MRAF framework uses missing-token prompting and reliability-aware cross-attention fusion to achieve 100% accuracy on some POLY-SIM 2026 tasks and competitive results on missing-face cases.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Traits Run Deeper proposes MFR, TSMF asymmetric fusion, and DCPR modules to improve multimodal personality assessment, claiming 25% MSE reduction and first place on AVI Challenge 2026.
citing papers explorer
-
Missing-Token Prompted Reliability-Aware Fusion for Robust Polyglot Speaker Identification
MRAF framework uses missing-token prompting and reliability-aware cross-attention fusion to achieve 100% accuracy on some POLY-SIM 2026 tasks and competitive results on missing-face cases.
-
Traits Run Deeper: Trait-Specific Asymmetric Fusion for Personality Assessment
Traits Run Deeper proposes MFR, TSMF asymmetric fusion, and DCPR modules to improve multimodal personality assessment, claiming 25% MSE reduction and first place on AVI Challenge 2026.