Text Embedding Models

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6 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings
May 7, 2026Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +2Word Embeddings Text Embedding Models
Department of ICT University of Agder Grimstad, Norway · School of Engineering Newcastle University Newcastle upon Tyne, UK
Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders
May 2, 2026Ailiang Lin, Zhuoyun Li, Keyu Mao +2Text Embeddings In-Context Learning
Institute of Science Tokyo · Tencent
Kernel Affine Hull Machines as Compute-Efficient Encoders for Frozen Semantic Spaces
May 1, 2026Mohit Kumar, Somayeh Kargaran, Bernhard A. Moser +1Legal IR Kernel Methods
University of Rostock, Germany · Software Competence Center Hagenberg GmbH, Austria Hagenberg, Austria
Citation-Driven Multi-View Training for Patent Embeddings: QaECTER and Sophia-Bench
Apr 24, 2026Younes Djemmal, You Zuo, Kim Gerdes +1Legal IR Text Embedding Models
ALMAnaCH · 1AI Lab, Questel, Paris, France · 3Inria, Paris, France +3
ORPHEAS: A Cross-Lingual Greek-English Embedding Model for Retrieval-Augmented Generation
Apr 22, 2026Ioannis E. Livieris, Athanasios Koursaris, Alexandra Apostolopoulou +2Retrieval-Augmented Generation Word Embeddings
Novelcore, Athens, GR 10436 · Department of Business Administration & Organization Administration, University of Peloponnese, Kalamata„ GR 24100
Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models
Apr 20, 2026Tobias Grantner, Emanuel Sallinger, Martin FlechlMemory-Efficient Inference Text Embedding Models
Dynatrace Research · TU Wien · University of Oxford
TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications
Apr 20, 2026Pranshav Gajjar, Vijay K ShahText Embedding Evaluation Text Embedding Models
NextG Wireless Lab, North Carolina State University, Raleigh, USA
Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data
Apr 20, 2026Zhaohua Liang, Zhilin Wang, Renjie Cao +1Learning to Rank Semantic Search
OpenJobs AI · San Francisico, CA, USA
FLARE: Task-agnostic embedding model evaluation through a normalization process
Apr 19, 2026Jingzhou Jiang, Yixuan Tang, Yi Yang +1Text Embedding Evaluation Text Embedding Models
The Hong Kong University of Science and Technology
REZE: Representation Regularization for Domain-adaptive Text Embedding Pre-finetuning
Apr 19, 2026Seungmin Lee, Jeonghwan Lee, Hyunkuk Lim +2Text Embeddings Representation Learning
PwC Korea GenAI Team, Seoul, South Korea
JFinTEB: Japanese Financial Text Embedding Benchmark
Apr 17, 2026Masahiro Suzuki, Hiroki SakajiText Embedding Evaluation Text Embedding Models
Amova Asset Management Co., Ltd. · Tokyo, Japan · Hokkaido University +1
World Properties without World Models: Distributional Associations and the Interpretation of Decoding Results from Language Models
Mar 4, 2026Elan BarenholtzLLM World Models Word Embeddings
Department of Psychology & Center for Complex Systems and Brain Sciences Florida Atlantic University
Asking the Right Questions: Ontology-Grounded Interpretable Embeddings for Biomedical Text
Mar 2, 2026Yixuan Tang, Zhenghong Lin, Yandong Sun +3Text Embeddings Representation Learning
School of Computing, National University of Singapore
GRAN-TED: Generating Robust, Aligned, and Nuanced Text Embedding for Diffusion Models
Dec 17, 2025Bozhou Li, Sihan Yang, Yushuo Guan +6Text Embedding Evaluation T2I Generation
Peking University · Kling Team, Kuaishou Technology · Xi’an Jiaotong University +1
Refine Thought: A Test-Time Inference Method for Embedding Model Reasoning
Oct 14, 2025Guangzhi Wang, Kai Li, Yinghao Jiao +1Test-Time Scaling Text Embedding Models
CareerInternational Research Team
A Framework for Deductive Semantic Content Analysis at Scale in Science Education Using Text Embeddings
Aug 27, 2025Jonas Timmann Mjaaland, Markus Fleten Kreutzer, Halvor Tyseng +5Educational Technology Text Embedding Evaluation
Center for Interdisciplinary Education, University of Oslo, NO-0316 Oslo, Norway · Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853, USA · Department of Physics, California State University Fullerton, Fullerton, CA 92831, USA +1
ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings
Aug 3, 2025Ali Shiraee Kasmaee, Mohammad Khodadad, Mahdi Astaraki +4Text Embedding Evaluation Information Retrieval
Department of Computational Science and Engineering, McMaster University, Canada · BASF Canada Inc., Canada · BASF Corporation, USA
Modelling Adjectival Modification Effects on Semantic Plausibility
Jul 29, 2025Anna Golub, Beate Zywietz, Annerose EichelText Embedding Models
Institute for Natural Language Processing, University of Stuttgart, Pfaffenwaldring 5b, Stuttgart, 70569, Baden-Württemberg, Germany.
NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
Jul 13, 2025Hanwool Lee, Sara Yu, Yewon Hwang +4Multilingual Language Models Text Embeddings
FinancialNLPLab, MODULABS Shinhan Securities Seoul, Republic of Korea · FinancialNLPLab, MODULABS KT Seoul, Republic of Korea · FinancialNLPLab, MODULABS EMRO Seoul, Republic of Korea +4
Memory-Efficient FastText: A Comprehensive Approach Using Double-Array Trie Structures and Mark-Compact Memory Management
Jun 2, 2025Yimin DuText Embeddings Word Embeddings
Independent Researcher Beijing, China
FastE: Readout-Triggered Token Compression for LLM Embedding Inference
Date pendingJinsong Shu, Jinyong Wen, Baokun Wang +4Token Pruning LLM Inference Acceleration
Zhejiang University · Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security · Ant Group +1

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FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings

Ahmed K. Kadhim, Lei Jiao, Rishad Shafik, Ole-Christoffer Granmo+1

Department of ICT University of Agder Grimstad, Norway · School of Engineering Newcastle University Newcastle upon Tyne, UK

Word Embeddings Text Embedding Models

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Embedding models in natural language processing (NLP) increasingly rely on deep architectures such as BERT, while simpler models such as Word2Vec provide efficient representations but limited interpretability. The Tsetlin Machine (TM) offers an alternative logic-based learning paradigm. Omni TM Autoencoder (Omni TM-AE) applies this paradigm to static embedding by exploiting automaton state distributions within a single clause layer, but its training process remains slow. In this work, we propose FastOmniTMAE, a reformulation of Omni TM-AE that replaces sequential training dependencies with a two-stage parallel process: evaluation and update. Using a Single-Run Multi-Environment Benchmark covering classification, similarity, and clustering, FastOmniTMAE achieves up to 5

\times

faster training in classification while maintaining comparable embedding quality under both Spearman and Kendall similarity measures. To address the limited efficiency of TM training on conventional GPUs, we further implement FastOmniTMAE as a reusable accelerator on SoC-FPGA platforms. The Multi-Hardware Benchmark shows that FastOmniTMAE achieves similarity scores of 0.669 on a resource-constrained FPGA and 0.696 on an UltraScale+ SoC, demonstrating efficient logic-based embedding training with a small hardware footprint.

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