VideoThinker improves lightweight MLLM video reasoning by creating a bias model to capture shortcuts and applying causal debiasing policy optimization to push away from them, achieving SOTA efficiency with minimal data.
[bayesian analysis in expert systems]: Com- ment: Graphical models, causality and intervention.Statisti- cal Science, 8(3):266–269
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Beyond Perceptual Shortcuts: Causal-Inspired Debiasing Optimization for Generalizable Video Reasoning in Lightweight MLLMs
VideoThinker improves lightweight MLLM video reasoning by creating a bias model to capture shortcuts and applying causal debiasing policy optimization to push away from them, achieving SOTA efficiency with minimal data.