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Distributed & Parallel Computing
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507 papers, from 4 September 2026 to 5 November 2026: 507 arXiv preprints.
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Newest papers, page 23
Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling
Test-time scaling can improve large language model reasoning by generating and combining multiple candidate responses.Reputation as Community Memory for the Agentic Web
Agents can now externalize experience into memory, consolidating historical traces into semantic knowledge and procedural shortcuts that persist between sessions.Adaptive Bayesian Partner Selection for Federated Clinical Centers
Federated learning (FL) in healthcare faces pronounced heterogeneity and temporal concept drift across clinical centers, where evolving patient populations and care practices shift data distributions.High-Performance Tensor Formulation of the Viterbi Algorithm for Hidden Semi-Markov Models
Hidden Semi-Markov Models (HSMMs) are fundamental probabilistic models widely adopted across diverse domains, from computational biology to finance and signal processing.The Price of Random Access: Measuring Block Granularity Across Four Compressed Formats
Random access into compressed data is normally bought with density.Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation
Federated learning lets multiple participants train a shared model without pooling raw data, by exchanging locally trained model updates instead.Decomposition Buys Integrity, Not Yield
Multi-agent systems split a task across a tree of agents and justify the split with folklore: smaller contexts, cleaner separation, parallelism.Fathom: Per-Query Read Depth for Sparse Decoding over Offloaded KV Caches
When agentic sessions run to a million tokens with many sessions resident at once, the KV cache and the index that ranks it live in host memory, and the scan that ranks all n keys for a top-k step…Fast Stencil Computations on a Single Arbitrarily Moving Interval
A stencil computation repeatedly updates every cell of a grid from its neighbours' values at the previous timestep.Shared KV Caching for Replicated 27B Inference: Correctness Failures and Performance Boundaries
Shared host-memory caching can avoid repeated prefill when a request moves between inference replicas.Validating Hybrid-State Cache Recovery for GLM-5.3-Flash with vLLM and LMCache
External cache transfers can succeed while a hybrid language model resumes from an inconsistent state.When Tool Calls Succeed but Workflows Fail: Anomalies at the Agent-Tool Boundary
AI agents increasingly execute long-running workflows that externalize effects through independently supplied tools.CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization
Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap for practical LLM deployment.Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis
This paper presents a systematic examination and experimental comparison of the prominent Federated Learning (FL) frameworks FedML, Flower, Substra, and OpenFL.SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation
Robust aggregation methods for federated learning quietly rest on a fragile assumption: that whoever shows up in a given round is a fair sample of the full population.Cognitive Admission Control: Risk-Conditioned Assurance for Consequential Actions in Agentic Distributed Systems
In agentic distributed systems, an agent may be authorized to mutate external infrastructure while lacking evidence that the mutation is ready to execute.ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Execution
Scientific applications increasingly rely on high-performance computing (HPC), yet translating a scientist's high-level goal into a correct target-scale execution remains brittle and labor-intensive.Partition-Aware Scheduling for Mobile Heterogeneous Inference Co-Execution
Modern mobile inference runs on heterogeneous platforms combining mobile GPUs with multiple CPU core clusters.OpWeave: Flexible Operator Disaggregation for Heterogeneous LLM Serving
LLM serving systems increasingly disaggregate inference into finer-grained stages, with recent approaches separating attention from FFN or MoE execution during decode.Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data
In wireless federated learning (FL), data heterogeneity and multiple local updates induce client drift, degrading model convergence.