together-ai-67cbe82a·3 events·first seen Aliases: Together AI, Together.AI
Researchers at Stanford University and Together AI tested six LLMs equipped with web-search tools on daily news questions across six languages, finding that retrieval failures account for the majority of errors (38.8%) rather than reasoning or comprehension failures. Top models exceeded 90% accuracy on well-formed English multiple-choice questions, but performance degraded significantly for Hindi, free-response formats, and questions containing false premises. The study identifies three retrieval improvement levers—indexing coverage, source ranking, and multilingual query handling—and suggests retrieval optimization may yield larger gains than model scaling for time-sensitive queries.
A multi-item roundup covers several AI developments: Nvidia unveiled NemoClaw at GTC 2026, an enterprise software stack integrating with OpenClaw to add security and governance for agentic deployments, with launch partners including Salesforce, Cisco, and CrowdStrike. OpenAI released GPT-5.4 mini and nano, smaller variants optimized for speed with benchmark results on SWE-Bench Pro and OSWorld-Verified, priced at $0.75 and $0.20 per million input tokens respectively. Nvidia also released Nemotron 3 Nano 4B, a hybrid Mamba-Transformer 4B parameter on-device model. Additional items cover Midjourney V8 alpha (5x faster, diffusion-only) and Mamba-3, a 1.5B state space model from CMU and Together.AI with improved accuracy over Mamba-2.
Together AI has announced an integration with Hugging Face that enables fine-tuning of any model from the Hugging Face Hub directly through Together AI's platform. This partnership expands access to fine-tuning infrastructure for open-weight models without requiring users to manage their own compute. The integration targets developers and enterprises seeking managed fine-tuning workflows for a broad range of open-source LLMs.