cs.DC · 2026-06-23 · No. 32
Distributed, Parallel, and Cluster Computing, 2026-06-23.
10 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
10 entries-
01
Kamera: Unified Position-Invariant Multimodal KV Cache for Training-Free Reuse
Bole Ma, Jan Eitzinger, Harald Koestler, Gerhard Wellein
cs.DC · cs.AI · cs.CV
Multimodal agents repeatedly re-examine the same video frames, UI screenshots, and rendered artifacts as their context window slides and reasoning iterates, yet every look-back re-encodes from scratch, because prefix caches serve reuse only at a fixed leading position. We show this recompute is avoidable, and identify exactly what naive KV reuse loses: the cross-chunk conditioning a chunk absorbs from its neighbours. This loss is asymmetric....
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02
Concordia: JIT-Compiled Persistent-Kernel Checkpointing for Fault-Tolerant LLM Inference
Yuhang Gan, Yiwei Yang, Yuyi Li, Xiangyu Gao, Yichen Wang, Rain Jiang, Xiaoning Ding, Andi Quinn, Chen Qian
cs.DC · cs.LG
Long-running LLM agents keep valuable state resident on GPUs: KV caches, request schedulers, communication state, and sometimes online adapters. Losing this state after a GPU or communicator failure can discard minutes to hours of work, yet existing recovery mechanisms either restart the whole serving stack or require application-specific checkpoint logic inside every attention and runtime component. This paper argues that fault tolerance for...
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03
Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences
Ashkan Pirmani, Ilse Vermeulen, Goran Vinterhalter, Lotte Geys, Axel Faes, Muhammad Quamber Ali, Nishkala...
cs.DC · cs.LG
Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research under strict privacy regulation. The methods are maturing fast, but the practical barrier now comes earlier: a team starting a federated project meets a scattered mix of frameworks, governance obligations, and unfamiliar roles, with no structured place to begin that fits its own background....
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04
When Staking Rewards Compound: Measuring the Impact of Ethereum's Pectra Upgrade
Mohammed Benseddik, Benjamin Kraner, Claudio J. Tessone
cs.DC · q-fin.CP · q-fin.ST
Ethereum's beacon chain hosts over 920,000 active validators, a number inflated by the legacy 32 ETH stake cap. The Pectra upgrade (May 2025) addresses this by introducing 0x02 compounding validators, raising the maximum stake per validator from 32 to 2,048 ETH and enabling automatic reward reinvestment. This paper examines how compounding affects consensus-layer rewards, whether higher balances provide execution-layer advantages, and whether...
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05
Node-Level Performance and Energy Characterization of Flagship Science Applications on SuperMUC-NG Phase 2
Salvatore Cielo, Elmira Birang, Alexander Pöppl, Sajad Azizi, Plamen Dobrev, Margarita Egelhofer, Ivan Pribec, Gerald Mathias
cs.DC · cs.PF
We present a systematic performance and energy-efficiency characterization of five flagship scientific workloads on SuperMUC-NG phase 2, the 28 PetaFLOPs system at the Leibniz Supercomputing Center (LRZ) equipped with Intel Xeon Platinum 8480+ and Intel Data Center GPU Max 1550 (Ponte Vecchio, PVC) accelerators. The selected codes span molecular dynamics (gromacs, lammps), astrophysics and cosmology (OpenGadget3, AthenaK), and finite-element...
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06
Solving Approximate Agreement on continuous and discrete spaces
Augustin Albert, Sergio Rajsbaum
cs.DC
We consider $n$ asynchronous processes prone to crashes, communicating via shared read-write registers, and study the wait-free solvability of approximate agreement: given inputs, processes must output values that are close to each other, and satisfy a validity property. At the very least, if inputs are identical, all outputs must equal that input. The problem has been studied for various input spaces: continuous, discrete, one-dimensional or...
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07
Nautilus: A Verifiable Hierarchical Federated Learning Framework for Vehicular-Edge-Cloud Systems
Linyang Wu, Linpeng Jia, Hanwen Zhang, Tiantian Duan, Yi Sun
cs.DC
Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but the extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. While dynamic scheduling strategies can mitigate this issue, they introduce new trust concerns: how to verify that scheduling decisions are fair, and whether clients faithfully execute optimization instructions without disclosing...
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08
LiveServe: Interaction-Aware Serving for Real-Time Omni-Modal LLMs
Xiangyu Zhi, Peiqi Yin, Sheng Guan, Chenguang Zheng, James Cheng, Xiao Yan
cs.DC
Realtime omni-modal LMs support speech-centric conversations where users stream inputs, hear generated audio, and interrupt freely. Existing Omni-LM serving systems still rely on throughput-oriented LLM scheduling and LRU KV offloading. These policies ignore audio playback and multi-turn reuse: they may generate tokens far beyond what users hear, wasting work after barge-in, and evict KV state needed in the next turn. LiveServe is an...
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09
Decentralized Operations of Decarbonized Chemical Plants with Renewable-driven Transmission Systems
Richard Reed, kazi Arman Ahmed, Saba Ghasemi, Zheyu Jiang, Paritosh Ramanan
cs.DC
Electrification of ethane cracking offers a promising pathway to industrial decarbonization, provided that the electricity is sourced from renewable energy. However, integrating electrified chemical plant microgrids with a decarbonized power grid requires joint operations planning between Independent System Operators and chemical plants, which is hindered by the highly confidential nature of plant operational data. In this paper, we propose a...
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10
EchoFlow: A Workload-Aware Parameter Tuning Method for Blockchain Systems
Ben Lian, Linpeng Jia, Xing Chen, Xiaofeng Chen, Yi Sun
cs.DC
Blockchain systems expose a large number of tunable parameters that significantly influence system performance. However, in practice, a single parameter configuration is often applied across different workloads, leaving substantial unexploited performance potential. To address this, we propose EchoFlow, a blockchain parameter tuning framework that adaptively adjusts parameter configurations based on workload characteristics, enabling...
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