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40 results for “embedding”
Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers
Hugging Face published a technical blog post explaining how to train and fine-tune multi-vector embedding models using the Sentence Transformers library, targeting developers building retrieval-augmented or dense search systems.
Aug 26, 2026
How we built a SOTA search engine using PostgreSQL, pgvector, and Qwen3 embeddings [P]
A Hugging Face engineer describes how Papers with Code implemented a hybrid keyword-semantic search system using PostgreSQL, pgvector, and Qwen3 embeddings — improving retrieval over single-method baselines.
Aug 26, 2026
Lexical Coupling in GUI Element Grounding: Sentence Embeddings Track Labels across Mobile and Web
A new arXiv paper demonstrates that common embedding-based evaluations for GUI grounding often mistake lexical label matching for true semantic understanding, urging methodological corrections in evaluation design.
Aug 25, 2026
Enterprise AI agents are only as reliable as the messiest documents behind them
Enterprise AI adoption is hitting scalability limits because current context-engineering approaches treat knowledge as application-specific rather than as a unified, governed enterprise asset — requiring architectural shift toward shared knowledge platforms.
Aug 24, 2026
Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics
Researchers propose a new black-box LLM safety classification method using dynamical systems theory (Koopman operators) applied to prompt-response embedding dynamics, aiming to detect unsafe outputs without model access.
Aug 21, 2026
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
Hugging Face announced new multi-vector (late interaction) embedding models built with Sentence Transformers, enabling more precise semantic search by representing queries and documents as multiple vectors rather than single embeddings.
Aug 18, 2026
Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
Researchers introduced ORCA, a two-stage contrastive learning framework for anomaly detection in collider physics that improves sensitivity to new physics signals and enables interpretable attribution of anomalies to known physics processes using embedding geometry.
Aug 17, 2026
AWS Introduces Native Vector Search for DynamoDB
AWS added native vector search capabilities to DynamoDB, enabling developers to perform approximate nearest-neighbor queries on embeddings stored directly in the database without requiring a separate vector database.
Aug 16, 2026
Don't classify. Hallucinate!
A developer blog post describes a pragmatic, low-resource technique for auto-tagging legacy blog content using LLM 'hallucinated' tags followed by vector similarity matching against an existing tag corpus — solving a real-world tagging scalability problem without requiring fine-tuning or retraining.
Aug 16, 2026
llm-gemini 0.33
A developer tool plugin (llm-gemini 0.33) was released to add support for Google’s newly launched Gemini 3.7 Flash and related models, including embedding models and enhanced tooling features like reasoning traces and server-side CodeExecution.
Published Aug 13, 2026 · Analyzed Aug 16, 2026
Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis
Hugging Face announced OlmoEarth embeddings, a new feature allowing users to export custom embeddings from its OlmoEarth Studio platform for downstream analysis.
Aug 12, 2026
Position Encoding in Transformers: From Absolute and Relative Methods to Rotary Position Embeddings and Long-Context Scaling
A technical survey paper on position encoding methods in Transformers synthesizes and compares absolute, relative, and rotary embedding techniques, with emphasis on long-context scaling strategies and empirical evaluation criteria.
Aug 13, 2026
Beyond Decision Boundaries: Relational Geometry Attacks on Contrastive Embedding Manifolds
Researchers introduced a new adversarial attack framework that corrupts the relational geometry of contrastive embedding manifolds—targeting similarity structure rather than classification decisions—and demonstrated severe performance degradation on verification systems like Markmatch.
Aug 12, 2026
A Study of ASR Adaptation and Representation Dimensionality Reduction in Persian Speech Emotion Recognition Using Whisper
Researchers adapted Whisper for Persian Speech Emotion Recognition (SER) using PCA-based dimensionality reduction to cut parameters and training costs, finding it improves performance on the ShEMO dataset while ASR fine-tuning delivered only modest SER gains.
Aug 7, 2026
AI Recommendation Poisoning: How "Ask AI" Buttons Silently Alter LLM Memory
Researchers identified a novel prompt injection technique exploiting pre-filled 'Ask AI' buttons on commercial websites to silently alter LLM behavior without malware, credentials, or exploits.
Aug 6, 2026
SJEPA: Learning Elegant Latent Dynamics with Hybrid Symbolic-Neural Predictors
SJEPA is a new joint-embedding predictive architecture that integrates symbolic rules with neural corrections to learn interpretable, low-complexity latent dynamics — advancing the goal of making AI models' internal state transitions both predictive and human-understandable.
Aug 6, 2026
Google in India embeds AI into Google Pay
Google has integrated its Gemini AI assistant into Google Pay in India to provide users with conversational insights about their spending behavior.
Aug 4, 2026
Guarantees on Dynamical System Distinguishability for LLM Token Generation
A theoretical paper establishes formal guarantees for distinguishing LLM-generated text by modeling token embeddings as stochastic linear dynamical systems and proving exponential decay in misclassification probability with sequence length.
Aug 3, 2026
SDO: Structure-Aware Data Organization for Efficient LLM Post-Training
Researchers introduced SDO, a new data organization framework that dynamically adjusts mini-batch composition and sample exposure during LLM post-training to improve convergence efficiency and gradient coherence without requiring model warm-up.
Jul 31, 2026
FloDR: An invertible dimensionality reduction method based on a normalising flow
FloDR is a new invertible dimensionality reduction method that preserves unused dimensions to enable diagnostic visualizations—like conditional spread and hidden contrast—with statistical confidence testing, addressing interpretability limits of t-SNE and UMAP.
Jul 30, 2026
Hierarchical Grading in Large Language Models
Researchers propose Graded Large Language Models (GLLMs), a theoretical extension of transformer architecture using algebraic grading to improve statistical efficiency for level-stratified prediction tasks, with claims of provable risk separation and pre-certified optimization.
Jul 28, 2026
Making Open-Source Text LLM Watermarks Durable Against Merging
Researchers propose 'Merge-Adversarial Training' to make watermarks embedded in open-source LLMs resistant to model merging—a common post-training modification that previously erased such watermarks.
Jul 24, 2026
emb-diversity: A Tool for Embedding-Based Measurement of Data Diversity
A new open-source tool called emb-diversity provides standardized, embedding-based methods to measure data diversity across stylistic, semantic, language, and speaker dimensions — addressing a fragmentation in NLP evaluation practices.
Jul 23, 2026
MegazoneCloud and Korean Re Sign Strategic Agreement to Advance AX
MegazoneCloud and Korean Re signed a non-binding MOU to explore AI integration across insurance processes, positioning the collaboration as a long-term 'tech companion' partnership.
Jul 23, 2026