Search: “hardware”

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

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K2Lab: Standalone(ish) Krea2 bbox style prompting and lora containment

DGX agent

I've been digging into Krea2 to see if there's any way to condition inference in specific regions of pixel -> latent space to implement bbox type prompting in order to apply multiple simultaneous char

local-air-stablediffusion

28 Jul 2026

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

model-releasesarxiv-cs-ai

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

researcharxiv-cs-lg

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

model-releasesarxiv-cs-ai

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

Agents

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

Kimi K3 weights drop today. We're deploying on A100s, H200s and B300s this week and the A100 math is already rough

tldr; we are going to host K3 on A100s (yes, thats correct, we'll try to see if it holds up), H200s & B300s - expect results for A100s & H200s this week while we setup the B300 cluster this weekend &

Nvidia forms the Open Secure AI Alliance, a coalition including CrowdStrike, Hugging Face, and Dell to develop and share tools for AI safety and cybersecurity (Jaspreet Singh/Reuters)

Jaspreet Singh / Reuters: Nvidia forms the Open Secure AI Alliance, a coalition including CrowdStrike, Hugging Face, and Dell to develop and share tools for AI safety and cybersecurity — Nvidia (NVDA.

safetytechmeme

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

researcharxiv-cs-lg

Ornith-397B running at Q4 on a single RTX PRO 6000 Blackwell 96GB - 2,354 tok/s prefill, ~20–24 tok/s decode

I've been building Krasis, an MoE-focused runtime for streaming big models through limited VRAM on NVIDIA consumer/workstation GPUs, and I think this is the most interesting result so far: Ornith-1.0-

local-air-localllama

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

Qwen3.6-27B speculative decoding gets better on heavier quants

I finished the speed leg of my spec-decode benchmarking for Qwen3.6-27B, main algorithms across quants. Overall: the heavier the quant, the more spec-decode buys you (10 of 10 speculative configs rank

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

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

model-releasesberkeley-ai-research

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26 Jul 2026

Will prices finally go down?

I am seeing more and more videos as posts about how OpenAI is in complete financial ruin, Anthropic isn't much better. Their expenses go with the revenue they make etc etc. Meta made big investments i

local-air-localllama

26 Jul 2026

Yes, open-source / open-weight models are important for a healthy AI ecosystem. That's how we can verify things, check claims, and keep up o…

model-releasessebastian-raschka--x

Yes, open-source / open-weight models are important for a healthy AI ecosystem. That's how we can verify things, check claims, and keep up outside the closed labs. Plus, it gives us the freedom to run

26 Jul 2026

CachyLLama: llama.cpp fork with persistent SSD-backed KV caching for local agent workflows

If you run local agentic coding harnesses (Aider, Claude Code, etc.), prompt evaluation usually eats up most of your execution time. Every turn re-evaluates thousands of identical prefix tokens_system

I released Inflect v2: two ultra-tiny complete TTS models under 4M and 10M parameters

I’ve spent the past month trying to find the point where an extremely small TTS model stops feeling like a size experiment and starts feeling genuinely useful. Today I’m releasing Inflect v2, with two

I spent a year building a free SDXL & Anima trainer that runs on my 12 GB GPU — here's what came out of it

local-air-stablediffusion

A little over a year ago I got frustrated trying to fine-tune SDXL on my RTX 3060. Every option either forced lower resolution, locked away important settings behind massive config files, or needed a

LFM 2.5 230M running at 1440 tok/s in-browser through a custom backend

Everything runs through WebGPU, in-browser or in electron/tauri apps. It's fully portable and supports either Nvidia and Apple Silicon (Metal). The actual kernels are optimized for the specific hardwa

local-air-localllama

MI50 power curve tests

tests done power limiting the GPU on LACT - real power usage varies wildy at 20W it ranges from 25W to 56W same behavior happens on every setting prompt for the test runs: https://github.com/lukesdevl

Agents

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

model-releasesarxiv-cs-ai

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

CachyLLama’s: llama.cpp fork with persistent KV cache that makes long local-agent sessions much less painful

I’m not affiliated with this project, but I’ve been running it recently and I’m surprised it hasn’t received more attention here: https://github.com/fewtarius/CachyLLama CachyLLama is a fork of llama.

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

Getting the most out of MTP

If you want to get the most out of MTP. You have to run some tests / benchmarks to do so. Turning it on with defaults will get improvements, but for many models and card combinations, you are leaving

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

Agents

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