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
1Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
23216850
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- M. T. Moghaddam (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 2 papers
- Productivity: 2 papers
- Adoption: 1 paper
- Governance: 1 paper
- Org Design: 1 paper
Claim outcomes
- Adoption Rate: 2 papers
- Error Rate: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Output Quality: 1 paper
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Developer Productivity: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| Rebuild the workspace, not the agent: companies should store operational context as an LLM-native prose substrate so AI agents read, learn and are governed from the same versioned files; this 'substrate inversion' aims to make deployments auditable, coordinated and capable of compounding improvements rather than stalling after pilot runs.arxiv | M. T. Moghaddam provider id |
2026-09-11 | 0 |
| AI coding assistants didn’t raise architectural defects but they did bloat code: in 151 open-source Java projects, adopters saw no change in total architectural smells while code size grew ~12.8%, causing a 6.7% drop in smell density driven by larger codebases rather than cleaner architecture.arxiv | M. T. Moghaddam provider id |
2026-06-11 | 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.