XLSCOUT Unveils ParaEmbed 2.0: a Powerful Embedding Model Tailored for Patents and IP with Expert Support from Hugging Face

New and improved embedding model

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.

New embedding models and API updates

Deploy Embedding Models with Hugging Face Inference Endpoints

Benchmarking Patent Drafting from Inventor-Style Disclosures

arXiv:2608.21249v1 Announce Type: new Abstract: While recent large language models (LLMs) have achieved promising results on individual patent drafting tasks, they fundamentally fail to investigate t...

Welcome EmbeddingGemma, Google's new efficient embedding model

Heterogeneous Dependency Graph-Guided Attentionfor Patent Representation Learning

arXiv:2605.10073v3 Announce Type: replace Abstract: Pre-trained language models advance patent classification and retrieval by encoding claims as flat token sequences, but they overlook the dependenc...

Getting Started With Embeddings

Unmasking Face Embeddings: Reading, Rendering and Naming with Foundation Models

arXiv:2609.00411v1 Announce Type: new Abstract: Modern face recognition (FR) owes much of its success to deep neural networks that learn to extract compact identity embeddings from face images. These...

Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval

Sparse Coverage is an unsupervised semantic retrieval framework designed for patent prior‑art search. It maps local span embeddings to a sparse vocabulary of embedding‑space centers chosen via a coverage‑oriented k‑center objective, allowing spans to activate nearby centers and produce sparse representations that work with inverted‑index retrieval. Experiments on CLEF‑IP 2013 demonstrate that Sparse Coverage matches or surpasses dense patent encoders in document‑level recall while remaining competitive at the passage level, making it an effective first‑stage retrieval approach for patent search.

GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval

GreenLeaf Law Embed Tiny is a 0.6 B parameter embedding model designed for legal domain retrieval. It achieves 75.11 % on the Massive Legal Embedding Benchmark and 64.38 % on MTEB(Law, v1), outperforming other models under 1 B parameters. The model is trained via a two‑stage pipeline that distills knowledge from a larger teacher, fine‑tunes with hard negative mining, and uses a curated dataset of 3.4 million query‑passage pairs, including 150,000 human‑curated samples from diverse legal jurisdictions, while supporting efficient inference with multiple quantization levels.

🪆 Introduction to Matryoshka Embedding Models

Modeling Claim Dependency Structure for Patent Litigation Prediction with Graph Attention Networks

arXiv:2608.21924v1 Announce Type: new Abstract: Patent litigation imposes substantial costs on firms and distorts R&D incentives, making early risk identification a practically important task. While...