MAdam removes Adam's weighting and geometric mismatches in multi-objective optimization via a preference-conditioned curvature preconditioner, consistently improving results across tasks.
EchoPrune treats redundant video tokens as temporal echoes and prunes them via query relevance and reconstruction error, letting VideoLLMs process up to 20x more frames for +8.6% accuracy and 5.6x faster prefilling.
mRNABench benchmarks mature mRNA property predictions across 59 tasks and 135K experiments, revealing synergies between self-supervised objectives that yield a compact state-of-the-art Mamba model using 700x fewer parameters.