Search: “inference”

PRISM: Prompt Refinement via Image-grounded Self-rewarding Mechanism for Text-to-Image Generation

arXiv:2607.24353v1 Announce Type: new Abstract: Text-to-image generation models can synthesize high-quality images from natural language descriptions, but their performance remains highly sensitive to

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Local Ai

QueenVIS: Rethinking Image-Only Training for Video Instance Segmentation via Query Enrichment

DGX agent

arXiv:2607.24598v1 Announce Type: new Abstract: Video instance segmentation (VIS) requires models to detect, segment, and track object identities across frames, and most methods enforce temporal consi

local-aiarxiv-cs-cv

28 Jul 2026

Hardware

Real2Sim2Real for Vision-Language-Action Manipulation: An AMD ROCm-Based Pipeline

arXiv:2607.22997v1 Announce Type: cross Abstract: Physical AI -- the integration of large vision-language-action (VLA) models with embodied agents that act in the real world -- has emerged as the next

hardwarearxiv-cs-ai

Rethinking Expert Training for Model Merging with Prompt Learning

model-releasesarxiv-cs-cv

arXiv:2607.24465v1 Announce Type: new Abstract: Model merging aims to combine multiple domain-specialized experts trained from a shared foundation model into a single multi-task model. Existing approa

Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting

arXiv:2602.17634v2 Announce Type: replace-cross Abstract: Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time ser

researcharxiv-cs-ai

Simulating Tenant Responses to Energy Policy Interventions with Transaction-Cost-Aware LLM Age

arXiv:2607.24341v1 Announce Type: new Abstract: Recent studies use Large language models (LLMs) to simulate human opinions and decisions by prompting models with demographic, attitudinal, or persona-b

Small, Bias-Free, Blind and Convolutional Denoiser: A compact ConvNeXt U-Net for blind Gaussian color-image denoising

model-releasesarxiv-cs-cv

arXiv:2607.22793v1 Announce Type: cross Abstract: We describe and evaluate BF-ConvUNeXt, a compact bias-free ConvNeXt U-Net for blind additive-white-Gaussian-noise color image denoising, combining fou

StageGuard: Physiologically Constrained Sleep Staging

arXiv:2607.23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficie

StepX-Edge: An On-Device UI Vision-Language Model via Architecture-Training-Deployment Co-Design

model-releasesarxiv-cs-cv

arXiv:2607.22708v1 Announce Type: new Abstract: Deploying a vision-language model with full UI understanding on end devices has long been trapped between accuracy and efficiency: on one side is the ac

Test-Time Adaptation via Dual Distillation for Videos Under Severe Distribution Shifts

arXiv:2607.24611v1 Announce Type: new Abstract: Deep learning models have achieved state-of-the-art performance in several computer vision tasks. However, they experience severe performance degradatio

Agents

Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning

arXiv:2607.22697v1 Announce Type: new Abstract: Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution. However, st

agentsarxiv-cs-ai

The Kimi K3 architecture figure for yesterday's big open-weight model release, along with some observations and thoughts. 1. Yes, it looks r…

model-releasessebastian-raschka--x

The Kimi K3 architecture figure for yesterday's big open-weight model release, along with some observations and thoughts. 1. Yes, it looks relatively complicated, but it's essentially a scaled-up prod

Applications

The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing

arXiv:2607.24396v1 Announce Type: cross Abstract: In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware

applicationsarxiv-cs-ai

Through the Bottleneck: How Multi-head Latent Attention Separates Content from Position in Language Models

arXiv:2607.23054v1 Announce Type: cross Abstract: Multi-head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value pairs through a shared low-rank bottleneck (cKV), achieving 81% KV-

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective

arXiv:2607.24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diff

UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling

arXiv:2607.24157v1 Announce Type: new Abstract: Modern computer vision pipelines remain fragmented, with tasks such as text-to-image generation, editing, restoration, and classical perception handled

researcharxiv-cs-cv

VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy

arXiv:2607.23006v1 Announce Type: cross Abstract: Scientific question answering requires a retrieval system to solve two distinct problems: identifying which papers are relevant and locating the suppo

local-aiarxiv-cs-ai

Applications

Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models

arXiv:2607.22723v1 Announce Type: new Abstract: Visual information extraction (VIE) from visually rich documents remains challenging due to high layout variability and real-world impairments. Existing

applicationsarxiv-cs-cv

Voice-Driven Semantic Perception for UAV-Assisted Emergency Networks

arXiv:2602.17394v2 Announce Type: replace-cross Abstract: Unmanned Aerial Vehicle (UAV)-assisted networks are increasingly foreseen as a promising approach for emergency response, providing rapid, fle

researcharxiv-cs-ai

WaveZip: Wavelet-Driven Space-Time Decoupling for Video Token Condensation

arXiv:2607.23265v1 Announce Type: new Abstract: Existing Large Vision-Language Models (LVLMs) struggle with long-form video understanding due to the quadratic computational cost of visual tokens. Whil

researcharxiv-cs-cv

White-hat hacking IS the defense to black-hat hacking. The techniques are the same. How does Dario expect companies to do it if their models refuse?

You patch security holes by intentionally finding them. If the models refuse to do it, how can companies protect themselves against rogue AIs, whether they are Chinese or OpenAI/Anthropic themselves?

safetyr-localllama

Wired for Overconfidence: A Mechanistic Perspective on Inflated Verbalized Confidence in LLMs

arXiv:2604.01457v3 Announce Type: replace Abstract: Large language models are often not just wrong, but confidently wrong: when they produce factually incorrect answers, they tend to verbalize overly

researcharxiv-cs-cl

WISERouter: LLM Routing with Workload Budget Constraint

arXiv:2607.23765v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance across multiple domains, but using the most capable model for every query is prohibitive a

researcharxiv-cs-ai

Zing: Social Mind for LLMs

model-releasesarxiv-cs-cl

arXiv:2607.23740v1 Announce Type: new Abstract: As large language models move from isolated task solving toward long-term service in human environments, they require social intelligence: the ability t

Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits

arXiv:2607.22012v1 Announce Type: new Abstract: Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and

Distributional Determinantal Point Process for Repulsive Clustering of Distributions

arXiv:2607.21847v1 Announce Type: cross Abstract: We introduce the distributional determinantal point process (dDPP) as a novel repulsive point process whose atoms are probability distributions rather

researcharxiv-cs-lg

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

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression

arXiv:2506.18748v2 Announce Type: replace-cross Abstract: We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subjec

Agents

Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes

arXiv:2506.13553v4 Announce Type: replace Abstract: Road topology reasoning is fundamental for autonomous driving, requiring both accurate perception of road elements and understanding of their comple

agentsarxiv-cs-cv

IFCLoRA: Topology-Aware Rank Allocation for Parameter-Efficient Fine-Tuning

arXiv:2607.22251v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for large language models, but its performance depends strongly on ho

k{appa}-LoRA: Condition Numbers Reveal Which LoRA Matrices Worth Updating

arXiv:2607.22489v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted technique for efficient neural network fine-tuning, decomposing model updates into low-rank matri

LayoutLite: Token-Level Implicit Layout Analysis for Efficient Document OCR

arXiv:2607.22200v1 Announce Type: new Abstract: End-to-end OCR systems based on vision-language models have achieved strong performance in complex document OCR, but their efficiency is limited by the

Learning What Matters: Supervising Sparse Attention Routing with Causal Evidence Sets

model-releasesarxiv-cs-cl

arXiv:2607.21692v1 Announce Type: cross Abstract: Sparse attention reduces the cost of long contexts by allowing each query to read only selected parts of the input. These selectors are often trained

Modernizing the skies: NOAA and Google Cloud collaborate to advance weather forecasting

model-releasesgoogle-cloud-ai

The National Oceanic and Atmospheric Administration (NOAA) is embarking on a transformative journey to redefine how we understand and predict patterns in the Earth’s atmosphere that affect the weather

Ordered Action Tokens for Visuomotor Policy Learning

arXiv:2607.21670v1 Announce Type: cross Abstract: Action tokenization maps continuous robot action chunks to discrete tokens and has become an important interface for modern visuomotor policies. Exist

Parameter-free Adaptive Sparse Attention via Compression-Based Content Selection

arXiv:2607.21752v1 Announce Type: new Abstract: Data-adaptive sparse attention masks substantially outperform fixed patterns (e.g., BigBird and Longformer) and can even exceed dense attention on long

Probing Speaker Identity Sensitivity in Audio Deepfake Detectors

arXiv:2607.21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks. Yet the same d

researcharxiv-cs-lg

Procedural Knowledge Is Not Low-Rank: Why LoRA Fails to Internalize Multi-Step Procedures

arXiv:2607.21612v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods like LoRA have become the default for adapting large language models, succeeding across instruction following,

RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory

arXiv:2607.21731v1 Announce Type: new Abstract: Transformers are widely used across many domains, including natural language processing, computer vision, web search, and DNA sequence analysis. Given t

local-aiarxiv-cs-lg

Spectral Prior for Reducing Exposure Bias in Diffusion Models

arXiv:2607.22091v1 Announce Type: new Abstract: Diffusion models typically suffer from error accumulation during iterative sampling, commonly referred to as exposure bias. We reveal systematic frequen

Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions

arXiv:2607.21635v1 Announce Type: new Abstract: Personal agents maintain memories, learned skills, tool configurations, and policy state that evolve with each user. Existing agent benchmarks often eva

Unexpected use of local llm

I was refreshing my youtube and found out my favourite reviewer uploaded a battery test of 78 smartphones: https://youtu.be/MpgUFrsIWSQ the author said they started using robotic arm to simulate a per

local-air-localllama

Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis

arXiv:2607.22262v1 Announce Type: cross Abstract: Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spati

100B Models on Cheap Hardware: how realistic and limitations

There is a lot of buzz around running 100B parameter models on cheap local hardware using ternary (1.58-bit) quantization like Microsoft's BitNet architecture. The theoretical hardware shortcuts are i

model-releasesr-ollama

26 Jul 2026

We open-sourced Logue — a privacy-first macOS meeting-notes + writing app that runs on-device (MLX, Apple Silicon) entirely

At Bitwize, we've been building Logue, a native macOS app for AI meeting notes and writing, and we just open-sourced it (MIT). We're sharing it here because the whole point is that it runs 100% on-dev

local-air-localllama

26 Jul 2026

Hardware

Can AMD break the CUDA Moat? AMD Advancing AI 2026

AMD’s AI accelerator software stack has progressed sharply, moving from a 0 % chance of catching Nvidia’s CUDA moat in early 2025 to a “great chance” of success by July 2026 after leadership changes a

hardwaresemianalysis

25 Jul 2026

PSA: DO NOT use Intel consumer platforms for multi-GPU setups

Since a lot more people are trying to build their own multi-GPU machines, I thought I should help to prevent a common mistake people make with building multi-GPU machines. Which is using an Intel cons

local-air-localllama

25 Jul 2026

A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Prediction

arXiv:2607.20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge,