9 resultados para "embeddings"
Bancos de Dados Vetoriais: Onde os Embeddings Vivem
Dados & EmbeddingsO que é um banco de dados vetorial e por que a busca por significado precisa de um: similaridade, busca aproximada (ANN), índices e quando você realmente precisa de um.
O Que São Embeddings, na Prática
Dados & EmbeddingsEmbeddings explicados sem fórmulas: vetores num mapa de significados, como são gerados, o que é distância de cosseno e onde eles são usados.
RAG do Zero: Busca + Geração
Dados & EmbeddingsRAG explicado passo a passo: indexar documentos com chunking e embeddings, recuperar trechos relevantes, montar o prompt e gerar a resposta.
Embeddings & RAG: A Memória das IAs
Dados & EmbeddingsEntenda embeddings, busca semântica, bancos vetoriais e RAG — como dar memória e fontes a um modelo de linguagem, e por que tudo depende dos dados.
Tokenizer-Agnostic Engram Module
LLMs & TextoarXiv:2607.29065v1 Announce Type: new Abstract: Deepseek's Engram, a conditional memory module, was introduced to trade-off storage versus reasoning in large language models. However, the module relies on token-level $N$-gram hashing for Engram embedding lookup, introducing a tight coupling to the tokenizer used: a model with a different tokenizer would have to train its own Engram embeddings from scratch. To improve the reusability of Engram embeddings, we propose a change to the hashing routin...
DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models
Geração de ImagemarXiv:2607.28936v1 Announce Type: new Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space. However, the decision boundaries of deep face recognition (FR) systems are often sufficiently narrow that they can be conflated, rendering the models vulnerable to adversarial attacks. In such scenarios, the FR system fails to distinguish between an authentic source and a meticulously crafted adversarial face. Existing adversa...
ReLoop-UME: Recurrent Depth with Learnable Retrieval Registers for Universal Multimodal Embedding
Dados & EmbeddingsarXiv:2607.28751v1 Announce Type: new Abstract: Universal multimodal embedding (UME) maps heterogeneous multimodal inputs into a shared embedding space. Existing UME models either form embeddings through single forward encoding or add computation through explicit rationale tokens and latent autoregressive states. Although token expansion can improve complex matching, serial generation increases retrieval latency and makes the final embedding depend on generated intermediate states. This raises a...
Guarantees on Dynamical System Distinguishability for LLM Token Generation
LLMs & TextoarXiv:2607.28667v1 Announce Type: new Abstract: Recent work has shown that classifying large language models (LLMs)' responses can be distinguished by modeling token embeddings as trajectories of a black-box dynamical system (DS) and comparing prediction residuals of two DSs. Despite the empirical success of this dynamical approach, a theoretical understanding of why it works, how well it scales as a function of the token sequence, and when it transfers across embedding models remains lacking. W...
Dados & Embeddings
Dados & EmbeddingsEmbeddings, busca semântica, RAG e os datasets que treinam tudo.