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GraphForge, an evidence-graph based framework that grounds both the task and its verification in real files, and rejects fine-tuning on the SFT model's own rollouts yields further improvements on all three benchmarks, suggesting that the rubrics provide a useful selection signal.
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.
Video-DR is introduced, featuring a decoupled perception-exploration pipeline with stage-wise tool unlocking that compels exhaustive cross-frame visual grounding prior to web retrieval, enabling autonomous exploration that breaks the imitation-learning ceiling.
Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings.
These results establish source-intent preservation and shared executable-state grounding as key principles for scalable terminal-task synthesis as key principles for scalable terminal-task synthesis.
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