Retrieval-augmented generation

Tracking the Behavioral Trajectories of Adapting Agents

Text files such as skill files, memory files, and behavioral configuration files play a central role in defining how modern agents act. Through edits by humans or the agents themselves, these files may evolve over time, directly steering the agent's behavior in future interactions.

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and assortative edges in disassortative graphs. This structural inversion creates structure-feature mismatches that disrupt neighborhood aggregation across different graph types.

Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs

Structure-guided NER optimization for enterprise GraphRAG systems The post Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs appeared first on Towards Data Science .

Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval

Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does.

Baseline Enterprise RAG, From PDF to Highlighted Answer

Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted.

RAG Is Burning Money — I Built a Cost Control Layer to Fix It

Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality.

Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling

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Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

Multimodal Embedding & Reranker Models with Sentence Transformers

Build a Domain-Specific Embedding Model in Under a Day

Identifying Interactions at Scale for LLMs

--> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI.

OpenAI takes an ownership stake in Thrive Holdings to accelerate enterprise AI adoption

OpenAI takes an ownership stake in Thrive Holdings to accelerate enterprise AI adoption, embedding frontier research and engineering directly into accounting and IT services to boost speed, accuracy, and efficiency while creating a scalable model for industry-wide transformation.

Welcome EmbeddingGemma, Google's new efficient embedding model

What exactly does word2vec learn?

What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task.

Scaling domain expertise in complex, regulated domains

Discover how Blue J is transforming tax research with AI-powered tools built on GPT-4. 1.

Parquet Content-Defined Chunking

Training and Finetuning Sparse Embedding Models with Sentence Transformers

Evaluating RAG with LLM as a Judge

Train 400x faster Static Embedding Models with Sentence Transformers