LLM News

A new training approach using knowledge graphs as implicit reward models enables a 14-billion-parameter model to outperform much larger systems like GPT-5.2 and Gemini 3 Pro on complex multi-hop reasoning tasks. Researchers combined supervised fine-tuning and reinforcement learning with knowledge graph path signals to ground models in verifiable domain facts.

Researchers have published MedXIAOHE, a medical multimodal foundation model designed for clinical applications. According to the authors, the model achieves state-of-the-art performance across diverse medical benchmarks and surpasses several closed-source multimodal systems on multiple capabilities.