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open-models

Dharma Field Extractor: Any Document In, Structured Fields in JSON Out

The Dharma Field Extractor, a 2-billion-parameter model, specializes in extracting specific fields from diverse document types—such as invoices, IDs, and medical forms—into structured JSON format. It outperforms general-purpose frontier LLMs in accuracy and reduces cost by up to 147 times when self-hosted, demonstrating that domain-specialized small models can surpass large general-purpose models on well-defined tasks. The model supports both open extraction (detecting fields without a schema) and schema-directed extraction (returning exact field names and types), with training data spanning English, Brazilian Portuguese, and multilingual documents. Benchmarks show superior F1 scores and valid JSON output across real-world datasets like SROIE and FUNSD, reinforcing the thesis that specialization beats scale for enterprise document processing.

Hugging Face Blog
rag-methods

Faster Retrieval for Better, More Efficient RAG

Maximum inner product search (MIPS) sits at the core of recommendation systems, search, and retrieval-augmented generation (RAG). Given a query vector, MIPS systems search within a database of millions of candidate key vectors for the one that best matches the query. The original data point associated with that best-matching key is then used to handle the query: in RAG, the document that is best suited to answer the question is typically prefixed to the query. As the number of keys grows to millions and vector dimensions reach thousands, the computational cost of running an exhaustive…

Apple Machine Learning
enterprise-apps

Four layers of context engineering for production AI agents: How Deep Insight applies them in working code

In production AI agents, context management is the real bottleneck. This post presents a four-layer context engineering framework (architecture, prompts, file-based tools, and verification agents), implemented in working code from Deep Insight, an open source multi-agent data analysis solution on the Strands Agents SDK and Amazon Bedrock AgentCore.

AWS Machine Learning Blog
datasets

From Activation to Intervention: Interpreting Sparse Visual Features through Reconstruction

arXiv:2611.05522v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose visual representations into individual features, offering a promising basis for explaining model behavior. Interpreting these features typically relies on images or pixel regions that most strongly activate them. However, this evidence becomes ambiguous when multiple features with different semantics frequently activate in the same locations, such as texture and color in the same patterns. We propose Reconstructive SAE Intervention (RecSAEI), a framework that shifts feature explanation from passive observation to active visual reconstruction. By intervening on representations along SAE decoder directions and reconstructing the visual output, we provide direct evidence of a feature's semantic meaning. We demonstrate that our method successfully disambiguates feature meanings across multiple datasets and vision encoders. Furthermore, our framework grounds [CLS] SAE interventions in a sparse set of patch SAE decoder directions. Beyond stable target response, this attribution also exposes the compositional relationship between elementary visual patterns and high-level semantics. Our results advocate for intervention reconstruction as an essential complementary signal for explaining neural networks.

arXiv cs.CV
coding-engineering

Gemini Omni 1.2 Flash now available on AI Gateway

Google's Gemini Omni 1.2 Flash is now accessible via AI Gateway, enabling users to generate 3–15 second videos from text prompts using a single API key without needing a separate Google account. The model supports video generation with native audio, editing clips using up to six reference images, extending videos by 3–10 seconds per call (up to 30 seconds total), and refining outputs via previousInteractionId. Output resolutions include 360p, 720p, 1080p, and 4K in 16:9 or 9:16 aspect ratios. Videos can be retrieved via generateText or streamText in the AI SDK as MP4 files. AI Gateway offers unified API access, usage tracking, cost monitoring, retry configurations, failover, performance optimization, custom reporting, Zero Data Retention, budget controls, and routing rules, reflecting provider pricing with no markup or platform fees, including for BYOK requests.

Vercel