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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.
General-purpose self-supervised audio representations can outperform specialized supervised models, reshaping the landscape of audio understanding in ALLMs.
Foley-Omni achieves expert-level performance in audio synthesis while generating cohesive soundtracks for video, enhancing both intelligibility and quality.
Unlock SOTA audio understanding by jointly training on readily available clip-level descriptions and scarce frame-level annotations, bridging the gap between global semantics and local details.
Foundation models trained on audio, general time series, and brain signals can be distilled into a single, powerful encoder for scientific time series, unlocking performance gains on par with task-specific training.
