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Atria Dawn Preview is introduced, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world.
FACET achieves unprecedented task synthesis quality by preserving source intent and ensuring executable state consistency, leading to more reliable terminal agents.
Intern-S2-Preview-397B not only excels in multimodal scientific reasoning but also enhances biological instruction performance without altering its foundational architecture.
Video-DeepResearch shatters the imitation-learning ceiling, achieving a 64% accuracy in complex video-based question answering, far surpassing existing models.
Unlock long-context reasoning in LLMs by turning agent trajectories into gold-standard QA pairs, outperforming models 8x larger on challenging reasoning tasks.
Even the best LLMs struggle to effectively discover, refine, and reuse skills over a lifetime of experience, suggesting current benchmarks significantly overestimate real-world agentic capabilities.
Current LLM efficiency metrics fail to capture the true cost of tool use, as measured by wall-clock latency, but a new hardware-aware metric closes the gap.
