Search: “inference”

Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries

arXiv:2602.21707v2 Announce Type: replace-cross Abstract: State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about

researcharxiv-cs-cv

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Learning Transferable Topology Priors for Multi-Agent LLM Collaboration Across Domains

DGX agent

arXiv:2605.17359v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have shown strong potential for complex reasoning by coordinating specialized agents through struct

safetyarxiv-cs-cl

19 May 2026

LLMForge: Multi-Backend Hardware-Aware Neural Architecture Search with Infinite-Head Attention for Edge Language Models

arXiv:2605.17653v1 Announce Type: cross Abstract: Sub-billion-parameter Transformer language models are increasingly deployed on edge devices, where the privacy, latency, and operating-cost advantages

LPG: Balancing Efficiency and Policy Reasoning in Latent Policy Guardrails

arXiv:2605.17329v1 Announce Type: cross Abstract: Guardrails are a critical safety layer for modern AI systems, but their operating regime is changing. As LLMs are deployed as customized assistants, s

Agents

MA^{2}P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion

arXiv:2605.18572v1 Announce Type: new Abstract: Persuasive dialogue generation plays a vital role in decision-making, negotiation, counseling, and behavior change, yet it remains a challenging problem

agentsarxiv-cs-cl

Physics-Aligned Canonical Equivariant Fourier Neural Operator under Symmetry-Induced Shifts

arXiv:2605.18606v1 Announce Type: new Abstract: Neural operators approximate PDE solution maps, but they need not respect the symmetries of the governing equation. In out-of-distribution (OOD) regimes

safetyarxiv-cs-lg

Position: AI Evaluations Should be Grounded on a Theory of Capability

arXiv:2509.19590v2 Announce Type: replace Abstract: Evaluations of generative models are now ubiquitous, and their outcomes critically shape public and scientific expectations of AI's capabilities. Ye

Post-Trained MoE Can Skip Half Experts via Self-Distillation

arXiv:2605.18643v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its dynamic variant further reduces computation by a

Prefix-Adaptive Block Diffusion for Efficient Document Recognition

arXiv:2605.16861v1 Announce Type: cross Abstract: Block Diffusion Models (BDMs) support parallel generation, flexible-length output, and KV caching, making them promising for efficient document parsin

PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation

arXiv:2605.16612v1 Announce Type: new Abstract: Rapid identification of candidate materials with target properties has become a key task in materials science. Machine learning has emerged as an altern

Tutorials

RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs

arXiv:2602.05367v2 Announce Type: replace Abstract: Efficient deployment of large language models (LLMs) requires extreme quantization, forcing a critical trade-off between low-bit efficiency and perf

tutorialsarxiv-cs-ai

RAG-based EEG-to-Text Translation Using Deep Learning and LLMs

arXiv:2605.17503v1 Announce Type: new Abstract: The decoding of linguistic information from electroencephalography (EEG) signals remains an extremely challenging problem in brain-computer interface (B

Red-Bandit: Test-Time Adaptation for LLM Red-Teaming via Bandit-Guided LoRA Experts

model-releasesarxiv-cs-cl

arXiv:2510.07239v2 Announce Type: replace Abstract: Automated red-teaming has emerged as a scalable approach for auditing Large Language Models (LLMs) prior to deployment, yet existing approaches lack

RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards

arXiv:2509.21319v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) are the main RL paradigms used in

SD-Search: On-Policy Hindsight Self-Distillation for Search-Augmented Reasoning

arXiv:2605.18299v1 Announce Type: new Abstract: Search-augmented reasoning agents interleave internal reasoning with calls to an external retriever, and their performance relies on the quality of each

Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging

arXiv:2510.23641v2 Announce Type: replace-cross Abstract: Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deplo

local-aiarxiv-cs-ai

Speech-Guided Multimodal Learning for Vocal Tract Segmentation in Real-Time MRI

arXiv:2605.18466v1 Announce Type: new Abstract: Segmenting vocal tract articulators in real-time MRI (rtMRI) is a challenging dynamic image segmentation problem characterized by low contrast, rapid mo

local-aiarxiv-cs-cv

Stabilizing, Scaling & Enhancing MeanFlow for Large-scale Diffusion Distillation

model-releasesarxiv-cs-cv

arXiv:2605.17834v1 Announce Type: new Abstract: Diffusion models exhibit remarkable generative capability, but their high latency limits practical deployment. Many studies have attempted to reduce sam

Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions

arXiv:2507.05482v3 Announce Type: replace Abstract: Training-free diffusion guidance offers a flexible framework for leveraging off-the-shelf classifiers without additional training. Yet, current appr

researcharxiv-cs-lg

Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving

arXiv:2506.05442v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) offer a promising approach to end-to-end autonomous driving due to their human-like reasoning capabilities. Howe

SurgLQA: Scalable Long-Horizon Surgical Video Question Answering

model-releasesarxiv-cs-cv

arXiv:2605.17915v1 Announce Type: new Abstract: Surgical Video Question Answering (VideoQA) provides a promising paradigm for dynamic intraoperative interpretation, enabling real-time decision support

The Bayesian Geometry of Transformer Attention

arXiv:2512.22471v5 Announce Type: replace-cross Abstract: Transformers often appear to perform Bayesian reasoning in context, but verifying this rigorously has been impossible: natural data lack analy

Applications

Thermal-Only Crowd Counting with Deployment-Time Privacy Protection

arXiv:2605.17042v1 Announce Type: new Abstract: While RGB-Thermal crowd counting has shown promise, the paradigm faces critical limitations: RGB data raises privacy concerns in public surveillance, an

applicationsarxiv-cs-cv

Thinking with Patterns: Breaking the Perceptual Bottleneck in Visual Planning via Pattern Induction

arXiv:2605.16848v1 Announce Type: cross Abstract: Planning from raw visual input remains a significant challenge for current Vision-Language Models (VLMs), when the complexity of input is beyond their

local-aiarxiv-cs-ai

TouchMap-OR: Multi-View 3D Mapping of Hand-Surface Contacts

arXiv:2605.17638v1 Announce Type: new Abstract: Hand-surface interactions between clinicians, patients, and medical equipment play a central role in pathogen transmission during medical procedures. Ho

researcharxiv-cs-cv

Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering

arXiv:2605.18313v1 Announce Type: cross Abstract: Small vision-language models (2-8B) are well-suited for clin- ical deployment due to privacy constraints, limited connectivity, and low-latency requir

What Google I/O '26 means for developing agents on Google Cloud

model-releasesgoogle-cloud-ai

At Google I/O, we introduced a unified development toolkit featuring Antigravity 2.0 and the Managed Agents API, giving developers better ways to build locally and deploy securely to the cloud on a sh

World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems

arXiv:2605.16547v1 Announce Type: new Abstract: Semantic communication has emerged as a promising paradigm for enabling goal-oriented networking. However, most existing semantic communication solution

safetyarxiv-cs-lg

YawDD+: Frame-level Annotations for Accurate Yawn Prediction

arXiv:2512.11446v3 Announce Type: replace Abstract: Driver fatigue remains a leading cause of road accidents, responsible for 24% of crashes. While yawning serves as an early behavioral indicator of f

local-aiarxiv-cs-cv

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making

arXiv:2605.16054v1 Announce Type: cross Abstract: Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these appr

AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.15584v1 Announce Type: new Abstract: Vision-language models like CLIP have demonstrated remarkable zero-shot transfer capabilities. However, their susceptibility to imperceptible adversaria

CrystalBoltz: End-to-End Protein Structure Determination via Experiment-Guided Diffusion for X-Ray Crystallography

arXiv:2605.15564v1 Announce Type: new Abstract: Generative models trained on public databases of protein structures, most of which have been determined by X-ray crystallography, now provide powerful p

researcharxiv-cs-lg

DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer

arXiv:2605.15682v1 Announce Type: new Abstract: Large-scale pre-trained diffusion models have been extensively adopted for real-world image Super-Resolution because of their powerful generative priors

local-aiarxiv-cs-cv

Enabling Adversarial Robustness in AI Models through Kubeflow MLOps

arXiv:2605.15249v1 Announce Type: cross Abstract: AI models are increasingly deployed in cloud-native environments to support scalable and automated services. However, while platforms such as Kubernet

researcharxiv-cs-lg

Feedback World Model Enables Precise Guidance of Diffusion Policy

arXiv:2605.15705v1 Announce Type: cross Abstract: World models aim to improve robotic decision making by predicting the consequences of actions. However, in practice, their predictions often become un

Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models

model-releasesarxiv-cs-lg

arXiv:2509.12266v2 Announce Type: replace-cross Abstract: We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core c

GQLA: Group-Query Latent Attention for Hardware-Adaptive Large Language Model Decoding

arXiv:2605.15250v1 Announce Type: cross Abstract: Multi-head Latent Attention (MLA), the attention used in DeepSeek-V2/V3, jointly compresses keys and values into a low-rank latent and matches the H10

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations

model-releasesarxiv-cs-lg

arXiv:2605.15216v1 Announce Type: cross Abstract: Always-on AI applications, from environmental sensors to biomedical implants, require ultra-low power consumption. Analog circuits offer a path to sub

NoiseShift: Resolution-Aware Noise Recalibration for Better Low-Resolution Image Generation

arXiv:2510.02307v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models often degrade when sampled at resolutions outside the final training resolution set. Prior work has largely emp

local-aiarxiv-cs-ai

Offline Semantic Guidance for Efficient Vision-Language-Action Policy Distillation

arXiv:2605.16241v1 Announce Type: cross Abstract: Billion-parameter Vision-Language-Action (VLA) policies have recently shown impressive performance in robotic manipulation, yet their size and inferen

SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation

model-releasesarxiv-cs-cl

arXiv:2605.16117v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities across diverse NLP applications, such as translation, text generation, and question a

T2T-LA: A Topology-to-Topology LLM Agent for Graph Learning with Neither Feature Access nor Task Knowledge

model-releasesarxiv-cs-lg

arXiv:2512.08964v4 Announce Type: replace Abstract: Graph learning aims to convert data into graph representations, which are fundamental to many problems in machine learning for CAD, where circuits,

A plug-and-play generative framework for multi-satellite precipitation estimation

arXiv:2605.14426v1 Announce Type: cross Abstract: Reliable precipitation monitoring is essential for disaster risk reduction, water resources management, and agricultural decision-making. Multi-source

Action-Inspired Generative Models

arXiv:2605.14631v1 Announce Type: cross Abstract: We introduce Action-Inspired Generative Models (AGMs), a dual-network generative framework motivated by the observation that existing bridge-matching

AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving

arXiv:2603.14851v3 Announce Type: replace Abstract: Integrating vision-language models (VLMs) into end-to-end (E2E) autonomous driving (AD) systems has shown promise in improving scene understanding.

safetyarxiv-cs-cv

Distribution Corrected Offline Data Distillation for Large Language Models

arXiv:2605.14071v1 Announce Type: new Abstract: Distilling reasoning traces from strong large language models into smaller ones is a promising route to improve intelligence in resource-constrained set

safetyarxiv-cs-cl

Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale

arXiv:2605.14833v1 Announce Type: new Abstract: Current language model systems remain fundamentally stateless across sessions, limiting their ability to personalize interactions over time. While retri

Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding

arXiv:2605.14305v1 Announce Type: new Abstract: Discrete diffusion language models improve generation efficiency through parallel token prediction, but standard X_0 prediction methods introduce factor

researcharxiv-cs-cl