embedding

A guide on embedding the EYG scripting language into Erlang applications, covering dependency setup, running pure programs, error handling, and adding algebraic effect support.

LSearch 是一个 Windows 上的 Spotlight 风格搜索工具，使用 EmbeddingGemma 2 (270M) 嵌入模型，并组合了两个微调的 Qwen 3.5 4B 模型（分别针对编码和 agentic 任务）构成 MoE，用于代码修复。

The paper introduces Lens, a training-free framework for multimodal representation learning that addresses semantic perspective misalignment, achieving significant performance improvements on MMEB datasets without parameter updates.

CORE introduces a distillation method that transfers compositional ranking judgments from a reranker to an embedding model using a Rank-KL objective, enhancing compositional retrieval performance across benchmarks without compromising standard tasks.

Janet is a programming language designed for system scripting, automation, and embedding in larger programs, featuring a rich core library with built-in networking and PEG parsing.

UMER introduces a unified framework for multimodal retrieval that combines embedding and ranking via pair-aware discriminative reasoning, achieving state-of-the-art performance on the MMEB-V2 benchmark.

The author argues that agent memory layers should skip LLM-based extraction for deciding what to remember, instead using simple storage, embeddings, and retrieval, exemplified by their open-source memU tool.

Presents B1ade, a minimalist RAG architecture with a 335M zero-training embedding model and a 1B SLM trained via GRPO on 723M tokens, showing emergent attribution behavior and competitive QA performance without large-scale pretraining.

Describes using ncnn's Vulkan backend for vendor-agnostic ML inference on production edge devices, achieving 10x speedup over CPU ONNX for face detection and embedding models.

This project provides self-contained, highly-portable Python distributions with minimal runtime dependencies, useful for embedding Python in larger binaries or running on diverse systems.

Agentlas is a free, open-source agent memory system that uses a local SQLite database combined with lightweight embedding and keyword search to achieve 96.4% on LongMemEval without calling an LLM, outperforming alternatives like mem0 and supermemory.

Fusion Embedding introduces a family of models that add audio to a frozen vision-language embedding backbone, enabling a unified space for text, image, video, and audio retrieval. The models train only lightweight adapters and achieve audio-image retrieval without paired audio-visual data.

QuixiAI releases embeddinggemma.c, a fast cross-platform embedding inference engine written in C, supporting multiple backends (CPU, Metal, CUDA, ROCm, SYCL) and Matryoshka embeddings with a standard HTTP API.

NVIDIA releases Nemotron 3 Embed, a collection of open embedding models that top the RTEB leaderboard, featuring an 8B flagship model and efficient 1B variants for production-scale retrieval.

Microsoft has released a new embedding model called bitnet-embedding-0.6b on HuggingFace, but the model card is missing.

NVIDIA released Nemotron-3-Embed 1B and 8B models, state-of-the-art multilingual text embedding models for retrieval and semantic similarity, optimized for RAG systems.

NVIDIA released Nemotron-3-Embed-1B-BF16, a multilingual text embedding model optimized for retrieval and semantic similarity, achieving state-of-the-art performance across 34 languages for RAG systems.

Vultr released the VultronRetriever family of models, including Prime-8B, Core-4.5B, and Flash-0.8B, which achieve top rankings on the MTEB leaderboard with high efficiency and offline capabilities.

The paper proposes a multi-cluster boundary learning method for out-of-scope intent detection using MiniLM embeddings, achieving state-of-the-art performance on CLINC150, StackOverflow, and Banking77 datasets.

InductWave proposes a wavelet-based inductive embedding method for multi-hop logical query answering on knowledge graphs, achieving competitive performance with fewer message-passing layers.