Author

Jinfan Lv

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Jul 2026

QuantWAMs: Calibrating at the Right Granularity for World Action Models

QuantWAMs is presented, a PTQ framework that aligns quantization decisions with the calibration context defined by model structure, rollout distribution, and task objective, and co-training-objective saliency, which computes empirical-Fisher scores from the joint video--action gradient and assigns weight precision at a...

Jia-Chen Zhou, Jinfan Lv, Ruixuan Li et al. · arXiv.org · 0 citations

Preprint Aug 2026

AWARe: Mitigating Catastrophic Forgetting via Activation-Weighted Adaptive REtention

Activation-Weighted Adaptive REtention (AWARe) is a fine-tuning method that mitigates catastrophic forgetting by dynamically controlling parameter updates based on activation patterns, and operates without modifying model architectures, ensuring compatibility with existing inference engines.

Jun-Yong Liao, Jinfan Lv, Guoming Wang et al. · 1 citation