 131K tokens and projects >1,200x at 1M tokens, cutting long‑context inference costs by orders of magnitude.arxiv | Jorge L. Ruiz Williams provider id |
2026-02-06 | 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.