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Blockchain anomaly detection gets a massive boost: TEMG-TTA leverages temporal motifs and test-time adaptation to achieve a 54.88% performance leap over existing methods.
LLMs for recommendation can now surpass the limitations of static training signals, achieving sustained improvements in ranking accuracy, fairness, and diversity through a dynamically updated Bayesian distillation target.
Guaranteeing safety in autonomous systems gets a boost: this work enables formal verification of hybrid system code that directly controls physical processes.
Ditch the overly conservative error bounds: a new probabilistic approach to floating-point analysis delivers speed and precision by cleverly taming Taylor expansions.
An 80B model that runs like a 3B? Qwen3-Coder-Next shows you can get competitive coding agent performance with a fraction of the active parameters, thanks to smart training.
