BatteryMFormer is a multi-level Transformer that adds an aging-condition-aware decoder, meta degradation pattern memory, and dual-view encoder to forecast battery state-of-health trajectories from early operational data and outperforms baselines on four domains.
ClarA Vy: A Tool for Scalable and Accurate Malware Family Labeling,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
The paper releases two adversarial malware datasets (44k family-labelled, 33k type-labelled) with high evasion rates and demonstrates that 0.5% poisoning injection raises evasion from 26.1% to 92.8%.
Cybersecurity's scale, adversaries, labeling issues, and operational demands make it the superior test-case for general AI progress over NLP or computer vision.
citing papers explorer
-
BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting
BatteryMFormer is a multi-level Transformer that adds an aging-condition-aware decoder, meta degradation pattern memory, and dual-view encoder to forecast battery state-of-health trajectories from early operational data and outperforms baselines on four domains.