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:
2402724353
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ran Ding (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Firm Productivity: 1 paper
- 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 |
|---|---|---|---|
| A new MessyKitchens dataset and a Multi-Object Decoder sharply improve single-image multi-object 3D reconstruction—cutting registration errors and object interpenetration across benchmarks; by lowering perception cost and improving physical plausibility, the open release could accelerate robotics, AR/VR and related automation deployments.arxiv | Ran Ding provider id |
2026-03-17 | 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.