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Lu-Jia Yin

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Conference Open access Aug 2026

Hybrid-Adaptive Thread Tuning to Mitigate Simulation Execution Bottlenecks in High-Performance Reinforcement Learning Inference

In simulation-in-the-loop decision-making systems, reinforcement learning (RL) inference is often constrained by simulator-side execution overhead, where workloads are highly dynamic and sensitive to runtime thread configurations. Existing multithreaded strategies struggle to match thread resources before or during exe...

Ji-Ming Su, Han-Tao Hua, Lu-Jia Yin et al. · 0 citations
Conference Aug 2026

An ART-Bev-Based Cognitive-Behavioral Framework for Personality-Driven Simulated Agents

Modern multi-agent collaborative simulations demand more realistic, adaptive, and interpretable computer-generated agents (CGA). Existing models often exhibit rigid behaviors and weak individual differentiation. To address this, we propose a personality-driven cognitive framework that integrates the Big Five model, a t...

Xing-Xuan Chen, Zhaowei Liang, Lu-Jia Yin et al. · 0 citations
Conference Jul 2026

ParaCascade: A Parallel Cascading Framework Supporting Early Routing

Real-world inference tasks for large language models exhibit diverse difficulty levels. Existing LLM serving systems integrate models of different sizes and attempt to route tasks of appropriate difficulty to the most suitable model, aiming to reduce resource waste while guaranteeing service quality. Such systems usual...

Hao Wei, Lu-Jia Yin, Chen Chen et al. · 0 citations
Conference Jul 2026

AdaRAG: Budget-Aware Adaptive Retrieval-Augmented Generation via Hierarchical Reinforcement Learning

Multi-turn retrieval-augmented generation (RAG) improves question answering by decomposing evidence seeking into iterative retrieval and reasoning steps. Existing multi-turn RAG methods usually optimize when and how to retrieve while fixing the number of retrieved documents per step. However, we discovered that this fi...

Jia-Nan Sun, Miao Zhang, Chen Chen et al. · 0 citations
Conference Jul 2026

AMSche: Affinity-Aware Microservice Scheduling for Communication-Intensive Tasks

As computing resources in cloud environments become increasingly abundant, executing complex scientific workflows on large-scale cloud infrastructure has become a standard practice. However, communication-intensive workflows face two fundamental bottlenecks. First, the lack of physical topology awareness often forces h...

Hao Wei, Hai-Liang Chen, Jia-Nan Sun et al. · 0 citations

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