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
20Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2390728894
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- W. Herath (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Automation Exposure: 1 paper
- Consumer Welfare: 1 paper
- Firm Revenue: 1 paper
- Innovation Output: 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 |
|---|---|---|---|
| The web's human-first assumption is broken by AI intermediaries; to preserve truth and fair economics the authors propose making agents first-class: agent identity headers and rate limits, a tokenized, intent-based subscription model that charges agents like their human principals, and ATML plus cryptographic provenance to stop self-reinforcing AI content loops.arxiv | W. Herath provider id |
2026-06-17 | 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.