cs.DC · 2026-07-02 · No. 41

Distributed, Parallel, and Cluster Computing, 2026-07-02.

5 new papers in cs.DC. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

5 entries
  1. 01

    Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents

    Ran Yan, Wei Fu, Jiale Li, Shusheng Xu, Zhiyu Mei, Jiaxuan Gao, Jiarui Zhang, Xujie Shen, Hao Dai, Chuyi He, Zhen...

    cs.DC

    LLM agents are rapidly being deployed in production, including coding assistants, customer-support chatbots, and scientific research assistants, yet they remain fundamentally static in enterprise deployment. The LLM weights, system prompts, tool repertoires, and in-context harnesses are frozen at deployment time, and any improvement requires a manual loop of human-curated data collection, offline fine-tuning, modification of the agentic...

    arxiv.org/abs/2607.01120 · PDF

  2. 02

    Modeling and Chasing the Energy-Efficiency Sweet Spots in Modern GPUs

    Ayesha Afzal, Markus Manfred Li, Michael Panzlaff

    cs.DC

    Energy consumption is a key limitation in high-performance computing on heterogeneous CPU-GPU systems. This work studies how hardware configuration affects energy-to-solution under realistic workloads. We study energy efficiency regimes using molecular dynamics benchmarks (GROMACS and AMBER) and a stress-test benchmark (FIRESTARTER) on systems with A40, A100, H100, and H200 GPUs and Intel Ice Lake CPU, varying frequency scaling and power cap....

    arxiv.org/abs/2607.00819 · PDF

  3. 03

    ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving

    Sangjin Choi, Sukmin Cho, Yifan Xiong, Ziyue Yang, Youngjin Kwon, Peng Cheng

    cs.DC

    In prefill-decode (PD) disaggregated LLM serving, each request is assigned to a decode worker after prefill. Existing decode routers balance only load; for mixture-of-experts (MoE) models this is incomplete: equally loaded workers can differ in latency, since each decode step loads the weights of every distinct expert its batch activates. We present ELDR, an expert-locality-aware decode router for PD-disaggregated MoE serving. From a...

    arxiv.org/abs/2607.00466 · PDF

  4. 04

    CloudyGUI: A Novel Python-based Framework for Auto-Scaling and Cloud Workload Analysis

    Jyoti Bawa, Mohit Kaushik, Kuljit Kaur Chahal, Kamaljit Kaur

    cs.DC

    Purpose: Cloud computing environments are highly dynamic, creating major challenges for resource management. Accurate workload prediction is therefore essential for effective auto-scaling. To address this, we present CloudyGUI, a Python simulation framework with an easy-to-use GUI that allows researchers to test and validate resource management strategies. Methods: This framework employs a three-stage pipeline: workload generation, prediction...

    arxiv.org/abs/2607.00455 · PDF

  5. 05

    Promise-Future Synchronization for Cluster Asynchronous Many-Task Runtimes via MPI One-Sided Communication

    Mia Reitz

    cs.DC

    Asynchronous Many-Task (AMT) runtimes use futures as placeholders for values produced by other tasks. In the ItoyoriFBC AMT runtime, the existing future-only model binds each future to its producer at creation time and requires the number of tasks that read each future to be fixed at compile time. This prevents directly expressing algorithms that create dependencies dynamically. We extend ItoyoriFBC with an implementation of a promise-future...

    arxiv.org/abs/2607.00303 · PDF

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