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Chaoyue Niu

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Preprint Aug 2026

ECHO: Dyadic 3D Facial Motion Generation with Asymmetric Deterministic Articulation and Stochastic Reaction

We propose ECHO for dyadic 3D facial motion generation under a strict dual-stream audio-only setting, formulating the problem as an asymmetric task involving speech-constrained articulation and one-to-many listener reactions. To address this asymmetry, ECHO decomposes motion into a deterministic anchor that captures st...

Zhuo-Qiang Cai, Yu-Jie Sun, Chao-Yue Niu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Dynamic Flow, Static Graph: KV Cache Reuse for Efficient LLM Serving on Mobile NPUs

On-device large language model (LLM) serving is a cornerstone of local-first personal intelligence, offering users data sovereignty, strong privacy guarantees, and freedom from cloud API latency and cost. Although KV caching is widely used to reduce latency in long-context inference, existing designs were primarily opt...

Zheng-Xiang Huang, Sheng-Heng Chen, Chao-Yue Niu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

TROVE: Adaptive Agent Skill Orchestration via Trace-Grounded Route Validation and Editing

Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observed. However, such pre-execution commitment creates an orchestration bottleneck: when intermediate evidence invalidates the pending continuation, agents must either execute stale steps or replan broadly, compoun...

Tian-Xing Wang, Ming-Ming Zhao, Shuai Huang et al. · 1 citation
Book Open access Jul 2026

C2KV: Compressed and Composable KV Cache Reuse for Efficient LLM Inference

C2KV is proposed, a unified framework for non-prefix KV reuse that jointly optimizes KV cache compression and concatenation that significantly reduces KV cache storage and transfer costs.

Chuheng Du, Jun-Yi Chen, Hanlin Tang et al. · 2 citations
Preprint Aug 2026

Reflection with Action-Induced Visual Differences for Desktop GUI Agents

Evidence-First Reflection (EFR), a two-stage reflector that explicitly decouples action-induced visual differences extraction from outcome verification, makes reflection better grounded in screen transitions, while reducing both visual search complexity and reasoning burden.

Yijie Ma, Chao-Yue Niu, Fan Wu et al. · 0 citations

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