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A new benchmark reveals that AI models can be rigorously evaluated on deep research tasks, highlighting their nuanced capabilities across diverse topics.
Existing agents excel at structured tasks but falter in feedback-driven operations and long-horizon workflows, revealing critical gaps in their capabilities for scientific instrument control.
Privilege-induced style drift can undermine reasoning model performance, but RLCSD effectively redirects the learning signal to focus on what truly matters鈥攖ask-relevant tokens.
Achieving high-fidelity audio generation with just four sampling steps, AudioX-Turbo dramatically cuts inference costs while enhancing performance across multimodal tasks.
Forget prompt engineering: E2E-REME directly generates executable Ansible playbooks from diagnosis reports, outperforming large LLMs in microservice auto-remediation accuracy and efficiency.
Audio-Omni can edit sound, music, and speech with a single model, rivaling specialized systems and unlocking capabilities like knowledge-augmented reasoning and zero-shot cross-lingual control.
