Systems & Networks · Computer Science & AI
Distributed & Parallel Computing
The newest papers in Distributed & Parallel Computing that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
507 papers, from 4 September 2026 to 5 November 2026: 507 arXiv preprints.
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TLAPS-Bench: Formally Proving Correctness of Real-World System Specifications with TLA+ Proof System
TLAPS-Bench is a benchmark and execution harness for constructing machine-checked proofs for real-world system specifications in TLA+ with agentic AI.Towards a Trustworthy and Governable Internet of Agentics: An Architectural Perspective on Protocols, Systems, and Services
Large language model applications are increasingly being organized as tool-using and multi-agent workflows rather than as isolated chat interfaces.DynSL: Resource-Adaptive Model Training across Heterogeneous Edge Devices
Training models over data distributed across edge devices presents a trade-off: centralizing data simplifies training but raises privacy and regulatory concerns, while full local training may exceed…uTECH Turbo-MOVES: The Intersection of Software Architectural Innovation and Sustainable Mobility Analysis
Transportation is a major source of energy consumption, greenhouse gas emissions, and criteria air pollutants, making efficient emissions estimation essential for environmental planning and policy…DistDebug-Bench: Can LLM Agents Diagnose Bugs in Distributed Systems?
LLM agents show strong potential in software engineering tasks such as competitive programming and code synthesis.A GPU Engine for Sparse Matrix-Vector Products on $k^2$-Tree-Compressed Graphs
The $k^2$-tree is a compact representation of Boolean matrices: it stores the recursive hierarchy of their non-empty submatrices, with neither values nor coordinates, and serves rows and columns…INSIGHT-HPC: An Interpretable System Intelligence for Global HPC Telemetry
Modern high-performance computing (HPC) clusters integrate multi-generation CPUs and GPUs, which generate rich, high-dimensional telemetry streams from out-of-band baseboard management controllers…EpiLoRA: Fusing Many LoRA Adapters into Low-Precision GEMMs for Serving and RL
Low-rank adapters (LoRA) let one base model serve many fine-tuned variants: multi-tenant engines keep a single copy of the base and batch the requests of many tenants, each with its own adapter.ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration
Serving an LLM efficiently depends on the engine, how the model is split across GPUs, precision, scheduler settings, speculative decoding, KV-cache management, and prefill-decode disaggregation.Unlocking Turing Tensor Cores: A Measured Throughput Account of From-Scratch Pre-training on a Single 22G RTX 2080 Ti
The RTX 2080 Ti (TU102) ships full second-generation Tensor Cores in hardware, yet the vendor throttles FP16 matrix multiplication at the driver level, leaving usable tensor throughput well below…Token-as-a-Utility: A Model-Agnostic Architecture for Metering and Provisioning AI Inference Services
Token-based subscriptions are emerging as a commercial model for large language model inference, but extending them into interoperable utility services requires comparable consumption accounting.Serving Agentic Robot Fleets: Workload Characterization, KV-Cache Thrashing, and Admission Control
Agentic robots combine a vision-language brain, a tool-using harness, and a vision-language-action (VLA) policy.DIMWIT: Directly Invoking Models Without Intensive Tuning
Resource managers typically encode what to do in a task-specific policy---a heuristic, an optimization procedure, or a model trained for the task---that must be developed before use and may need…CacheWeave: Learning to Recompute for Efficient Position-Independent KV Caching
Position-independent caching reuses chunk KV caches across positions and requests.A Compute Center Anyone Can Join: A Peer-to-Peer System for AI Inference
Today, most people obtain AI inference by buying it from a small number of organizations that run large data centers, while many GPUs owned by individuals and small institutions sit idle for much of…Rethinking Model Aggregation for Federated Learning with Domain Skew: A Knowledge-Preserving Perspective
Domain skew challenges federated learning by requiring a shared model to accommodate distinct visual distributions.ReWY: Faster Backpropagation for Delta-Rule Attention - From 7 to 3 Matrix Multiplications
Delta-rule linear attention underlies language models such as Kimi Linear and Qwen3-Next.Paying for Fractional Error: Tight Constant-Round Approximation of Covering Problems with Short Broadcasts
We present a general framework for deterministic constant-round approximation of covering problems in sparse networks in the broadcast CONGEST model, where every vertex broadcasts the same $O(\log…Conditional Calibration Contracts under Coupled Load: A Branch-Aware Audit of Switched Inference
Bursty serving changes both queue states and the population selected for each model.Does the Trust Boundary Hold over Time? A Long-Horizon Benchmark for Receiver-Directed Federated Learning
In receiver-directed federated learning, each site draws a trust boundary.