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
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2302604957
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xianyang Liu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
Claim outcomes
- Other: 1 paper
- Decision 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 |
|---|---|---|---|
| AgenticPay, a new multi-agent negotiation benchmark, finds modern LLMs frequently fail to negotiate efficient, welfare-maximizing deals in simulated buyer–seller markets, exposing gaps in long-horizon strategic reasoning; the open framework aims to standardize research on language-driven agentic commerce.arxiv | Xianyang Liu provider id |
2026-02-05 | 12 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 12 cumulative citations. This is a coverage summary, not an author score or h-index.