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 |
|---|---|---|---|---|---|
| LOSI: Improving Multi-agent Reinforcement Learning via Latent Opponent Strategy Identification | In collaborative Multi-Agent Reinforcement Learning (MARL), agents must contend with non-stationarity introduced not only by teammates’ concurrent decisions but also by partially observable and divers... | 3.00 | 52% | See Reviews | View AI Dashboard |