cs.DC · 2026-07-13 · No. 52

Distributed, Parallel, and Cluster Computing, 2026-07-13.

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

01 — The papers

7 entries
  1. 01

    New Complexity Classes in Locally Checkable Labeling for Local Computation Algorithms

    Sijin Peng

    cs.DC · cs.DS

    Local Computation Algorithms (LCAs), introduced by Rubinfeld, Tamir, Vardi, and Xie (2011), are a special type of sublinear algorithms that, given probing access to a possibly massive input, are required to provide query access to a consistent solution, without maintaining a state between different queries. In this paper, we try to understand LCA through the lens of complexity classifications, described by the following question: Given a...

    arxiv.org/abs/2607.09626 · PDF

  2. 02

    Short Graph Sketches Suffice for Error-resilient Leader Verification in CONGEST

    Pawel Garncarek, Tomasz Jurdziński, Dariusz Kowalski, Subhajit Pramanick

    cs.DC

    Locally Checkable Proofs (LCPs) enable the verification of global graph properties using locally checkable certificates assigned by a prover. Recently, this framework was extended to Locally Checkable Proofs-with-Errors (LCPE), where an adversary may corrupt some certificates. Existing LCPE algorithms, however, are designed for the LOCAL model, whose unbounded communication makes them unsuitable for direct implementation in the...

    arxiv.org/abs/2607.09522 · PDF

  3. 03

    STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU

    Victor J. B. Jung, Gagandeep Singh, Joseph Melber, Kristof Denolf, Francesco Conti, Luca Benini

    cs.DC · cs.AI · cs.PF

    The growing adoption of large language model-based agents within operating system workflows has increased the importance of energy-efficient inference on laptop-class systems-on-chip (SoCs). While cloud offloading remains common, it introduces reliability and privacy concerns that are particularly problematic for agentic workloads. Recent laptop SoCs, therefore, incorporate neural processing engines (NPUs) optimized for energy efficiency;...

    arxiv.org/abs/2607.09385 · PDF

  4. 04

    EcoKube: Simulating Carbon-Aware Scheduling Policies in Heterogeneous Edge-Cloud Environments

    Gonçalo Ferreira, Shashikant Ilager

    cs.DC

    Energy demand from cloud and edge computing is rising rapidly, with AI workloads further intensifying electricity use and associated carbon emissions. In hybrid edge-cloud settings, sustainability impact depends on time- and location-varying grid Carbon Intensity (CI), site Power Usage Effectiveness (PUE), and heterogeneous hardware characteristics. Existing carbon-aware work explores solutions such as temporal elasticity, spatio-temporal...

    arxiv.org/abs/2607.09318 · PDF

  5. 05

    Bidirectional Resource Scheduling for Disaggregated and Asynchronous RL Post-Training

    Tan Zhiqiang, Wang Maoxin, Wang Sijie, Yin Yiming, Wang Qiang, Chu Xiaowen, Shi Shaohuai

    cs.DC

    It is well established that the reasoning capabilities of large language models (LLMs) can be improved by applying reinforcement learning (RL) in a post-training stage. In a standard RL iteration, the current model (the policy) generates experience through rollouts, and the resulting data is then used to update the policy during training. High-performance RL frameworks such as StreamRL and AReaL employ a disaggregated architecture and...

    arxiv.org/abs/2607.09207 · PDF

  6. 06

    Distributed Symmetry Breaking on Hyperbolic Random Graphs

    Yannic Maus, Janosch Ruff, Sonia Simons, George Skretas

    cs.DC · math.PR

    Real-world networks like the internet share patterns like a power law degree distribution and a high clustering coefficient. Many of these properties are captured by the generative model of hyperbolic random graphs (HRGs), which provides a theoretical framework for studying such networks. Motivated by the observation that several algorithms perform better on real-world networks than their worst-case guarantees suggest, we design and analyse...

    arxiv.org/abs/2607.09170 · PDF

  7. 07

    SiFAR: Synchronization-Free All-Reduce for Low-Latency LLM Inference

    Hritvik Taneja, Anish Saxena, Abhishek Revinipati, Jae Hyung Ju, Neal C. Crago, Moinuddin Qureshi

    cs.DC

    The rise of reasoning models and agentic systems has made LLM token-generation latency a key bottleneck. Unlike chatbots, whose latency gains saturate at human reading speed, these systems generate intermediate reasoning tokens not consumed by humans. Thus, per-token latency directly determines end-to-end response time. Low-latency inference uses minimal batching, making token generation bandwidth-bound. Tensor Parallelism addresses this by...

    arxiv.org/abs/2607.08973 · PDF

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