Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Dense Verifiable Rewards
该研究通过结合压缩视觉编码器、混合专家解码器和快速推测解码头，解决了低预算GPU上高效文档解析的问题，并使用密集可验证奖励进行优化。
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omni-macos: On-Device Omni-Modal Search on Apple Silicon
This work addresses the challenge of unifying text, code, documents, images, audio, and video in on-device multimodal search under stringent local compute and memory constraints. It presents the first end-to-end, full-modality multimodal search system deployed entirely on Apple Silicon Macs, integrating encoders, indexing, and storage while ensuring complete data locality. The system innovatively combines multimodal embeddings, quantized indexing, incremental re-encoding, fine-grained GPU acceleration, and memory-aware scheduling, all coordinated through a unified memory allocation mechanism that adheres to user-specified memory budgets. Evaluated across five Mac devices spanning an 8× range in NPU compute and a 32× range in memory capacity, the system consistently achieves efficient background indexing and real-time interactive search performance.
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jina-reranker-v3.5: An Efficient Listwise Reranker with Hybrid Attention and Self-Distillation
This work addresses the challenge of balancing inference efficiency, cross-domain robustness, and the ability to handle semi-structured data in listwise reranking models. We propose an efficient 0.6B-parameter listwise reranker based on a last-but-not-late (LBNL) interaction architecture, which innovatively integrates sliding-window and global attention mechanisms—comprising three local and two global attention layers—and leverages multi-domain mixed training combined with a three-stage self-distillation strategy. The resulting model achieves 63.20 nDCG@10 on BEIR, matching the performance of 4B-scale models, outperforms prior approaches on MIRACL and RTEB benchmarks, and yields a 9.6-point gain in nDCG@10 on semi-structured retrieval tasks while reducing inference latency by up to 1.56×.
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Test-Time Compute for Dense Retrieval: Agentic Program Generation with Frozen Embedding Models
This work addresses the challenge of enhancing retrieval performance of frozen small-scale embedding models without retraining. It proposes a test-time computation approach that leverages agent-based program search to automatically generate and evaluate inference programs for optimizing dense retrieval. The key insight is the discovery of a parameter-agnostic, unified algebraic formulation: interpolating the original query vector with the softmax-weighted centroid of its local top-K retrieved documents. This method consistently improves nDCG@10 across seven embedding model families spanning an order-of-magnitude difference in parameter count and demonstrates broad effectiveness on the full BEIR benchmark suite. To the best of our knowledge, this is the first demonstration that test-time computation can substantially boost the retrieval capability of frozen embedding models.
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jina-embeddings-v5-omni: Text-Geometry-Preserving Multimodal Embeddings via Frozen-Tower Composition
This work addresses the challenge of efficiently constructing a unified multimodal embedding space while preserving the performance of original text embeddings. To this end, the authors propose a frozen tower ensemble architecture that keeps both the pretrained text encoder and separate image and audio encoders fixed, aligning modalities solely through a lightweight connection module comprising only 0.35% of the total trainable parameters. Without fine-tuning any backbone models, the approach successfully maps text, images, audio, and video into a shared embedding space. It maintains strong textual embedding consistency while achieving multimodal retrieval performance comparable to that of significantly larger state-of-the-art models.
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Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Dense Verifiable Rewards
该研究通过结合压缩视觉编码器、混合专家解码器和快速推测解码头，解决了低预算GPU上高效文档解析的问题，并使用密集可验证奖励进行优化。
0 citationsRead paper
omni-macos: On-Device Omni-Modal Search on Apple Silicon
This work addresses the challenge of unifying text, code, documents, images, audio, and video in on-device multimodal search under stringent local compute and memory constraints. It presents the first end-to-end, full-modality multimodal search system deployed entirely on Apple Silicon Macs, integrating encoders, indexing, and storage while ensuring complete data locality. The system innovatively combines multimodal embeddings, quantized indexing, incremental re-encoding, fine-grained GPU acceleration, and memory-aware scheduling, all coordinated through a unified memory allocation mechanism that adheres to user-specified memory budgets. Evaluated across five Mac devices spanning an 8× range in NPU compute and a 32× range in memory capacity, the system consistently achieves efficient background indexing and real-time interactive search performance.
0 citationsRead paper
jina-reranker-v3.5: An Efficient Listwise Reranker with Hybrid Attention and Self-Distillation
This work addresses the challenge of balancing inference efficiency, cross-domain robustness, and the ability to handle semi-structured data in listwise reranking models. We propose an efficient 0.6B-parameter listwise reranker based on a last-but-not-late (LBNL) interaction architecture, which innovatively integrates sliding-window and global attention mechanisms—comprising three local and two global attention layers—and leverages multi-domain mixed training combined with a three-stage self-distillation strategy. The resulting model achieves 63.20 nDCG@10 on BEIR, matching the performance of 4B-scale models, outperforms prior approaches on MIRACL and RTEB benchmarks, and yields a 9.6-point gain in nDCG@10 on semi-structured retrieval tasks while reducing inference latency by up to 1.56×.
0 citationsRead paper
Test-Time Compute for Dense Retrieval: Agentic Program Generation with Frozen Embedding Models
This work addresses the challenge of enhancing retrieval performance of frozen small-scale embedding models without retraining. It proposes a test-time computation approach that leverages agent-based program search to automatically generate and evaluate inference programs for optimizing dense retrieval. The key insight is the discovery of a parameter-agnostic, unified algebraic formulation: interpolating the original query vector with the softmax-weighted centroid of its local top-K retrieved documents. This method consistently improves nDCG@10 across seven embedding model families spanning an order-of-magnitude difference in parameter count and demonstrates broad effectiveness on the full BEIR benchmark suite. To the best of our knowledge, this is the first demonstration that test-time computation can substantially boost the retrieval capability of frozen embedding models.
0 citationsRead paper
jina-embeddings-v5-omni: Text-Geometry-Preserving Multimodal Embeddings via Frozen-Tower Composition
This work addresses the challenge of efficiently constructing a unified multimodal embedding space while preserving the performance of original text embeddings. To this end, the authors propose a frozen tower ensemble architecture that keeps both the pretrained text encoder and separate image and audio encoders fixed, aligning modalities solely through a lightweight connection module comprising only 0.35% of the total trainable parameters. Without fine-tuning any backbone models, the approach successfully maps text, images, audio, and video into a shared embedding space. It maintains strong textual embedding consistency while achieving multimodal retrieval performance comparable to that of significantly larger state-of-the-art models.
0 citationsRead paper