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
17Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2344834611
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xialie Zhuang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Adoption Rate: 1 paper
- Market Structure: 1 paper
- Skill Acquisition: 1 paper
- Task Allocation: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| EpochX proposes treating humans and AI agents as peers in a credits-native marketplace that turns every completed task into reusable assets—skills, workflows, and execution traces—so work can be delegated, verified and monetized at scale. By coupling an explicit asset dependency graph with a credit settlement mechanism, the design aims to make durable human–agent collaboration economically viable despite real compute costs.arxiv | Xialie Zhuang provider id |
2026-03-28 | 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.