Update README.md

README.md

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-We present `KaLM-Reranker-V1`, a fast but not late-interaction (FBNL) reranker that decouples query and passage computation while retaining expressive relevance modeling. Built on an encoder-decoder architecture, KaLM-Reranker-V1 uses the encoder to pre-encode passages with Matryoshka embedding pooling, while the decoder models the system instruction, user instruction, and query intent; cross-attention then captures relevance between the query context and passage representations. This design makes
-KaLM-Reranker-V1 efficient through decoupled passage encoding, yet not late interaction, by preserving rich relevance modeling through cross-attention. We instantiate KaLM-Reranker-V1 in three sizes, `Nano`, `Small`, and `Large`, with `0.27B`, `1B`, and `4B` activated parameters, respectively.
+We present `KaLM-Reranker-V1`, a fast but not late-interaction (FBNL) reranker that decouples query and passage computation while retaining expressive relevance modeling. 
 
+Built on an encoder-decoder architecture, KaLM-Reranker-V1 uses the encoder to pre-encode passages with Matryoshka embedding pooling, while the decoder models the system instruction, user instruction, and query intent; cross-attention then captures relevance between the query context and passage representations. 
+This design makes KaLM-Reranker-V1 efficient through decoupled passage encoding, yet not late interaction, by preserving rich relevance modeling through cross-attention. 
+
+We instantiate KaLM-Reranker-V1 in three sizes, `Nano`, `Small`, and `Large`, with `0.27B`, `1B`, and `4B` activated parameters, respectively.
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+
+![kalm-reranker-v1 architecture](./assets/framework.jpg)
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+Extensive experiments on BEIR, MIRACL, and LMEB show that the KaLM-Reranker-V1 series achieves competitive reranking performance compared with strong industrial rerankers while significantly reducing online overhead.
 
-![kalm-reranker-v1 architecture](./assets/framework.jpg)