Adam Zweiger
80 posts
@AdamZweiger
Working on learning | @MIT_CSAIL
We introduce a new approach for fast and high-quality context compaction in latent space. Attention Matching (AM) achieves 50× compaction in seconds with little performance loss, substantially outperforming summarization and other baselines.
We are sharing an early preview of our ongoing SWE-1.6 training run. It significantly improves upon SWE-1.5 while being post-trained on the same pre-trained model - and it runs equally as fast at 950 tok/s. On SWE-Bench Pro it exceeds top open-source models. The preview model still exhibits some undesirable behaviors like overthinking and excessive self-verification, which we aim to improve. We are rolling out early access to a small subset of users in Windsurf.
We introduce a new approach for fast and high-quality context compaction in latent space. Attention Matching (AM) achieves 50× compaction in seconds with little performance loss, substantially outperforming summarization and other baselines.
