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Action with Visual Primitives

This work introduces AVP (Action with Visual Primitives), an end-to-end architecture that implements this visual-primitive-centric interface: the VLM infers the next-stage target and emits visual-primitive tokens that condition a flow-matching action expert, with supervision derived from end-effector kinematics.

Weilong Guo, Yucheng Wang, Ren-Ping Zhou et al. · 1 citation
#artificial intelligence Preprint Dec 2025

KV Admission: Learning What to Write for Efficient Long-Context LLM Inference

This paper formalizes KV management as a causal system of three primitives: KV Admission, Selection, and Eviction, and instantiate KV Admission via Write-Gated KV (WG-KV), a lightweight mechanism that learns to predict token utility before cache entry.

Yen-Chieh Huang, Rui Fang, Ming-Syan Chen et al. · 2 citations

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