All entries

arXiv:2510.15218v3 Announce Type: replace Abstract: The stacking ensemble combining RF, LightGBM, and DNN performed well on internal test sets, exhibiting an NPV greater than 99.9% even with substanti

applicationsarxiv-cs-lg

ENTIRE: Learning-based Volume Rendering Time Prediction

DGX agent

arXiv:2501.12119v3 Announce Type: replace-cross Abstract: We introduce ENTIRE, a novel deep learning-based approach for fast and accurate volume rendering time prediction. Predicting rendering time is

model-releasesarxiv-cs-cv

21 Apr 2026

Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs

arXiv:2510.00861v2 Announce Type: replace Abstract: While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limite

Erasing Thousands of Concepts: Towards Scalable and Practical Concept Erasure for Text-to-Image Diffusion Models

arXiv:2604.16481v1 Announce Type: new Abstract: Large-scale text-to-image (T2I) diffusion models deliver remarkable visual fidelity but pose safety risks due to their capacity to reproduce undesirable

safetyarxiv-cs-cv

Error as Signal: Stiffness-Aware Diffusion Sampling via Embedded Runge-Kutta Guidance

model-releasesarxiv-cs-cv

arXiv:2603.03692v2 Announce Type: replace Abstract: Classifier-Free Guidance (CFG) has established the foundation for guidance mechanisms in diffusion models, showing that well-designed guidance proxi

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

arXiv:2410.04509v3 Announce Type: replace Abstract: As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particular

ESsEN: Training Compact Discriminative Vision-Language Transformers in a Low-Resource Setting

arXiv:2604.18452v1 Announce Type: cross Abstract: Vision-language modeling is rapidly increasing in popularity with an ever expanding list of available models. In most cases, these vision-language mod

Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings

arXiv:2505.15353v3 Announce Type: replace Abstract: Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterog

Estimating Commonsense Plausibility through Semantic Shifts

arXiv:2502.13464v2 Announce Type: replace Abstract: Commonsense plausibility estimation is critical for evaluating language models (LMs), yet existing generative approaches--reliant on likelihoods or

Evalet: Evaluating Large Language Models through Functional Fragmentation

arXiv:2509.11206v4 Announce Type: replace-cross Abstract: Practitioners increasingly rely on Large Language Models (LLMs) to evaluate generative AI outputs through 'LLM-as-a-Judge' approaches. However

Tutorials

Evaluating Adaptive Personalization of Educational Readings with Simulated Learners

arXiv:2604.16744v1 Announce Type: new Abstract: We present a framework for evaluating adaptive personalization of educational reading materials with theory-grounded simulated learners. The system buil

tutorialsarxiv-cs-cl

Evaluating Multimodal LLMs for Inpatient Diagnosis: Real-World Performance, Safety, and Cost Across Ten Frontier Models

arXiv:2604.16980v1 Announce Type: new Abstract: Background: Large language models (LLMs) are increasingly proposed for diagnostic support, but few evaluations use real-world multimodal inpatient data,

Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic

arXiv:2604.16575v1 Announce Type: new Abstract: Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks, yet most studies assum

Evaluating the Impact of Verbal Multiword Expressions on Machine Translation

arXiv:2508.17458v2 Announce Type: replace Abstract: Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component

Evaluating Tool-Using Language Agents: Judge Reliability, Propagation Cascades, and Runtime Mitigation in AgentProp-Bench

arXiv:2604.16706v1 Announce Type: cross Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, but this assumption has rarely been validated a

EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations

arXiv:2604.18320v1 Announce Type: new Abstract: Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degr

safetyarxiv-cs-cv

Agents

Every docs team I've been on ends up punting on the same project: making code samples in docs testable in CI. We know it's important but the…

Every docs team I've been on ends up punting on the same project: making code samples in docs testable in CI. We know it's important but the implementation cost is too great. Skills + deep agents fina

agentsharrison-chase--x

Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning

arXiv:2601.10306v2 Announce Type: replace-cross Abstract: While Reinforcement Learning (RL) has advanced LLM reasoning, applying it to long-context scenarios is hindered by sparsity of outcome rewards

EvoComp: Learning Visual Token Compression for Multimodal Large Language Models via Semantic-Guided Evolutionary Labeling

arXiv:2604.17087v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language understanding tasks, yet their inference efficie

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning

arXiv:2508.07809v5 Announce Type: replace Abstract: Reinforcement learning with verifiable reward (RLVR) has become a promising paradigm for post-training large language models (LLMs) to improve their

Evolutionary Negative Module Pruning for Better LoRA Merging

arXiv:2604.17753v1 Announce Type: cross Abstract: Merging multiple Low-Rank Adaptation (LoRA) experts into a single backbone is a promising approach for efficient multi-task deployment. While existing

ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks

arXiv:2604.18052v1 Announce Type: cross Abstract: Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic. Although opaque 'black-box' models can achieve high accur

Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators

arXiv:2602.16090v2 Announce Type: replace-cross Abstract: The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and sl

Hardware

Excited to partner with the SpaceX team to scale up Composer. A meaningful step on our path to build the best place to code with AI.

Excited to partner with the SpaceX team to scale up Composer. A meaningful step on our path to build the best place to code with AI. SpaceXAI and @cursor_ai are now working closely together to create

hardwareelon-musk--x

Tutorials

Expert-Annotated Embryo Image Dataset with Natural Language Descriptions for Evidence-Based Patient Communication in IVF

arXiv:2604.16528v1 Announce Type: new Abstract: Embryo selection is one of multiple crucial steps in in-vitro fertilization, commonly based on morphological assessment by clinical embryologists. Altho

tutorialsarxiv-cs-cv

Applications

Explaining Uncertainty in Multiple Sclerosis Cortical Lesion Segmentation Beyond Prediction Errors

arXiv:2504.04814v3 Announce Type: replace-cross Abstract: Trustworthy artificial intelligence (AI) is essential in healthcare, particularly for high-stakes tasks like medical image segmentation. Expla

applicationsarxiv-cs-cv

Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution

arXiv:2512.11108v3 Announce Type: replace Abstract: Good quality explanations strengthen the understanding of language models and data. Feature attribution methods, such as Integrated Gradient, are a

Exploring Boundary-Aware Spatial-Frequency Fusion for Camouflaged Object Detection

model-releasesarxiv-cs-cv

arXiv:2604.17879v1 Announce Type: new Abstract: Camouflaged Object Detection is challenging due to the high degree of similarity between camouflaged objects and their surrounding backgrounds. Current

Exploring Concreteness Through a Figurative Lens

arXiv:2604.18296v1 Announce Type: new Abstract: Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor

Exploring Mutual Cross-Modal Attention for Context-Aware Human Affordance Generation

arXiv:2502.13637v2 Announce Type: replace Abstract: Human affordance learning investigates contextually relevant novel pose prediction such that the estimated pose represents a valid human action with

Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms

arXiv:2604.16757v1 Announce Type: new Abstract: The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and

Expressive yet Efficient Feature Expansion with Adaptive Cross-Hadamard Products

arXiv:2505.22226v2 Announce Type: replace Abstract: Recent theoretical advances reveal that the Hadamard product induces nonlinear representations and implicit high-dimensional mappings for the field

Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation

arXiv:2604.18168v1 Announce Type: new Abstract: Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existin

Extraction of informative statistical features in the problem of forecasting time series generated by It{o}-type processes

arXiv:2604.16865v1 Announce Type: cross Abstract: In this paper, we consider the problem of extraction of most informative features from time series that are regarded as observed values of stochastic

FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program

arXiv:2604.16450v1 Announce Type: cross Abstract: Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess de

safetyarxiv-cs-lg

Fairness Constraints in High-Dimensional Generalized Linear Models

arXiv:2604.16610v1 Announce Type: cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability. Conventional fairness

safetyarxiv-cs-lg

FairNVT: Improving Fairness via Noise Injection in Vision Transformers

arXiv:2604.16780v1 Announce Type: new Abstract: This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves both representation and predictio

safetyarxiv-cs-cv

'Faithful to What?' On the Limits of Fidelity-Based Explanations

arXiv:2506.12176v5 Announce Type: replace Abstract: In explainable AI, surrogate models are commonly evaluated by their fidelity to a neural network's predictions. Fidelity, however, measures alignmen

safetyarxiv-cs-lg

Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence

arXiv:2601.11886v2 Announce Type: replace Abstract: In high-stakes domains like medicine, it may be generally desirable for models to faithfully adhere to the context provided. But what happens if the

FaithLens: Detecting and Explaining Faithfulness Hallucination

arXiv:2512.20182v3 Announce Type: replace Abstract: Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retri

Falcon flies every few days. You can see launches in person from Florida or California.

model-releaseselon-musk--x

SpaceX's Falcon rockets conduct frequent launches occurring every few days, with public viewing opportunities available at launch facilities in Florida and California. This statement reflects SpaceX's

Local Ai

Famegrid Checkpoint ZIB

local-air-stablediffusion

Famegrid Checkpoint ZIB is built on Z-Image Base and produces modern, social-media-style photography with a natural look, designed to create polished yet believable images with good composition and li

Fast Online 3D Multi-Camera Multi-Object Tracking and Pose Estimation

arXiv:2604.16522v1 Announce Type: new Abstract: This paper proposes a fast and online method for jointly performing 3D multi-object tracking and pose estimation using multiple monocular cameras. Our a

Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD

arXiv:2604.18491v1 Announce Type: new Abstract: Computational Fluid Dynamics (CFD) is central to race-car aerodynamic development, yet its cost -- tens of thousands of core-hours per high-fidelity eva

Applications

Federated Rule Ensemble Method in Medical Data

arXiv:2604.17956v1 Announce Type: new Abstract: Machine learning has become integral to medical research and is increasingly applied in clinical settings to support diagnosis and decision-making; howe

applicationsarxiv-cs-lg

Agents

Federation over Text: Insight Sharing for Multi-Agent Reasoning

arXiv:2604.16778v1 Announce Type: new Abstract: LLM-powered agents often reason from scratch when presented with a new problem instance and lack automatic mechanisms to transfer learned skills to othe

agentsarxiv-cs-lg

FedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Prediction

arXiv:2604.16612v1 Announce Type: new Abstract: Traffic prediction plays a central role in intelligent transportation systems (ITS) by supporting real-time decision-making, congestion management, and

FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling

arXiv:2604.16574v1 Announce Type: new Abstract: Federated Learning (FL) faces challenges from client data heterogeneity and resource-constrained mobile devices, which can degrade model accuracy. Perso