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Achieving similar performance to larger models with significantly less data and faster inference speeds could redefine efficiency benchmarks in foundation models.
Intern-S2-Preview-397B not only excels in multimodal scientific reasoning but also enhances biological instruction performance without altering its foundational architecture.
Task-specific optimization in multimodal models can significantly boost performance by dynamically reallocating shared computation resources between vision and language components.
Current MLLMs still struggle to connect the dots between images and text when they're interleaved, highlighting a critical gap in real-world multimodal understanding.
Open-source multimodal models just leveled up: InternVL3 rivals closed-source titans like GPT-4o by pre-training vision and language together from the start.
