2025Research
HarEmb
Classification, Retrieval, and NLP from Embeddings Geometry
★93% Classification, 28x Faster Inference
HarEmb performs classification, retrieval, and NLP tasks by exploiting the geometry of LLM embedding matrices. Results achieved using Qwen2.5-0.5B, a very small model — demonstrating that embeddings geometry carries significant semantic information even at minimal scale. Lightweight components run 28x faster than conventional transformers.
- Client
- Independent — Author-attested
- Role
- Sole author
- Duration
- 2025
- Team
- Solo
Outcomes
93.16%
AG News
90.75%
Emotion
86.01%
IMDB
83.72%
SST-2
0.941
MS MARCO MRR@10
28x
Speedup
<150M
Total Network
<20M
Trainable Params
Highlights
- •Only lightweight forward pass components
- •Retrieval extension with MRR@10 >0.9
- •Throughput: thousands of samples per second
- •Exploits embeddings geometry with lightweight components
EmbeddingsEfficient InferenceNLPContent ModerationRAG