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Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory

arXiv:2512.07287v3 Announce Type: replace-cross Abstract: As intents unfold and environments change, multi-turn agents face continuously shifting decision contexts. Although reusing past experience is

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Agents

Experience Graphs: The Data Foundation for Self-Improving Agents

DGX agent

arXiv:2606.29823v1 Announce Type: cross Abstract: The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that l

agentsarxiv-cs-ai

30 Jun 2026

Expert Evaluation of Clinical AI Tools on Real Point-of-Care Clinical Queries

arXiv:2606.28960v1 Announce Type: new Abstract: Physicians now pose millions of clinical questions to AI tools each week, yet these tools are evaluated largely on hypothetical or exam-style questions,

Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration

arXiv:2509.21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. W

safetyarxiv-cs-lg

Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors

model-releasesarxiv-cs-cv

arXiv:2606.29963v1 Announce Type: new Abstract: The structural vulnerabilities of point cloud-based 3D object detectors remain poorly understood. Prior work has studied adversarial robustness primaril

Explaining Attention with Program Synthesis

arXiv:2606.19317v2 Announce Type: replace-cross Abstract: A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descrip

Exploiting Local Flatness for Efficient Out-of-Distribution Detection

arXiv:2606.29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particu

Exploiting Vision Encoder Vulnerabilities for Universal Adversarial Perturbations on Large Vision-Language Models

arXiv:2412.08108v3 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance on multimodal tasks but remain highly vulnerable to small adversaria

researcharxiv-cs-cl

Exploration and Online Transfer with Behavioral Foundation Models

arXiv:2606.29980v1 Announce Type: new Abstract: Zero-shot Transfer in Reinforcement Learning (RL) aims to train an agent that can generate optimal policies for any reward function, without additional

ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving

arXiv:2604.02714v2 Announce Type: replace Abstract: End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies t

safetyarxiv-cs-cv

Applications

Exploring Differences Between Tabular Enterprise Data and Public Benchmarks

arXiv:2606.30452v1 Announce Type: new Abstract: Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks. Yet, little is

applicationsarxiv-cs-lg

Exploring Motivations for Algorithm Mention in the Domain of Natural Language Processing: A Deep Learning Approach

arXiv:2606.29859v1 Announce Type: cross Abstract: With the rise of data-intensive science, algorithms have become central to scientific research. In academic papers, algorithms are mentioned for diffe

Exploring the Cryptographic Limits of Transformer Networks

arXiv:2606.29389v1 Announce Type: cross Abstract: In recent work it has been shown that colluding AI agents can use steganographic methods to exchange malicious information. Whether a transformer can

Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding

model-releasesarxiv-cs-lg

arXiv:2606.28655v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Inter

Applications

Exploring the Value of Diverse LLM Explanations in Introductory Programming

arXiv:2606.28882v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown the potential to generate code explanations that surpass those of peers in quality, offering promising opportu

applicationsarxiv-cs-ai

Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction

arXiv:2606.28457v1 Announce Type: new Abstract: Natural language processing (NLP) applications need large and rich amount of linguistic knowledge. Furthermore, electronic language sources such as dict

researcharxiv-cs-cl

Extrapolating from Regularised Solutions for Solving Ill-Conditioned Linear Systems in Machine Learning

arXiv:2606.30328v1 Announce Type: cross Abstract: Rapid prototyping of algorithms is a critical step in modern machine learning. Most algorithms exploit linear algebra, creating a need for lightweight

Tutorials

EyeMVP: OCT-Informed Fundus Representation Learning via Paired CFP--OCT Pretraining

arXiv:2606.15129v2 Announce Type: replace-cross Abstract: Color fundus photography (CFP) is the mainstay of large-scale retinal screening, but its diagnostic capacity is limited by the lack of depth-r

tutorialsarxiv-cs-ai

FacePlex: Full-Duplex Joint Speech-Facial Motion Generation for Conversational Avatars

arXiv:2606.30145v1 Announce Type: new Abstract: Natural face-to-face conversation requires real-time speech generation together with synchronized facial motion. Existing systems only partially address

Factorizable Normalizing Flows for parameter-dependent density morphing

model-releasesarxiv-cs-lg

arXiv:2606.30489v1 Announce Type: cross Abstract: Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeli

FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control

arXiv:2606.28476v1 Announce Type: new Abstract: High-precision humanoid control is limited by target-domain dynamics mismatch, where the same control objective can induce different realized motions un

safetyarxiv-cs-ro

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

arXiv:2606.29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent w

FAIL: Flow Matching Adversarial Imitation Learning for Image Generation

arXiv:2602.12155v2 Announce Type: replace Abstract: Post-training of flow matching models-aligning the output distribution with a high-quality target-is mathematically equivalent to imitation learning

safetyarxiv-cs-cv

Fairness Attacks on Recommender Systems

arXiv:2606.29064v1 Announce Type: cross Abstract: The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications. Although existing works ha

FalconTrack: Photorealistic Auto-Labeled Perception and Physics-Aware Vision-Based Aerial Tracking

arXiv:2606.29783v1 Announce Type: cross Abstract: Vision-based aerial tracking is critical in GPS-denied environments. Reliable perception for tracking depends on large-scale labeled data, yet most ph

Falsifying Discriminant Validity of Predictive Algorithms

arXiv:2601.17146v2 Announce Type: replace-cross Abstract: Empirical investigations into unintended model behavior often show that the algorithm is predicting another outcome than what was intended. Th

Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications

arXiv:2606.28607v1 Announce Type: cross Abstract: This work tackles the challenge of enhancing low-resolution LiDAR sensors for SLAM applications through a novel Deep Unrolling-based Super-Resolution

Fast Enough to Act: Spatio-Temporal Visual Token Merging for Low-Latency Robotic VLMs and VLAs

arXiv:2606.29350v1 Announce Type: cross Abstract: Vision-language models and vision-language action models endow the robot with unprecedented capabilities. However, the input of video and high-resolut

Fast Equivariant Imaging: Accelerating Unsupervised Learning and Model Adaptation via Inexact Splitting

arXiv:2507.06764v5 Announce Type: replace-cross Abstract: In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging

researcharxiv-cs-cv

Fast Numbers, Slow Language: Bridging Quantitative and Qualitative Earnings Signals

arXiv:2606.29734v1 Announce Type: new Abstract: Earnings announcements release two types of information sequentially: quantitative surprise (numeric earnings-per-share (EPS)/revenue versus analyst est

researcharxiv-cs-cl

Fast Wireless Foundation Models with Early-Exits

arXiv:2606.29640v1 Announce Type: cross Abstract: While wireless foundation models (FMs) are demonstrating strong potential to enable AI-Native 6G networks, their high computational cost remains a cri

FastPano3D: Feed-Forward Indoor Panoramic 3D Reconstruction from a Single Image

model-releasesarxiv-cs-cv

arXiv:2606.30352v1 Announce Type: new Abstract: Recent advances in 3D scene reconstruction have highlighted the intricate trade-offs among rendering quality, inference efficiency, and data dependency.

Tutorials

Faults in Our Formal Benchmarking: Dataset Defects and Evaluation Failures in Lean Theorem Proving

arXiv:2606.29493v1 Announce Type: new Abstract: Benchmarks for LLM-assisted theorem proving in Lean are often treated as intrinsically reliable because every solved instance comes with a machine-check

tutorialsarxiv-cs-ai

Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization

arXiv:2505.22578v2 Announce Type: replace Abstract: The optimization of neural networks under weight decay remains poorly understood from a theoretical standpoint. While weight decay is standard pract

FD^2: A Dedicated Framework for Fine-Grained Dataset Distillation

arXiv:2603.25144v2 Announce Type: replace-cross Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong

FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On

arXiv:2606.29319v1 Announce Type: new Abstract: Image-based Virtual Try-On (IVTON) has greatly advanced through diffusion models, yet existing methods require many sampling steps and depend on masks w

safetyarxiv-cs-cv

Feature-level Interaction Explanations in Multimodal Transformers

arXiv:2603.13326v2 Announce Type: replace-cross Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multim

Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks

arXiv:2405.00742v2 Announce Type: replace-cross Abstract: Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV

Federated Learning with Energy-Based Structured Probabilistic Inference

arXiv:2606.30161v1 Announce Type: cross Abstract: Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneo

FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibration

arXiv:2606.28654v1 Announce Type: cross Abstract: Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likel

fev-bench: A Realistic Benchmark for Time Series Forecasting

model-releasesarxiv-cs-lg

arXiv:2509.26468v3 Announce Type: replace Abstract: Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained

Local Ai

Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations

arXiv:2606.28391v1 Announce Type: cross Abstract: The wide use of Convolutional Neural Networks (CNN) in numerous domains and real-world classification applications is justified by their high precisio

local-aiarxiv-cs-ai

Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation

arXiv:2606.30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps

arXiv:2606.29110v1 Announce Type: new Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluat

FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images

arXiv:2606.30347v1 Announce Type: cross Abstract: We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more r

Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval

arXiv:2606.30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query. Embedding a

Applications

Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa

arXiv:2606.28404v1 Announce Type: cross Abstract: Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debates treat compute pri

applicationsarxiv-cs-ai

Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B

arXiv:2606.28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text gen