Papers by Yulong Ao
PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inference (2026.acl-long)
Copied to clipboard
| Challenge: | Existing pruning methods ignore prefill-decode (PD) disaggregation in practice. |
| Approach: | They propose a pruning method that is highly integrated with prefill-decode (PD) disaggregation, enabling more precise pruning of blocks. |
| Outcome: | The proposed method achieves strong performance in both PD disaggregation and PD unified settings, and can be extended to other non-block pruning methods. |
TrimTokenator: Towards Adaptive Visual Token Pruning for Large Multimodal Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing token pruning methods rely on costly calibration or suboptimal importance metrics, leading to redundant retained tokens. |
| Approach: | They propose a token pruning strategy that preserves cross-modal alignment and informational diversity. |
| Outcome: | The proposed method maintains strong performance while reducing tokens by 88.9% on two models. |