Agents
static embeddings are extremely fast/cheap compared to dense embeddings, and still quite powerful. one issue is that they lose accuracy over…
static embeddings are extremely fast/cheap compared to dense embeddings, and still quite powerful. one issue is that they lose accuracy over longer text, since the tokens get pooled and averaged late
static embeddings are extremely fast/cheap compared to dense embeddings, and still quite powerful. one issue is that they lose accuracy over longer text, since the tokens get pooled and averaged late interaction models score with MaxSim instead of average pooling, and avoid the pooling accuracy degradation. Can we apply these concepts to static models? This is a fun writeup by @LoganMarkewich - come check it out! https://www.llamaindex.ai/blog/exploring-static-embedding-retrieval Static embeddings offer unmatched throughput, but they also suffer from accuracy loss compared to traditional embedding models. Can we make them better for retrieval? We tried several things: ✅️ raw maxsim scoring on the per-token embeddings ✅️ training a small adapter model ✅️ c…
Related
- Obligatory plug for static embeddings. Are you using static embeddings in your projects? Would love to hear how people are leveraging them, …
- Static embeddings offer unmatched throughput, but they also suffer from accuracy loss compared to traditional embedding models. Can we make …
Source: Jerry Liu (X) | 2026-08-28