Media
Live Music Models
Lyria Team, null, Caillon, Antoine, McWilliams, Brian, Tarakajian, Cassie, Simon, Ian, Manco, Ilaria, Engel, Jesse, Constant, Noah, Li, Yunpeng, Denk, Timo I., Lalama, Alberto, Agostinelli, Andrea, Huang, Cheng-Zhi Anna, Manilow, Ethan, Brower, George, Erdogan, Hakan, Lei, Heidi, Rolnick, Itai, Grishchenko, Ivan, Orsini, Manu, Kastelic, Matej, Zuluaga, Mauricio, Verzetti, Mauro, Dooley, Michael, Skopek, Ondrej, Ferrer, Rafael, Petridis, Savvas, Borsos, Zalán, Oord, Äaron van den, Eck, Douglas, Collins, Eli, Baldridge, Jason, Hume, Tom, Donahue, Chris, Han, Kehang, Roberts, Adam
We introduce a new class of generative models for music called live music models that produce a continuous stream of music in real-time with synchronized user control. We release Magenta RealTime, an open-weights live music model that can be steered using text or audio prompts to control acoustic style. On automatic metrics of music quality, Magenta RealTime outperforms other open-weights music generation models, despite using fewer parameters and offering first-of-its-kind live generation capabilities. We also release Lyria RealTime, an API-based model with extended controls, offering access to our most powerful model with wide prompt coverage. These models demonstrate a new paradigm for AI-assisted music creation that emphasizes human-in-the-loop interaction for live music performance.
Dense SAE Latents Are Features, Not Bugs
Sun, Xiaoqing, Stolfo, Alessandro, Engels, Joshua, Wu, Ben, Rajamanoharan, Senthooran, Sachan, Mrinmaya, Tegmark, Max
Sparse autoencoders (SAEs) are designed to extract interpretable features from language models by enforcing a sparsity constraint. Ideally, training an SAE would yield latents that are both sparse and semantically meaningful. However, many SAE latents activate frequently (i.e., are \emph{dense}), raising concerns that they may be undesirable artifacts of the training procedure. In this work, we systematically investigate the geometry, function, and origin of dense latents and show that they are not only persistent but often reflect meaningful model representations. We first demonstrate that dense latents tend to form antipodal pairs that reconstruct specific directions in the residual stream, and that ablating their subspace suppresses the emergence of new dense features in retrained SAEs -- suggesting that high density features are an intrinsic property of the residual space. We then introduce a taxonomy of dense latents, identifying classes tied to position tracking, context binding, entropy regulation, letter-specific output signals, part-of-speech, and principal component reconstruction. Finally, we analyze how these features evolve across layers, revealing a shift from structural features in early layers, to semantic features in mid layers, and finally to output-oriented signals in the last layers of the model. Our findings indicate that dense latents serve functional roles in language model computation and should not be dismissed as training noise.
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