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%) | 3.00 |
| 70-90% | 0 (0%) | N/A |
| 90-100% | 0 (0%) | N/A |
| Total | 1 (100%) | 3.00 |
| Title | Abstract | Avg Rating | Quantity AI Content | Reviews | Pangram Dashboard |
|---|---|---|---|---|---|
| Causally Disentangled World Models: Guiding Exploration with an Agency Bonus | Model-Based Reinforcement Learning (MBRL) promises to improve sample efficiency, yet conventional world models learn a purely observational, black-box model of dynamics. This leads to causal confoundi... | 3.00 | 64% | See Reviews | View AI Dashboard |