Search: “inference”

FutureBridge: Token Selection Beyond Local Preference in Collaborative Decoding

arXiv:2608.06819v1 Announce Type: cross Abstract: Token-level collaboration allows a large language model (LLM) to assist a small language model (SLM) when their predictions diverge. Existing methods

local-aiarxiv-cs-ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tutorials

H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentation

DGX agent

arXiv:2608.07340v1 Announce Type: cross Abstract: Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through

tutorialsarxiv-cs-ai

10 Aug 2026

Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design

arXiv:2608.07091v1 Announce Type: cross Abstract: Edge Artificial Intelligence (Edge AI) enables the deployment of AI models directly on local edge devices, while such deployments are subject to stric

local-aiarxiv-cs-ai

Is Forward Prediction Enough? Physical State Grounding for JEPA World Models

arXiv:2608.06799v1 Announce Type: cross Abstract: Learning structured and control-relevant latent representations remains a key challenge for world models. Recent JEPA-based world models learn action-

safetyarxiv-cs-cv

Latent Fact-Checking: Detecting Misinformation through Activation Engineering

arXiv:2608.06417v1 Announce Type: cross Abstract: The proliferation of misinformation online has driven demand for scalable detection systems. While most existing approaches rely on surface-level ling

Limit Points of Reflow with Minibatch Optimal Transport

arXiv:2608.07042v1 Announce Type: cross Abstract: Rectified flows, also called flow matching or stochastic interpolants, are generative models that learn a time-dependent vector field steering a proba

tutorialsarxiv-cs-lg

Lost in Interpolation: Why Predictive Feedback Fails in Diffusion Language Models

arXiv:2608.06529v1 Announce Type: new Abstract: Soft-masking accelerates the convergence of Masked Diffusion Language Models (MDLMs). Existing formulations build this blend with linear interpolation (

Machine Learning-Based Inter-Crystal Scatter Recovery for Ultra-High Resolution PET Imaging

arXiv:2608.07155v1 Announce Type: new Abstract: Inter-crystal scatter (ICS) events pose a significant challenge in ultrahigh- resolution positron emission tomography (UHR-PET), especially as detector

Measurements Automatically Extracted from Zero Echo Time MRI Using Deep Learning Image Segmentation and Geometric Modeling Agree with Expert Manual Readings

arXiv:2608.07368v1 Announce Type: cross Abstract: Computed tomography (CT) remains the reference for 3D osseous morphometry in femoroacetabular impingement (FAI) but requires ionizing radiation and ma

MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring

arXiv:2608.06713v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unre

safetyarxiv-cs-ai

Multi Codec Discrete Diffusion Model for Text Guided Speech Inpainting and Editing

arXiv:2608.06424v1 Announce Type: cross Abstract: Speech recordings often contain missing, corrupted, or incorrect regions that must be reconstructed or modified without re-synthesizing the entire utt

PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model

arXiv:2608.06794v1 Announce Type: new Abstract: While diffusion models have made significant progress in text-to-image tasks, they still exhibit limitations when directly optimizing downstream objecti

safetyarxiv-cs-cv

PHOENIX: Fine-Tuned SLM-Powered Autonomous Satellite Lifetime Extension via Predictive Self-Healing and Multi-Agent AI Recovery

arXiv:2608.07126v1 Announce Type: cross Abstract: Most CubeSats, small and low-cost satellites roughly the size of a shoebox, do not survive as long as they were designed to: a study of 178 missions f

Provable Training Data Identification for Large Language Models

arXiv:2510.09717v3 Announce Type: replace-cross Abstract: Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However,

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

arXiv:2608.06901v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidl

researcharxiv-cs-cv

Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT

arXiv:2512.12236v2 Announce Type: replace-cross Abstract: Sparse-view Computed Tomography (CT) reconstructs images from a limited number of X-ray projections to reduce radiation and scanning time, whi

researcharxiv-cs-cv

Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs

arXiv:2601.08527v3 Announce Type: replace-cross Abstract: We propose a novel method for sampling from unnormalized Boltzmann densities based on a probability flow ordinary differential equation (ODE)

Scenix: Sparse-View 3D Scene Reconstruction via Executable Scene Programs

arXiv:2608.07012v1 Announce Type: new Abstract: Synthesizing a structured and editable 3D indoor scene from a few uncalibrated RGB views requires more than generating high-quality individual assets: a

researcharxiv-cs-cv

Science Edge Evaluation: SEE the Missing Step Toward Real Scientific Discovery

arXiv:2608.06931v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly involved in scientific discovery, yet it remains unclear whether they can support complex real laboratory

Agents

SkillAligner: Treating Retrieved Skills as Adaptable Drafts at Execution Time

arXiv:2608.06880v1 Announce Type: new Abstract: General-purpose skills promise reusable procedural knowledge for language agents, yet semantic relevance does not guarantee execution utility: a retriev

agentsarxiv-cs-lg

Agents

SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control

arXiv:2608.06587v1 Announce Type: cross Abstract: Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control. However, lit

agentsarxiv-cs-ai

TOFD: Target-Oriented Feature Decoupling against Poisoning Attacks in Split Federated Learning

arXiv:2608.07274v1 Announce Type: cross Abstract: Split Federated Learning (SFL) facilitates privacy-preserving collaborative training with reduced client-side overhead. However, its split architectur

Transformers Struggle to Use Their Emergent World Models: Revisiting the Tower of Hanoi, and the Illusion of Thinking

arXiv:2608.07077v1 Announce Type: new Abstract: The Tower of Hanoi is a simple planning puzzle that in prior work has proven challenging for large reasoning models (LRMs). Current models solve the sta

Applications

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series

arXiv:2608.07333v1 Announce Type: new Abstract: Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edg

applicationsarxiv-cs-lg

WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN

arXiv:2608.07267v1 Announce Type: new Abstract: Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VLA) policies t

safetyarxiv-cs-ai

A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance

model-releasesarxiv-cs-lg

arXiv:2608.06246v1 Announce Type: new Abstract: Post-training adaptation has become central to modern machine learning practice and includes techniques such as retraining, fine-tuning, parameter-effic

Adaptive-WAM: Quality-Guided Early-Exit Planning from Intermediate Video-Diffusion Features

model-releasesarxiv-cs-ro

arXiv:2608.06008v1 Announce Type: new Abstract: Large video diffusion models provide rich spatiotemporal priors for autonomous driving, but existing world-action models often inherit the cost of itera

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

arXiv:2608.05928v1 Announce Type: new Abstract: Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individ

tutorialsarxiv-cs-lg

Causal Episodic Memory for Feedback-Driven Agent Repair

arXiv:2608.05906v1 Announce Type: new Abstract: LLM agents that repair failures often discard successful corrections, forcing later episodes to rediscover similar solutions. We study whether finalized

Cautious Context Steering for Language Model Personalization

arXiv:2608.05813v1 Announce Type: new Abstract: Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds. Existing meth

tutorialsarxiv-cs-ai

CogVis: Must Open-Vocabulary Change Detection Perceive the Scene Anew for Every Query?

arXiv:2608.06150v1 Announce Type: new Abstract: Earth-surface monitoring requires change detection models capable of recognizing arbitrary semantic categories. Open-Vocabulary Change Detection (OVCD)

local-aiarxiv-cs-ai

ConceptADapt: Concept-guided Adaptive Feature Reconstruction with Dynamic Attention for Few-Shot Industrial Anomaly Detection

arXiv:2608.05743v1 Announce Type: new Abstract: Few-shot industrial anomaly detection (FS-IAD) focuses on detecting and localizing visual defects in industrial inspection during the cold-start phase,

local-aiarxiv-cs-cv

Hardware

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

arXiv:2512.02551v4 Announce Type: replace-cross Abstract: In this paper, we propose CUDA-L2, a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimi

hardwarearxiv-cs-ai

d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation

arXiv:2601.07568v3 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random-orde

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data

model-releasesarxiv-cs-lg

arXiv:2608.05930v1 Announce Type: cross Abstract: The experience sampling method (ESM) is a longitudinal research design where participants report their thoughts, emotional states and behaviours multi

EmoWorld: A Decoupled Affective Field for Controllable Emotional Video Generation

arXiv:2608.06231v1 Announce Type: new Abstract: Emotion shapes how viewers interpret a scene, yet existing video generators entangle global atmosphere, affect-bearing semantic cues, and temporal progr

safetyarxiv-cs-cv

Energy-Guided Flow Matching

model-releasesarxiv-cs-cv

arXiv:2608.05811v1 Announce Type: new Abstract: Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dim

EpiBench: Can LLMs Understand Epitopes for Antibody Drug Discovery?

arXiv:2608.06022v1 Announce Type: new Abstract: Epitopes determine where antibodies bind antigens and shape downstream therapeutic properties such as functional blockade and escape resistance, making

EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver

arXiv:2602.11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from

Equipment-centric workpiece localization in near real-time using deep learning-based vision and event-driven finite state machines

arXiv:2608.05744v1 Announce Type: new Abstract: Continuous workpiece localization is essential for traceability and process coordination in hot forging, but direct tracking is unreliable because of ex

Evidential Rule Learning for Interpretable Classification with Abstention

arXiv:2608.05859v1 Announce Type: cross Abstract: Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence b

GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models

arXiv:2608.05948v1 Announce Type: new Abstract: Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit s

GST-Bench: Can VLMs Develop Global Spatial Awareness from Video?

model-releasesarxiv-cs-cv

arXiv:2608.05747v1 Announce Type: new Abstract: Spatial intelligence is fundamental to embodied agents, yet existing benchmarks focus on local spatial perception from single or few viewpoints, overloo

How to Recognize New Words: A Comparison Between Context Biasing Methods and Speech LLMs

arXiv:2608.05759v1 Announce Type: new Abstract: Recognizing new and rare words - named entities, acronyms, domain specific special words, and other items scarce in training data - remains a key challe

tutorialsarxiv-cs-cl

Hybrid Probabilistic Zonotopes for Identifiable and Refinable Predictive Uncertainty

arXiv:2608.05454v1 Announce Type: new Abstract: Probabilistic prediction heads in neural networks typically output either a Gaussian mixture or a single conformal region. Neither separates the distinc

JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment

model-releasesarxiv-cs-ro

arXiv:2608.05674v1 Announce Type: new Abstract: Robot data is scarce, so generalist policies need to learn from heterogeneous sources, including human egocentric video, simulation, and real robots, wh

Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations

model-releasesarxiv-cs-lg

arXiv:2608.06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Eq

Large Language Models Threaten Double-blind Review

arXiv:2608.05157v1 Announce Type: cross Abstract: Double blind peer review serves as the scientific community primary defense against status and affiliation bias. Its effectiveness rests on the assump

safetyarxiv-cs-ai