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:
2281767177
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jinyuan Sun (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Consumer Welfare: 1 paper
- Firm Revenue: 1 paper
- Adoption Rate: 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 |
|---|---|---|---|
| Per-token billing for commercial LLMs is vulnerable to large-scale overcharging: providers who conceal the model, tokenizer or execution can inflate reported token usage by hundreds of percent, turning modest honest bills into orders-of-magnitude larger charges; honest pricing will require evidence not controlled by the provider (e.g., attestation, cryptographic proofs, or third-party re-execution).arxiv | Jinyuan Sun provider id |
2026-05-28 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.