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arXiv:2605.19330v1 Announce Type: new Abstract: LLM agents organize behavior through skills - structured natural-language specifications governing how an agent reasons, retrieves, and responds. Unlike

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Not All Tokens Are Worth Caching: Learning Semantic-Aware Eviction for LLM Prefix Caches

DGX agent

arXiv:2605.18825v1 Announce Type: new Abstract: Prefix caching is a key optimization in Large Language Model (LLM) serving, reusing attention Key-Value (KV) states across requests with shared prompt p

model-releasesarxiv-cs-lg

20 May 2026

Applications

Performance Monitoring of Proton Exchange Membrane Water Electrolyzer by Transformers-Based Machine Learning Model

arXiv:2605.19107v1 Announce Type: new Abstract: Green hydrogen plays an essential role in decarbonization, with capacity projected to scale to 560 GW by 2030 (vs. 1.39 GW in 2023) in net-zero settings

applicationsarxiv-cs-lg

Phase-Aware Mixture of Experts for Agentic Reinforcement Learning

arXiv:2602.17038v3 Announce Type: replace Abstract: Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a single

Hardware

PiKV: KV Cache Management System for Mixture of Experts

arXiv:2508.06526v3 Announce Type: replace-cross Abstract: As large-scale language models continue to scale up in both size and context length, the memory and communication cost of key-value (KV) cache

hardwarearxiv-cs-ai

Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming

arXiv:2605.19052v1 Announce Type: cross Abstract: Lagrangian Relaxation (LR) is a powerful technique for solving large-scale Mixed Integer Linear Programming (MILP), particularly those with decomposab

researcharxiv-cs-lg

SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

arXiv:2605.19587v1 Announce Type: new Abstract: Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation-based policy evaluation, where a useful scene must specify not only w

Tight Regret Bounds for Fixed-Price Bilateral Trade

arXiv:2504.04349v3 Announce Type: replace-cross Abstract: We examine fixed-price mechanisms in bilateral trade through the lens of regret minimization. Our main results are twofold. (i) For independen

researcharxiv-cs-lg

Unified Deployment-Aware Evaluation of Open Reasoning Language Models

model-releasesarxiv-cs-cl

arXiv:2604.07035v2 Announce Type: replace Abstract: Open reasoning language models are often compared under mixed sample sizes, partially standardized prompts, and accuracy-centered summaries, which m

What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code

arXiv:2605.19762v1 Announce Type: new Abstract: Code has become a standard component of modern foundation language model (LM) training, yet its role beyond programming remains unclear. We revisit the

A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows

arXiv:2605.17808v1 Announce Type: new Abstract: We develop a unified theoretical framework for data-free one-step sampling from unnormalized target distributions based on Wasserstein gradient flows. F

researcharxiv-cs-lg

Beyond Linear Superposition: Discovering Climate Features in AI Weather Models with KAN-SAE

arXiv:2605.17493v1 Announce Type: cross Abstract: Deep learning weather prediction models achieve remarkable predictive skill yet remain largely opaque: we know little about how they represent physica

Body-Grounded Perspective Formation and Conative Attunement in Artificial Agents

arXiv:2605.16728v1 Announce Type: new Abstract: This paper proposes a minimal architecture for body-grounded perspective formation in artificial agents. Extending prior work, the model introduces an i

Capturing LLM Capabilities via Evidence-Calibrated Query Clustering

arXiv:2605.17110v1 Announce Type: new Abstract: Query clustering organizes queries into groups that reflect shared latent capability demands, enabling capability-aware LLM evaluation. Existing cluster

Coarse Semantic Injection for LLM-Conditioned Structured Indoor Prediction

arXiv:2605.16832v1 Announce Type: new Abstract: Large language models (LLMs) have recently been used as structured decoders for indoor understanding from 3D point-token inputs. However, point cloud en

researcharxiv-cs-cv

Confidence Geometry Reveals Trace-Level Correctness in Large Language Model Reasoning

arXiv:2605.16824v1 Announce Type: cross Abstract: Large language models (LLMs) generate not only reasoning text, but also token-level confidence trajectories that record how uncertainty evolves during

researcharxiv-cs-cl

Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting

arXiv:2508.04227v2 Announce Type: replace Abstract: Vision-language models (VLMs) and the recent surge of Multimodal Large Language Models (MLLMs) have revolutionized artificial intelligence with unpr

Agents

DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition

arXiv:2605.16441v1 Announce Type: cross Abstract: Beat-level Electrocardiography (ECG) arrhythmia detection aims to assign an arrhythmia class to each beat in a recording, yet many existing systems tr

agentsarxiv-cs-ai

Distilling Tabular Foundation Models for Structured Health Data

arXiv:2605.18702v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) achieve strong performance on health datasets, but their inference cost and infrastructure requirements limit practic

Domain Transfer Becomes Identifiable via a Single Alignment

arXiv:2605.17918v1 Announce Type: cross Abstract: Domain transfer (DT) maps source to target distributions and supports tasks such as unsupervised image-to-image translation, single-cell analysis, and

Tutorials

Encoding Robust Topological Signatures for Hyperdimensional Computing

arXiv:2605.16785v1 Announce Type: cross Abstract: Hyperdimensional (HD) computing offers an attractive alternative to deep networks for edge learning due to its simplicity, fast prototype-based infere

tutorialsarxiv-cs-ai

EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers

arXiv:2605.16745v1 Announce Type: new Abstract: This paper addresses the challenge of integrating 3D meshes as a native modality within Multimodal Large Language Models (MLLMs). Diffusion-based large

researcharxiv-cs-cv

Experimentally validated quantum-secure federated learning over a multi-user quantum network

model-releasesarxiv-cs-ai

arXiv:2501.12709v2 Announce Type: replace-cross Abstract: Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum fe

GRaD-Nav++: Vision-Language Model Enabled Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics

model-releasesarxiv-cs-ro

arXiv:2506.14009v2 Announce Type: replace Abstract: Autonomous drones capable of interpreting and executing high-level language instructions in unstructured environments remain a long-standing goal. Y

How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning

arXiv:2605.16591v1 Announce Type: cross Abstract: In-context learning (ICL) excels at new tasks from minimal examples, yet we still lack a mechanistic explanation of how few-shot prompts shape a model

Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design

arXiv:2605.17137v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into evolutionary frameworks has established a new paradigm for automated heuristic discovery. Despite t

Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation

model-releasesarxiv-cs-lg

arXiv:2605.18704v1 Announce Type: cross Abstract: Unmanned Aerial Vehicles in dynamic environments face telemetry outages, structural vibrations, and regime-dependent noise that invalidate the station

Learning Unbiased Permutations via Flow Matching

arXiv:2605.16755v1 Announce Type: cross Abstract: Learning permutations is fundamental to sorting, ranking, and matching, but existing differentiable methods based on entropy-regularized Sinkhorn prod

Tutorials

Leveraging Latent Visual Reasoning in Silence

arXiv:2605.18641v1 Announce Type: new Abstract: Latent visual reasoning involves visual evidence more directly in multimodal reasoning by inserting continuous latent tokens before textual generation.

tutorialsarxiv-cs-cv

Tutorials

LLM-Based Intelligent Notification Composition: From Static Personalization to Context-Aware Persuasive Messaging

arXiv:2605.16264v1 Announce Type: cross Abstract: Push notifications remain among the most direct channels through which digital platforms engage users, yet existing approaches have invested heavily i

tutorialsarxiv-cs-cl

Memory-Guided Tree Search with Cross-Branch Knowledge Transfer for LLM Solver Synthesis

arXiv:2605.17539v1 Announce Type: new Abstract: Combinatorial optimization (CO) underlies decision-making from logistics to chip design, where infeasible solutions are operationally unusable and small

PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics

arXiv:2605.18303v1 Announce Type: cross Abstract: World models built on recurrent state space architectures enable efficient latent imagination, yet remain physically unstructured, producing dynamics

Quantum Sidecar Architectures for Hybrid AI Training and Inference: Stateful Protected Registers, Stateless Reset-and-Reprepare Circuits and Quantum Weight-State Outlook

arXiv:2605.18031v1 Announce Type: cross Abstract: We propose a quantum sidecar architecture family for future hybrid AI training and inference. The central idea is not to store an entire Transformer i

Recall Isn't Enough: Bounding Commitments in Personalized Language Systems

arXiv:2605.16712v1 Announce Type: new Abstract: Long-context and memory systems usually treat personalization as a recall problem. In practice, many failures occur later, when a system commits: it tur

Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling

arXiv:2605.18599v1 Announce Type: new Abstract: Transformer-based models have advanced feedforward novel view synthesis (NVS). Current architectures such as GS-LRM and LVSM mix semantic information (e

safetyarxiv-cs-cv

SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models

arXiv:2605.17985v1 Announce Type: cross Abstract: We propose a new method for compressing physics foundation models (PFMs) which is a new trend in AI for Science. While model compression is essential

Scenario Generation in Roundabouts with Adjustable Interaction Intensity

arXiv:2605.18026v1 Announce Type: new Abstract: Roundabouts, characterized by frequent merging and yielding interactions, remain a safety-critical corner case for the development and testing of intell

safetyarxiv-cs-ro

SparseSAM: Structured Sparsification of Activations in Segment Anything Models

arXiv:2605.17633v1 Announce Type: cross Abstract: The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory.

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models

arXiv:2605.17577v1 Announce Type: new Abstract: Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to impercepti

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

The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level

arXiv:2604.02178v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures have become the dominant choice for scaling Large Language Models (LLMs), activating only a subset of p

Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors

arXiv:2605.18611v1 Announce Type: new Abstract: We propose a unified reinforcement learning framework that enables a single policy to perform walking, running, and fall recovery on the Unitree G1 huma

safetyarxiv-cs-ro

Applications

UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities

arXiv:2504.20734v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external kn

applicationsarxiv-cs-ai

When Accuracy Is Not Enough: Uncertainty Collapse between Noisy Label Learning and Out-of-Distribution Detection

arXiv:2605.17795v1 Announce Type: cross Abstract: Learning with noisy labels (LNL) is typically benchmarked by closed-set classification accuracy, yet deployment often requires classifiers to reject o

A Reproducible and Physically Feasible Dynamic Parameter Identification Framework for a Low-Cost Robot Arm

model-releasesarxiv-cs-ro

arXiv:2605.15949v1 Announce Type: new Abstract: This paper presents a reproducible and physically feasible dynamic parameter identification framework for CRANE-X7, a low-cost robot arm driven by modul

Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging

arXiv:2505.21698v3 Announce Type: replace Abstract: Vision-language foundation models achieve promising performance in natural image classification, yet their direct application to medical imaging is

Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG

arXiv:2602.23410v3 Announce Type: replace-cross Abstract: Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited

Applications

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts

arXiv:2605.15888v1 Announce Type: cross Abstract: Heterogeneous Graph Prompt Learning (HGPL)has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation m

applicationsarxiv-cs-ai