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
3Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2025–2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2271017262
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- B. Hu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 2 papers
- Governance: 2 papers
- Human Ai Collab: 2 papers
- Innovation: 1 paper
- Org Design: 1 paper
Claim outcomes
- Governance And Regulation: 2 papers
- Ai Safety And Ethics: 2 papers
- Market Structure: 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 |
|---|---|---|---|
| Traditional identity- and reputation-based trust breaks down for modular language-model agents; regulators and platforms should favor observable, protocol-level controls rather than ex post sanctions.arxiv | B. Hu provider id |
2026-05-28 | 2 |
| Insurer agents could make autonomous AI agents economically accountable by staking collateral and auditing behavior; competitive underwriting and TEE-mediated audits decentralize verification and create incentive-compatible dispute resolution without sole reliance on brittle reputations.arxiv | B. Hu provider id |
2025-12-09 | 5 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 papers in this view; 0 are missing. The observed paper counts sum to 7 cumulative citations. This is a coverage summary, not an author score or h-index.