In 2025, AI music went from fringe experiment to mainstream reality, charting tracks, sparking debate, and challenging how we define creativity. The future of music is here, but it’s complicated. Check out my post on the state of AI music around the globe to find out why🎵🤔!
https://t.co/oZ0NlsMW70
The compression researchers and the LM researchers were converging on the same thing from opposite directions.
Sound is just another sequence now. That wasn't the plan. That was the side effect.
Paper: https://t.co/3nmroo2IZo
Qwen3-TTS is what happens when someone takes that seriously.
No separate speech engine. The LM predicts codec tokens. Same as it predicts words. Flow matching decodes them back to audio.
VoiceCraft beats SOTA speech editing without redesigning the architecture.
Standard AR models only see the past — if you're editing a word mid-sentence, the model generates blind with no idea what comes after.
Their fix: don't change the model. Change the sequence.
Mask the edit region → move future context before the generation point → model now sees both sides.
Same causal LM. Same training setup. Bidirectional context for free.
Next time you're stuck trying to redesign a model — ask if you can solve it with sequence ordering instead. The architecture is rarely the bottleneck.
we also tried training a World Model on the 90 trajectories we had — obviously very overfit. it didn’t finish training until a few hours after the hackathon ended, but cool learning experience.
shoutout @PrimeIntellect for the credits!
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