Main Takeaways Background LLM agents are seeing more and more applications in real life, from being personal assistants to helping software engineers write code and even working side by side with scientists on…
The landscape of AI agent development has evolved rapidly, with developers needing robust frameworks to build, test, and benchmark intelligent systems. MCP-Universe emerges as a comprehensive solution, providing a modular framework designed around…
Time series forecasting plays a central role in data-driven decision making. Yet, adapting forecasting models across different domains and temporal resolutions often requires custom engineering. This increases both development and maintenance costs —…
The Challenge with Flows Today Salesforce flows sit at the heart of modern CRM automation, yet authoring them still requires a unique mix of declarative drag‑and‑drop and Apex know‑how. To ease this process,…
Architecture, Training and Dataset Github Code: https://github.com/JiuhaiChen/BLIP3o Models: https://huggingface.co/BLIP3o/BLIP3o-Model Demo: https://huggingface.co/spaces/BLIP3o/blip-3o Motivation OpenAI’s GPT-4o has demonstrated state-of-the-art performance in image understanding, generation and editing tasks. Emerging hypotheses of its architecture suggest a hybrid…
xGen-small is an enterprise-ready compact LM that combines domain-focused data-curation, scalable pre-training, length-extension, instruction fine-tuning, and reinforcement-learning to deliver Enterprise AI with long-context performance at predictable, low cost.
In the rapidly evolving landscape of artificial intelligence (AI), we're witnessing jagged intelligence in the enterprise. Here's a closer look.
Time series forecasting is becoming increasingly important across various domains, thus having high-quality, diverse benchmarks are crucial for fair evaluation across model families.
We propose Moirai-MoE, the first mixture-of-experts time series foundation model, achieving token-level model specialization in a data-driven manner.
The SFR-Embedding-Mistral marks a significant advancement in text-embedding models, building upon the solid foundations of E5-mistral-7b-instruct and Mistral-7B-v0.1.
