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:
2284777109
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Lei Hou (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Other: 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 |
|---|---|---|---|
| Shaping the execution environment, not just agent code, substantially increases LLM agents' ability to autonomously discover scientific solutions: EurekAgent's permission, artifact, budget, and human-in-loop engineering yields state-of-the-art results on multiple benchmarks and even finds a 26-circle packing solution for under $11 in API cost. The findings suggest that designing environments is a key lever for productive, low-cost autonomous research agents, though gains are shown on a narrow suite of tasks and depend on specific model/APIs.arxiv | Lei Hou provider id |
2026-06-11 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 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.