Search: “inference”

Online Localized Conformal Prediction

arXiv:2605.05497v2 Announce Type: replace Abstract: Conformal prediction is a framework that provides valid uncertainty quantification for general models with exchangeable data. However, in the online

Tutorials

PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks

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DGX agent

arXiv:2605.06833v1 Announce Type: cross Abstract: Misbehavior detection in Vehicle-to-Everything (V2X) networks is a second line of defense against insider falsification attacks that cryptographic mec

tutorialsarxiv-cs-ai

11 May 2026

Pan-FM: A Pan-Organ Foundation Model with Saliency-Guided Masking for Missing Robustness

arXiv:2605.07055v1 Announce Type: cross Abstract: Foundation models (FMs) have shown great promise in medical imaging, but most FMs are trained on unimodal data within isolated domains, such as brain

PaT: Planning-after-Trial for Efficient Test-Time Code Generation

arXiv:2605.07248v1 Announce Type: new Abstract: Beyond training-time optimization, scaling test-time computation has emerged as a key paradigm to extend the reasoning capabilities of Large Language Mo

safetyarxiv-cs-cl

Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics

arXiv:2605.07738v1 Announce Type: cross Abstract: Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simul

ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices

arXiv:2602.21858v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have made significant progress in mobile agent development, yet their capabilities are predominantly confin

Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretation

arXiv:2605.07785v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) in medical imaging aim to improve model interpretability by predicting intermediate clinical concepts before final diag

safetyarxiv-cs-cv

Hardware

RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory

arXiv:2605.06675v1 Announce Type: cross Abstract: Large language models cache all previously computed key-value (KV) pairs during generation, and this KV cache grows linearly with sequence length, mak

hardwarearxiv-cs-cl

Agents

RelAgent: LLM Agents as Data Scientists for Relational Learning

arXiv:2605.07840v1 Announce Type: new Abstract: Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, g

agentsarxiv-cs-lg

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

arXiv:2605.06173v2 Announce Type: replace-cross Abstract: Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems

Local Ai

RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics

arXiv:2510.24736v3 Announce Type: replace-cross Abstract: Generating property-optimized mRNA sequences is central to applications such as vaccine design and protein replacement therapy, but remains ch

local-aiarxiv-cs-lg

Sat3R: Satellite DSM Reconstruction via RPC-Aware Depth Fine-tuning

model-releasesarxiv-cs-cv

arXiv:2605.07264v1 Announce Type: new Abstract: Accurate Digital Surface Model (DSM) reconstruction from satellite imagery is critical for applications such as disaster response, urban planning, and l

SatSurfGS: Generalizable 2D Gaussian Splatting for Sparse-View Satellite Surface Reconstruction

model-releasesarxiv-cs-cv

arXiv:2605.07181v1 Announce Type: new Abstract: Sparse-view satellite image surface reconstruction remains highly challenging, fundamentally because the reliability of multi-view matching under satell

Scaling Categorical Flow Maps

arXiv:2605.07820v1 Announce Type: new Abstract: Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they u

SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion

arXiv:2605.07482v1 Announce Type: cross Abstract: Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous

researcharxiv-cs-ai

Hardware

Sparser, Faster, Lighter Transformer Language Models

arXiv:2603.23198v2 Announce Type: replace-cross Abstract: Scaling autoregressive large language models (LLMs) has driven unprecedented progress but comes with vast computational costs. In this work, w

hardwarearxiv-cs-cl

SparseRL-Sync: Lossless Weight Synchronization with ~100x Less Communication

arXiv:2605.07330v1 Announce Type: cross Abstract: In large-scale reinforcement learning (RL) systems with decoupled Trainer-Rollout execution, the Trainer must regularly synchronize policy weights to

Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control

arXiv:2605.07182v1 Announce Type: new Abstract: Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiri

Tutorials

Structured Coupling for Flow Matching

arXiv:2605.07676v1 Announce Type: new Abstract: Standard flow matching scales well but typically relies on an unstructured source distribution, limiting its ability to learn interpretable latent struc

tutorialsarxiv-cs-lg

SWaRL: Safeguard Code Watermarking via Reinforcement Learning

arXiv:2601.02602v2 Announce Type: replace-cross Abstract: We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLMs by embedd

Sword: Style-Robust World Models as Simulators via Dynamic Latent Bootstrapping for VLA Policy Post-Training

arXiv:2605.07288v1 Announce Type: cross Abstract: The integration of Vision-Language-Action (VLA) models with World Models has gained increasing attention. One representative approach treats learned W

Theoretical Limits of Language Model Alignment

arXiv:2605.07105v1 Announce Type: cross Abstract: Language model (LM) alignment improves model outputs to reflect human preferences while preserving the capabilities of the base model. The most common

safetyarxiv-cs-cl

ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models

arXiv:2510.01290v2 Announce Type: replace Abstract: The long-output context generation of large reasoning models enables extended chain of thought (CoT) but also drives rapid growth of the key-value (

Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models

arXiv:2605.06683v1 Announce Type: cross Abstract: Transformer-based large language models are in some respects limited by the quadratic time and space computational complexity of attention. We introdu

Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning

arXiv:2605.07799v1 Announce Type: cross Abstract: Training foundation models is computationally intensive and often slow to converge.We introduce PIQL,Privileged Information for Quick and Quality Lear

researcharxiv-cs-ai

Training-Free Multimodal Large Language Model Orchestration

arXiv:2508.10016v3 Announce Type: replace Abstract: Building interactive omni-modal assistants often relies on end-to-end multimodal alignment to fuse heterogeneous modalities, which incurs substantia

safetyarxiv-cs-cl

Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation

arXiv:2605.07924v1 Announce Type: cross Abstract: Discrete flow matching generates text by iteratively transforming noise tokens into coherent language, but may require hundreds of forward passes. Dis

TriP: A Triangle Puzzle Approach to Robust Translation Averaging

arXiv:2605.07143v1 Announce Type: new Abstract: Translation averaging aims to recover camera locations from pairwise relative translation directions and is a fundamental component of global Structure-

researcharxiv-cs-cv

Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting

arXiv:2605.06916v1 Announce Type: new Abstract: Probabilistic weather forecasting requires not only accurate trajectories, but calibrated distributions over plausible atmospheric futures. Recent data-

Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs

arXiv:2605.08045v1 Announce Type: new Abstract: Converting free-text cardiac magnetic resonance (CMR) reports into auditable structured data remains a bottleneck for cohort assembly, longitudinal cura

researcharxiv-cs-cl

Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods

arXiv:2605.07987v1 Announce Type: cross Abstract: Atlas-based approaches allow high-quality, patient-specific shape reconstructions of cardiac anatomy from sparse and/or noisy data such as point cloud

researcharxiv-cs-cv

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors

arXiv:2505.11325v4 Announce Type: replace-cross Abstract: Prior-data fitted networks (PFNs) have emerged as promising foundation models for prediction from tabular datasets, achieving state-of-the-art

applicationsarxiv-cs-ai

VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training

arXiv:2602.10693v3 Announce Type: replace-cross Abstract: Off-policy updates are inevitable in reinforcement learning (RL) for large language models (LLMs) due to rollout staleness from asynchronous t

VideoRouter: Query-Adaptive Dual Routing for Efficient Long-Video Understanding

arXiv:2605.05848v2 Announce Type: replace-cross Abstract: Video large multimodal models increasingly face a scalability bottleneck: long videos produce excessively long visual-token sequences, which s

VIMCAN: Visual-Inertial 3D Human Pose Estimation with Hybrid Mamba-Cross-Attention Network

arXiv:2605.07552v1 Announce Type: new Abstract: The rapid advances in deep learning have significantly enhanced the accuracy of multimodal 3D human pose estimation (HPE). However, the state-of-the-art

researcharxiv-cs-cv

Weblica: Scalable and Reproducible Training Environments for Visual Web Agents

arXiv:2605.06761v1 Announce Type: new Abstract: The web is complex, open-ended, and constantly changing, making it challenging to scale training data for visual web agents. Existing data collection at

applicationsarxiv-cs-ai

When Does Embedding Magnitude Matter? A Cross-Task Functional-Symmetry Framework

arXiv:2602.09229v3 Announce Type: replace Abstract: Cosine similarity normalizes both sides; dot product normalizes neither. We propose a 2x2 framework that independently controls query-side and docum

Downloading now... 1M token context window with supposedly usable coding agent capability all on a 128GB Macbook Pro is 🤯

model-releasesclem-delangue--x

Downloading now... 1M token context window with supposedly usable coding agent capability all on a 128GB Macbook Pro is 🤯 🚨 OPEN SOURCE AI IS LITERALLY UNSTOPPABLE 🚨 The legendary founder of Redis (An

9 May 2026

Open sourced an iOS app that runs LLMs on-device with llama.cpp, and lets you plug in your own Ollama for automatic health insights from HealthKit

An iOS application that enables large language models to run directly on-device using llama.cpp technology, allowing users to integrate their own Ollama instances for processing Apple HealthKit data t

model-releasesr-ollama

9 May 2026

Local Ai

Chrome's 4GB AI model isn't new, but you're not wrong for being confused

Google has offered Gemini Nano for Chrome since 2024 as a lightweight, on-device model , but users reasonably expect the visible AI Mode to use the on-device model with queries staying local, when in

local-aiars-technica

8 May 2026

Red Hat brings AI, virtualization and hybrid cloud under one platform

Enterprise artificial intelligence is growing more complicated, and platform engineering is becoming the control layer that keeps it moving. As AI moves into production, the challenge is no longer jus

applicationssiliconangle

8 May 2026

Local Ai

3D Ultrasound-Derived Pseudo-CT Synthesis Using a Transformer-Augmented Residual Network for Real-Time Operator Guidance

arXiv:2605.04856v1 Announce Type: new Abstract: Computed tomography (CT) is indispensable for clinical diagnosis and image-guided interventions but exposes patients to ionizing radiation, motivating t

local-aiarxiv-cs-cv

Hardware

A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers

arXiv:2605.04074v1 Announce Type: new Abstract: AI data centers experience rapid fluctuations in power demand due to the heterogeneity of computational tasks that they have to support. For example, th

hardwarearxiv-cs-lg

A Scalable Multi-Task Model for Virtual Sensors

arXiv:2601.20634v2 Announce Type: replace Abstract: Virtual sensors replace expensive physical sensors in critical applications through machine learning by predicting target signals from available mea

Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs

arXiv:2605.04169v1 Announce Type: cross Abstract: Surgical team performance arises from complex interactions between technical execution and non-technical skills, including communication and coordinat

Active Contact Sensing for Robust Robot-to-Human Object Handover

arXiv:2605.04610v1 Announce Type: new Abstract: Robot-to-human object handover is an essential skill for robot assistants, from serving drinks at home to passing surgical tools in the operating room.

researcharxiv-cs-ro

Adaptive Consensus in LLM Ensembles via Sequential Evidence Accumulation: Automatic Budget Identification and Calibrated Commit Signals

arXiv:2605.04236v1 Announce Type: new Abstract: Large Language Model ensembles improve reasoning accuracy up to a performance boundary; beyond it, additional deliberation degrades accuracy. Static-bud

Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning

arXiv:2605.04911v1 Announce Type: new Abstract: Tabular data synthesis aims to generate high-quality data while preserving privacy. However, we find that existing tabular generative models exhibit a c

applicationsarxiv-cs-lg