Brief

Researchers are developing multimodal large language models (MLLMs) that can process and integrate information from various data types, including text, audio, and video. One approach, MM-When2Speak, focuses on improving conversational timing by predicting when a brief reaction or a full response is appropriate, showing a threefold improvement in performance. Other research explores training MLLMs using only pairwise modalities to reduce data curation effort and addresses fine-grained visual understanding challenges through self-distillation techniques. These advancements aim to create more natural, engaging, and capable AI systems that can better perceive and interact with the real world. AI

Researchers are exploring how large language models (LLMs) align with human brain activity across different languages and tasks. Studies show that intermediate LLM layers best predict brain responses, and this alignment is influenced by training data language dominance rather than inherent model typology. Furthermore, instruction-tuned multimodal LLMs demonstrate stronger brain alignment, particularly when organized around task-specific demands rather than just surface semantics. AI