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This paper introduces RecursiveMAS, a framework that extends recursive scaling principles to multi-agent systems for improved collaborative reasoning efficiency and accuracy. It demonstrates significant speedups and token reduction across various benchmarks compared to standard baselines.
LACE introduces a lattice attention mechanism that enables concurrent reasoning paths in LLMs to share intermediate insights and correct errors during inference, improving reasoning accuracy by over 7 points compared to standard isolated parallel sampling.