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:
2452309613
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Elizabeth Phillips (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Task Allocation: 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 |
|---|---|---|---|
| Robots that plan courteously and anticipate how current actions raise future costs for others cut cumulative task costs in simulated persistent environments; a factored anticipatory planner yields double-digit improvements over myopic baselines and meaningful gains over selfish anticipatory planning while keeping per-robot training modular.arxiv | Elizabeth Phillips provider id |
2026-07-22 | 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.