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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
119783776
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- S. Ravindran (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
- Creativity: 1 paper
- Other: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| An evolutionary twist on dream-replay makes LLM agents more adaptable: CosmoCore-Evo uses mutation and selection of replayed trajectories to deliver up to 35% more novel solutions and 25% faster adaptation to API and library shifts on code-generation benchmarks. Ablations attribute gains to the evolutionary components, though results rest on benchmark and fitness-function choices.arxiv | S. Ravindran provider id |
2025-12-20 | 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.