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Under a 30% total power cut, allocating power dynamically along each task's power-performance curve recovered an average of about 1,500 tokens/s/task, or 63% of the gap between even allocation and the theoretical optimum.

Previous throttled scheduling mostly used even allocation, leaving this gap because it did not exploit differences in tasks' power-performance curves.

The result comes from 131 H200 training runs, 24 validation runs and 34 matched H100 tasks, self-reported by the preprint's authors.

Experiments used at most 32 GPUs, concentrated on H100/H200; the preprint was submitted on September 10, has not been peer-reviewed, and has no third-party replication; they do not extrapolate to 10,000-GPU clusters, inference workloads or grid benefits.

Even when citations are real and their conclusions point the right way, more than 70% of positive evidence still fails to logically support a paper's claim of novelty. NovGauge tested novelty judgments across 18 models using 619 paper pairs and 50 multi-paper sets; the authors report hallucination rates between 0% and 39%, with best Verified F1 around 43% to 72%.

Previously, novelty judgments relied on whether citations were real and whether their conclusions pointed the right way, but that standard misses a large share of positive evidence that does not hold up logically.

The measurement is author-reported: 619 paper pairs, 50 multi-paper sets, 18 models, hallucination rates 0% to 39%, best Verified F1 around 43% to 72%.

This is an author-built preprint benchmark that has not yet been independently reproduced; it is suited to testing whether citations support conclusions, and cannot be extrapolated to the accuracy of all AI literature reviews.