ICLR 2026 - Submissions
Submissions
Summary Statistics
| Quantity AI Content | Count | Avg Rating |
|---|---|---|
| 0-10% | 0 (0%) | N/A |
| 10-30% | 1 (100%) | 5.50 |
| 30-50% | 0 (0%) | N/A |
| 50-70% | 0 (0%) | N/A |
| 70-90% | 0 (0%) | N/A |
| 90-100% | 0 (0%) | N/A |
| Total | 1 (100%) | 5.50 |
| Title | Abstract | Avg Rating | Quantity AI Content | Reviews | Pangram Dashboard |
|---|---|---|---|---|---|
| Generalization and Scaling Laws for Mixture-of-Experts Transformers | We develop a theory of generalization and scaling for Mixture-of-Experts (MoE) Transformers that cleanly separates \emph{active} per-input capacity from \emph{routing} combinatorics. Conditioning on f... | 5.50 | 25% | See Reviews | View AI Dashboard |