ICLR 2026 - Submissions
Submissions
Summary Statistics
| Quantity AI Content | Count | Avg Rating |
|---|---|---|
| 0-10% | 0 (0%) | N/A |
| 10-30% | 0 (0%) | N/A |
| 30-50% | 0 (0%) | N/A |
| 50-70% | 1 (100%) | 2.50 |
| 70-90% | 0 (0%) | N/A |
| 90-100% | 0 (0%) | N/A |
| Total | 1 (100%) | 2.50 |
| Title | Abstract | Avg Rating | Quantity AI Content | Reviews | Pangram Dashboard |
|---|---|---|---|---|---|
| XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation | Graph-based Retrieval-Augmented Generation (GraphRAG) extends traditional RAG by using knowledge graphs (KGs) to give large language models (LLMs) a structured, semantically coherent context, yielding... | 2.50 | 55% | See Reviews | View AI Dashboard |