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 →
2Distinct papers
25Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2310524276
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- A. Gunaratna (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 2 papers
- Governance: 2 papers
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Governance And Regulation: 2 papers
- Ai Safety And Ethics: 2 papers
- Adoption Rate: 1 paper
- Regulatory Compliance: 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 | A. Gunaratna provider id |
2026-06-17 | 0 |
| Embedding governance inside LLM agents yields high compliance in one retail deployment: a four-layer Pre-Action Governance Reasoning Loop delivered 95% compliance and no false human escalations in a production supply-chain workflow; however, the result rests on a single proprietary implementation with limited methodological transparency.arxiv | A. Gunaratna provider id |
2026-04-28 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 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.