Notable data and AI papers

Papers627Labs45

627 papers · 45 labs

Efficient Code Embeddings from Code Generation Models research paper by Jina AI, 2025

Jina AI · Inference and efficiency · Aug 29, 2025 · 23 citations · 20 upvotes · unverified

Read on arXiv

What it shows

Jina-code-embeddings uses an autoregressive backbone pre-trained on text and code to generate embeddings for code retrieval, question-answering, and similarity identification.

By Daria Kryvosheieva, Saba Sturua, Michael Günther and 2 more · arXiv 2508.21290 · PDF

UnverifiedHugging Face's summary; not yet checked by hand.

More from Jina AI

All 7
Multimodal and roboticsUnverifiedMay 8, 2026jina-embeddings-v5-omni: Text-Geometry-Preserving Multimodal Embeddings via Frozen-Tower CompositionFrozen-encoder model composition enables efficient multimodal embedding by freezing pre-trained text models and adding specialized encoders for images and audio while maintaining text embedding consistency.Jina AI11 citations10 upvotesRead →Retrieval and dataUnverifiedFeb 17, 2026jina-embeddings-v5-text: Task-Targeted Embedding DistillationCompact text embedding models are developed through a combined training approach using distillation and contrastive loss, achieving state-of-the-art performance while supporting long-context sequences and efficient quantization.Jina AI28 citations28 upvotesRead →Multimodal and roboticsUnverifiedDec 3, 2025Jina-VLM: Small Multilingual Vision Language ModelJina-VLM, a 2.4B parameter vision-language model, achieves top performance in multilingual visual question answering using a SigLIP2 vision encoder and Qwen3 language backbone with an attention-pooling connector.Jina AI3 citations17 upvotesRead →Retrieval and dataUnverifiedSep 29, 2025jina-reranker-v3: Last but Not Late Interaction for Document RerankingA multilingual document reranker using causal self-attention achieves state-of-the-art performance with a compact architecture.Jina AI45 citations13 upvotesRead →Training and scalingUnverifiedSep 16, 2024jina-embeddings-v3: Multilingual Embeddings With Task LoRAjina-embeddings-v3, a large-scale text embedding model, achieves state-of-the-art performance in multilingual and long-context retrieval tasks using Low-Rank Adaptation and Matryoshka Representation Learning.Jina AI221 citations35 upvotesRead →Alignment and safetyUnverifiedMay 30, 2024Jina CLIP: Your CLIP Model Is Also Your Text RetrieverA novel multi-task contrastive training method improves CLIP model performance on both text-image and text-text retrieval tasks.Jina AI61 citations37 upvotesRead →

Other inference and efficiency papers

Topic
Inference and efficiencySep 12, 2023Efficient Memory Management for Large Language Model Serving with PagedAttentionvLLM manages the KV cache like pages of virtual memory, serving models with 2 to 4 times the throughput.UC Berkeley9,965 citations72 upvotesRead →Inference and efficiencyMay 27, 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-AwarenessFlashAttention computes exact attention with far fewer GPU memory reads and writes, making long sequences faster.Stanford University5,830 citations16 upvotesRead →Inference and efficiencyUnverifiedJul 24, 2025Group Sequence Policy OptimizationGroup Sequence Policy Optimization (GSPO) is a reinforcement learning algorithm that improves training efficiency and performance of large language models by using sequence-level importance ratios and operations.Alibaba (Qwen)814 citations323 upvotesRead →Inference and efficiencyUnverifiedFeb 16, 2025Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse AttentionNSA, a trainable sparse attention mechanism, enhances long-context modeling efficiency without sacrificing performance, achieving improvements in speed and accuracy over full attention models.DeepSeek596 citations157 upvotesRead →Inference and efficiencyUnverifiedApr 7, 2025SmolVLM: Redefining small and efficient multimodal modelsSmolVLM, a series of compact multimodal models, achieves high performance with minimal GPU memory usage, making efficient deployment on mobile and edge devices possible.Hugging Face330 citations212 upvotesRead →Inference and efficiencyUnverifiedOct 17, 2025Chronos-2: From Univariate to Universal ForecastingChronos-2, a pretrained model with a group attention mechanism, achieves state-of-the-art performance in zero-shot univariate, multivariate, and covariate-informed forecasting tasks.Amazon276 citations28 upvotesRead →
About this paper
Venue
arXiv.org
Citations
23, 1 influential · Semantic Scholar
Upvotes
20 · Hugging Face
Lab
Jina AI · on Companies · on Acquisitions · on Downtime · on labs · on marketplaces · on Paydays · on repos · on socials · on standards · on terms

Changes

CitationsNov 9, 2026Citations rose from 21 to 23.
CitationsNov 2, 2026A first count of 1 influential citations.
CitationsNov 2, 2026A first count of 21 citations.
New paperNov 2, 2026Found by the weekly Hugging Face scan; not yet checked by hand.