MAGI-1 is a 24B-parameter autoregressive video world model that predicts denoised frame chunks sequentially with increasing noise to enable causal, scalable, streaming generation up to 4M token contexts.
Cophy: A scalable, portable, and interactive index advisor for large workloads
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Index tuning, i.e., selecting the indexes appropriate for a workload, is a crucial problem in database system tuning. In this paper, we solve index tuning for large problem instances that are common in practice, e.g., thousands of queries in the workload, thousands of candidate indexes and several hard and soft constraints. Our work is the first to reveal that the index tuning problem has a well structured space of solutions, and this space can be explored efficiently with well known techniques from linear optimization. Experimental results demonstrate that our approach outperforms state-of-the-art commercial and research techniques by a significant margin (up to an order of magnitude).
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AIM is an automated index recommendation engine for SQL databases that has been validated on thousands of production instances with fast convergence, no-regression guarantees, and metrics-driven explanations.
citing papers explorer
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MAGI-1: Autoregressive Video Generation at Scale
MAGI-1 is a 24B-parameter autoregressive video world model that predicts denoised frame chunks sequentially with increasing noise to enable causal, scalable, streaming generation up to 4M token contexts.
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AIM: A practical approach to automated index management for SQL databases
AIM is an automated index recommendation engine for SQL databases that has been validated on thousands of production instances with fast convergence, no-regression guarantees, and metrics-driven explanations.