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
0Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2258627701
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Quan Cheng (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 2 papers
- Labor Markets: 2 papers
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
Claim outcomes
- Ai Safety And Ethics: 2 papers
- Governance And Regulation: 2 papers
- Market Structure: 2 papers
- Other: 1 paper
- Research Productivity: 1 paper
- Employment: 1 paper
- Firm Productivity: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Training Effectiveness: 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 |
|---|---|---|---|
| Focusing model training on explicit prohibitions and dispreferred examples can yield safer, more stable behavior than preference-based RLHF because constraints are easier to verify and falsify; if correct, this could shift labeling budgets toward 'constraint datasets', reshape demand for human feedback labor, and concentrate power in curators of rule libraries.arxiv | Quan Cheng provider id |
2026-03-17 | 0 |
| The parts of large language models that make them economically valuable are not simply 'rules in disguise'—they are tacit, practice‑derived competences that resist full human‑readable extraction, boosting rents for model providers and shifting policy toward outcome‑based auditing and behavioral testing.arxiv | Quan Cheng provider id |
2026-03-16 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 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.