Search: “hardware”

When Fish Look Alike: Tracking Identities with Dual-branch Elasticity

model-releasesarxiv-cs-cv

arXiv:2607.26412v1 Announce Type: new Abstract: Tracking dense, homogeneous targets like schooling fish remains a major challenge for multiple object tracking due to extreme inter-individual homogenei

30 Jul 2026

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Addressable Recall Compaction for Long Context-Window Control in AI Agents

DGX agent

arXiv:2607.25066v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate reasoning traces, actions, and tool observations that can eventually exceed a model's fixed context window. Existing

model-releasesarxiv-cs-ai

29 Jul 2026

Agentic AI for Scientific Reasoning in Autonomous Quantum Sensing Experiments

arXiv:2607.25145v1 Announce Type: cross Abstract: We implement an agentic AI workflow built around a large language model (LLM) agent for autonomous experiments with nitrogen-vacancy (NV) centers in d

Aletheia: An Offline-First Clinical Decision Support System for Differential Diagnosis in Low-Resource Healthcare Settings

arXiv:2607.24814v1 Announce Type: new Abstract: Access to specialist clinical expertise remains severely limited across sub-Saharan Africa, where physician-to-patient ratios can fall below 1:25,000 in

Applications

AngelSpec: Towards Real-World High Performance Inference with Speculative Decoding

arXiv:2607.25852v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference without changing the target distribution, but no single drafting structure performs best

applicationsarxiv-cs-cl

pimathbf{R}^2: Reactive Real-time Flow Policies

arXiv:2607.26055v1 Announce Type: cross Abstract: Generalist manipulation policies increasingly take the form of action-chunking flow policies built on large pretrained backbones. Such chunks run open

safetyarxiv-cs-ai

ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop

arXiv:2607.24770v1 Announce Type: new Abstract: Procedural tasks such as furniture assembly and home repair impose substantial cognitive demands because users must interpret instructions, track task p

local-aiarxiv-cs-ai

Bit-Accurate FPGA Evaluation of Learned Feature Gating in a Fixed-Point Fourier-Feature Automatic Modulation Classifier

arXiv:2607.24568v1 Announce Type: new Abstract: Learned feature reweighting can improve automatic modulation classification (AMC) in software, but the same operation introduces additional arithmetic a

CausalGate: Causal Importance Distillation for Transformer Module Pruning

model-releasesarxiv-cs-cl

arXiv:2607.22720v1 Announce Type: cross Abstract: Existing adaptive inference methods for Large Language Models rely on observational heuristics, such as hidden-state similarity or activation magnitud

Characterizing Arbitrary Lindbladian Dynamics with a Few Pauli Measurements

arXiv:2607.23044v1 Announce Type: cross Abstract: Quantum devices are open systems whose dynamics interleave coherent evolution with dissipation, and benchmarking, error mitigation, and error correcti

Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI

arXiv:2607.23972v1 Announce Type: new Abstract: Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing survey

researcharxiv-cs-cv

DAP-Pose: Deep Temporal Alignment and Physics-aware Cross-modal Sensor Fusion for Robust Pose Estimation

model-releasesarxiv-cs-cv

arXiv:2607.23755v1 Announce Type: new Abstract: Robust and accurate pose estimation with multi-modal sensors is fundamental for autonomous vehicles and mobile robotic systems in complex environments.

Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines

arXiv:2607.24692v1 Announce Type: cross Abstract: Inference systems increasingly combine a fast path that returns predictions within the application's latency deadline together with a higher-accuracy

agentsarxiv-cs-ai

From Data to Device: ELMOD An Efficient German-First 2.7B Language Model for Mobile Inference

model-releasesarxiv-cs-cl

arXiv:2607.24585v1 Announce Type: new Abstract: We present ELMOD - Efficient Language Model for On-Device Deployment - a compact (2.7B) German language model designed for efficient inference on resour

Generative Artificial Intelligence (GenAI) to convert images of queuing networks into verifiable simulation models: an open-weight LLM workflow approach

arXiv:2607.24259v1 Announce Type: new Abstract: Recent work has explored the use of Large Language Models (LLMs) to automate simulation model building, typically by generating executable code directly

Infrared Imaging Empowered by Artificial Intelligence for Pediatric Skeletal Triage: A Narrative Review and Future Perspectives

arXiv:2607.24727v1 Announce Type: new Abstract: Background. Pediatric musculoskeletal trauma represents up to 18% of pediatric ED visits, yet diagnosis still depends on ionizing radiography. Cumulativ

safetyarxiv-cs-cv

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting

arXiv:2607.22568v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed on mobile and embedded devices to improve privacy and reduce network latency. Yet on-device infer

local-aiarxiv-cs-ai

ML-based Predictive Models for Power Consumption in Virtualised O-RANs

arXiv:2607.24256v1 Announce Type: cross Abstract: As communication networks adopt virtualized and disaggregated architectures, achieving energy efficiency has become increasingly important for both ec

Multi-Objective Structured Pruning of LLMs for Latency and Model Size Optimization

arXiv:2607.22583v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved widespread adoption because of their strong reasoning and query-response capabilities. However, deploying the

Neuromorphic Object Detection: An In-Depth Study and Future Directions

model-releasesarxiv-cs-cv

arXiv:2607.23576v1 Announce Type: new Abstract: Conventional frame-based cameras face significant challenges in detecting objects under high-speed motion blur or in low-light environments. Neuromorphi

Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs

arXiv:2607.22786v1 Announce Type: cross Abstract: In this work, we explore how the inference time of a Transformer Neural Network can be efficiently optimized with applications to real-time anomaly de

Practical advantage beyond the quadratic speedup limit with fully-quantum walks

arXiv:2607.22818v1 Announce Type: cross Abstract: We introduce a new class of fully-quantum Metropolis walks in which both the proposal and acceptance steps are intrinsically quantum. Unlike standard

Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Management

arXiv:2607.24352v1 Announce Type: new Abstract: The aim of this article is to verify whether integrating large language models (LLMs) with the Retrieval-Augmented Generation (RAG) architecture enables

local-aiarxiv-cs-cl

Stacking the Deck: Tunable Trainability in Stacked LCUs

arXiv:2607.24686v1 Announce Type: cross Abstract: Variational quantum circuits have been central to many proposed near-term applications of quantum computing, but a growing body of evidence suggests t

SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface

arXiv:2606.18816v2 Announce Type: replace-cross Abstract: Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimen

Tutorials

The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows

arXiv:2607.24569v1 Announce Type: cross Abstract: Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation. In controlled wake f

tutorialsarxiv-cs-lg

TriSP: Tri-Signal Structured Pruning for Large Language Models

arXiv:2607.22587v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance across diverse tasks but their deployment is constrained by the memory and compute cost of their

Decentralized Multi-Agent Swarms for Autonomous Grid Security in Industrial IoT: A Consensus-based Approach

arXiv:2601.17303v2 Announce Type: replace Abstract: As Industrial Internet of Things (IIoT) environments scale to tens of thousands of connected devices, centralized security architectures introduce l

agentsarxiv-cs-lg

Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification

arXiv:2607.21186v1 Announce Type: cross Abstract: Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning

Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA

arXiv:2607.21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the nak

Applications

HD3C: Efficient Medical Data Classification for Edge Devices

arXiv:2509.14617v4 Announce Type: replace Abstract: Efficient medical data classification is essential for modern disease screening, particularly in resource-constrained environments where power budge

applicationsarxiv-cs-lg

ISPCloak: Weaponizing ISP for Optimization-Free Physical Camouflage against Deepfake Detectors

arXiv:2607.21897v1 Announce Type: new Abstract: The rapid advancement of generative models has spurred the critical need to evaluate the worst-case robustness of deepfake detectors. In this paper, we

researcharxiv-cs-cv

On the Depth Scalability of Logic Gate Networks

arXiv:2607.21633v1 Announce Type: new Abstract: Logic Gate Networks (LGNs) implement computation through compositions of Boolean operations, yet unlike classical Boolean circuits, existing LGNs do not

Plug, Play, and Comply: A Modular Framework for Online Variable Impedance with Arbitrarily Oriented Compliance Axes

arXiv:2607.22483v1 Announce Type: new Abstract: The paper proposes a robot-agnostic compliant-control framework that extends the ROS control ecosystem with standardized joint and Cartesian command int

researcharxiv-cs-ro

QC-PHAST Search: Classical--Quantum Query Benchmarks for Finite-Pool Rare-Regime Discovery

arXiv:2607.21995v1 Announce Type: cross Abstract: Rare-regime discovery in parameterized dynamical systems is an active-search problem: find one verified parameter at which a scientifically defined qu

Safe Learning Predictive Control for Ego-World Robotic Systems

arXiv:2607.22225v1 Announce Type: new Abstract: Safe autonomous navigation in shared environments requires the ability to anticipate and react to the latent behaviors of surrounding robots. In this pa

safetyarxiv-cs-ro

Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study

arXiv:2607.21866v1 Announce Type: new Abstract: Prior classical-ML learning-curve work fits power laws to tree, linear, and kernel models on tabular data, but at small scale: typically one curve, one

ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation

arXiv:2607.22530v1 Announce Type: new Abstract: Contact-rich robot manipulation requires physical interaction cues that are often invisible to cameras, making tactile sensing essential for robust cont

safetyarxiv-cs-ro

A real-time RGB-D perception pipeline for autonomous impact hammers in mining: self-filtering, rock segmentation and rock-breaking poses generation

arXiv:2607.20748v1 Announce Type: cross Abstract: Impact hammers, also known as rock-breakers, are essential machines in mining operations, where they perform secondary reduction. In underground minin

agentsarxiv-cs-cv

Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks

arXiv:2607.21482v1 Announce Type: new Abstract: Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models. Their

AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation

model-releasesarxiv-cs-ro

arXiv:2607.21588v1 Announce Type: new Abstract: Learning effective robot manipulation policies requires diverse, high-quality demonstrations, yet existing data pipelines are often difficult to scale b

CRAWO: Custom Resources for Adaptive Workload Orchestration

arXiv:2607.20490v1 Announce Type: new Abstract: Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralized cloud data

FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

arXiv:2607.18020v2 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) solve PDEs by incorporating physical constraints into neural-network training, but large-scale problems are

HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting

arXiv:2606.17028v2 Announce Type: replace-cross Abstract: Simple linear and frequency-domain models remain surprisingly competitive in long-horizon time-series forecasting, and recent mechanistic evid

i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation

arXiv:2508.11485v3 Announce Type: replace Abstract: Accurate and reliable navigation is crucial for autonomous unmanned ground vehicles (UGVs). However, current UGV datasets fall short in meeting the

agentsarxiv-cs-ro

Interaction Dynamics Modeling and Predictive Control for Safe Steerable Catheter--Tissue Interaction

arXiv:2607.20939v1 Announce Type: cross Abstract: Safe steerable catheter control is fundamentally a problem of interaction dynamics: the tip must follow a planned motion, remain compliant against mov

safetyarxiv-cs-ai

Leaky Language Models: Stealing Architecture and Inference Optimizations via Per-Token Timing

arXiv:2607.20723v1 Announce Type: cross Abstract: This work presents LeakyLMs, a set of attacks that leak proprietary model, architecture, and deployment information from production language models. L

Machine Learning for Charge State Characterization of Isolated Double Quantum Dots

arXiv:2607.20871v1 Announce Type: cross Abstract: Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on t

local-aiarxiv-cs-lg