⚡ LLM Efficiency

📹 ICCV2025 · 1 paper notes

📌 Same area in other venues: 📷 CVPR2026 (8) · 🔬 ICLR2026 (171) · 💬 ACL2026 (23) · 🧪 ICML2026 (48) · 🤖 AAAI2026 (9) · 🧠 NeurIPS2025 (34)

MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation

This paper proposes MixANT, which introduces input-dependence into the forgetting gate (A matrix) of Mamba via a Mixture-of-Experts approach. A lightweight router dynamically selects context-aware A matrices to control temporal memory propagation, achieving state-of-the-art performance across all three dense action anticipation benchmarks: 50Salads, Breakfast, and Assembly101.