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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2346069654
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Hongliang Lu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Other: 1 paper
- Developer Productivity: 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 benchmark built from 223 real-world mixed-integer programs reveals that large language models that excel on toy tasks struggle to translate realistic, industrial-scale optimization requirements into correct formulations, exposing practical failure modes invisible at small scale.arxiv | Hongliang Lu provider id |
2026-02-11 | 5 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 5 cumulative citations. This is a coverage summary, not an author score or h-index.