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QuasiMoTTo achieves up to 47% fewer samples while maintaining accuracy, challenging the conventional wisdom that independent sampling is necessary for effective parallelization.
SSA achieves superior long-context inference by leveraging gist tokens, outperforming traditional attention mechanisms without the need for complex architectural modifications.
SPIRAL achieves a remarkable 15% performance increase by combining sequential, parallel, and aggregative reasoning in language models.
Language models can now learn to forget strategically, achieving 2-3x memory efficiency without sacrificing reasoning accuracy.
