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Shifting from fixed-length tokens to semantic subwords can dramatically enhance the efficiency of attention mechanisms in generative recommenders.
SciDSK transforms how AI agents interact with scientific datasets, enabling more effective discovery and interpretation through a structured, reusable skill representation.
X2Streaming-TTS achieves true token-level synthesis with a median time to first audio token of just 15.8 ms, outperforming traditional pseudo-streaming models in quality and responsiveness.
Real-time turn-taking detection can be achieved with unprecedented accuracy and low latency using a novel dual-head modeling approach.
Forget complex model architectures for cross-domain recommendation: Taesar shows that cleverly transforming your data can unlock better performance with standard models.
CoT reasoning can hurt recommender performance by drowning out important ID signals – unless you compress reasoning chains and use bias-subtracted contrastive decoding to realign the inference subspace.
