Systems & Networks · Computer Science & AI
Performance
The newest papers in Performance that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
158 papers, from 4 September 2026 to 5 November 2026: 158 arXiv preprints.
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CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances
Technology-mediated audience participation (TMAP) enables audiences to influence live performances, yet balancing interaction with meaningful agency remains challenging.An Empirical Evaluation of Signing and Verification Performance of Verifiable Credential Formats: A Cross-Language Benchmark of SD-JWT VC, W3C VCDM, and mdoc
We compare the signing and verification performance of three Verifiable Credential (VC) formats--SD-JWT VC; the W3C Verifiable Credentials Data Model 2.0 with a Data Integrity cryptosuite (W3C…Sonar-MoE: In-Backward Expert Updates for Memory-Efficient Mixture-of-Experts Training
Most parameters of a Mixture-of-Experts (MoE) model belong to its experts, and standard training keeps every expert gradient in GPU memory until the optimizer step.Exploring the Power Consumption of ERC-4337 Bundling on Server Infrastructure
The Ethereum ecosystem offers the most widespread support for Decentralised Applications (DApps), with over 120 networks currently documented as using variants of the Ethereum Virtual Machine (EVM)…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.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…Robust Remote ECG Monitoring under Energy Constraints: Anomaly Filtering versus Predictive Uncertainty
Machine learning for remote electrocardiogram monitoring must handle noise and diseases absent from training under tight resource constraints.FieldRT: Reconfigurable Architecture from Geometric Sensitivity to Streaming Acceleration
Geometric perception on edge must simultaneously achieve two goals that are rarely addressed together: preserve accurate spatial understanding for object localization and operate within stringent…Robustness Starts with the Objective: Designing and Certifying Robust LLM-Generated Heuristics
LLMs are increasingly used to design heuristics for computer systems.BitLLM: Efficient Low-Bit, Conversion-Hiding LLM Decoding on Mobile GPUs
The rapid scaling of large language models (LLMs) has unlocked remarkable capabilities across various domains.Ripple: Deadline-Aware CPU-GPU Sharing for Embodied Inference
Unified-memory edge devices increasingly run LLM/VLM inference alongside time-critical CPU tasks for perception and control.Capacity Planning for Resource-Constrained Clinical LLM Serving
Deploying large language models (LLMs) under a fixed accelerator budget requires jointly deciding how computational capacity is allocated across model sizes and how incoming requests are routed…TRIAGE: Reliability- and Load-Conditioned Escalation for Two-Tier LLM Serving with a Semantic Evidence Judge
Two-tier LLM serving escalates selected requests from a small model to a large one, but prompt-based routers cannot see whether retrieved evidence supports an answer, and they treat the GPU cost of…Benchmark Study for Quantum Statevector Simulators for Scientific Computing
Quantum circuit simulators are essential for quantum algorithm development, yet systematic runtime benchmarks comparing major frameworks across CPU and GPU backends are limited.AdapBench: Adaptive Early-Stopping and Prioritization for Efficient Microbenchmarking in Cloud-based CI/CD
Cloud-based software systems frequently release new versions, a fact further accelerated through agentic coding.Understanding the Energy Cost of Quantized Agents on Shared GPUs
Quantization can lower the energy of language-model calls and free memory for serving more agents, but its benefit per completed task remains unclear when agents share a GPU.SpBench: An Agent-Native Framework for Cross-Engine Lakehouse Benchmarking
SpBench is an agent-native framework for cross-engine lakehouse benchmarking, in production use for benchmarking Snowpark, Snowpark Connect for Apache Spark, and other execution environments across…When the Same Prompt Gives Different Answers (and the Same Answer Isn't Always Right): A Taxonomy of Failure Mechanisms, Testing, and Mitigation Strategies for LLM-Assisted Evidence Synthesis Read-and-Evaluate Tasks
LLM read-and-evaluate tasks include study screening, data extraction, risk-of-bias assessment, and critical appraisal in the evidence synthesis field.How Far Are We From On-Device VLM Inference? A Measurement Study on Edge NPUs
Diverse optimizations spanning model architecture, inference runtime, and hardware acceleration have been proposed for on-device vision-language model (VLM) deployment on edge NPUs.