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Intern-S2-Preview-397B not only excels in multimodal scientific reasoning but also enhances biological instruction performance without altering its foundational architecture.
MDB-Link boosts Text-to-SQL accuracy by over 30 points while streamlining the schema selection process in multi-database scenarios.
SkillMemo transforms robotic manipulation by enabling models to leverage reusable skill structures, leading to unprecedented compositional generalization in complex tasks.
A unified evaluation framework that simplifies the assessment of LLM-based agents could drastically enhance reproducibility and accelerate research breakthroughs.
Action-only decoding in GigaWorld-Policy-0.5 slashes inference latency to 85 ms, revolutionizing real-time robot control efficiency.
Evaluator quality for robotic policies hinges more on long-horizon consistency than on short-term visual fidelity, reshaping our approach to world model design.
Structured allocentric representations can boost spatial reasoning in foundation models by up to 18%, even in the absence of visual inputs.
Decomposing GUI agent trajectories into verifiable milestones and auditing the evidence chain yields a 10% boost in RL training performance, outperforming single-judge reward systems.
Strategic recovery from failures is key to deploying robots for complex assembly tasks in the real world.
Humanoid robots can now reliably transport objects on a tray in the real world, thanks to a hierarchical RL approach that isolates and cancels gait-induced disturbances.
