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arXiv:2606.26758v1 Announce Type: new Abstract: High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their developm

hardwarearxiv-cs-ai

Einstein World Models

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arXiv:2606.26969v1 Announce Type: new Abstract: Does intelligence require the ability to reason about phenomena beyond direct experience? It is natural to suspect that some complex thought cannot be c

researcharxiv-cs-ai

26 Jun 2026

EMA-FS: Accelerating GBDT Training via Gain-Informed Feature Screening

arXiv:2606.26337v1 Announce Type: new Abstract: Gradient Boosted Decision Trees (GBDT), exemplified by LightGBM, spend a dominant fraction of training time -- typically 65-70% -- constructing per-feat

Embarrassingly Simple Self-Distillation Improves Code Generation

model-releasesarxiv-cs-cl

arXiv:2604.01193v2 Announce Type: replace Abstract: Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement le

Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees

arXiv:2606.26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require

researcharxiv-cs-lg

Empirical Software Engineering TerraProbe: A Layered-Oracle Framework for Detecting Deceptive Fixes in LLM-Assisted Terraform

arXiv:2606.26590v1 Announce Type: new Abstract: Security misconfigurations in Terraform Infrastructure-as-Code are a growing risk in cloud deployments, and large language models are increasingly used

Agents

Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning

arXiv:2606.27330v1 Announce Type: cross Abstract: Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks in

agentsarxiv-cs-ai

Enabling self-supervised learned primal dual with Noise2Inverse

arXiv:2606.26991v1 Announce Type: cross Abstract: X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are no

researcharxiv-cs-lg

Local Ai

EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images

arXiv:2508.17916v2 Announce Type: replace Abstract: Depth estimation is a foundational component for 3D reconstruction in minimally invasive endoscopic surgeries. However, existing monocular depth est

local-aiarxiv-cs-cv

EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting

model-releasesarxiv-cs-ai

arXiv:2606.27277v1 Announce Type: new Abstract: Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. I

Epiphany-Aware KV Cache Eviction Without the Attention Matrix

arXiv:2606.26472v1 Announce Type: cross Abstract: As reasoning models emit chains of thought tens of thousands of tokens long, KV cache increasingly becomes a deployment bottleneck. Existing cache evi

applicationsarxiv-cs-cl

Tutorials

Equivariance and Augmentation for Bayesian Neural Networks

arXiv:2606.26273v1 Announce Type: new Abstract: Symmetries are important for many deep learning tasks, ranging from applications in the sciences to medical imaging. However, there is an ongoing debate

tutorialsarxiv-cs-lg

Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention

arXiv:2606.26560v1 Announce Type: new Abstract: Delta-rule linear attention improves recurrent memory updates by correcting what is already stored at the current write address before writing new conte

researcharxiv-cs-cl

Error-Conditioned Neural Solvers

model-releasesarxiv-cs-ai

arXiv:2606.27354v1 Announce Type: cross Abstract: Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical tas

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

arXiv:2606.26772v1 Announce Type: new Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

arXiv:2510.17459v3 Announce Type: replace-cross Abstract: In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary sy

Estimating Uncertainty in Classifier Performance with Applications to Large Language Models and Nested Data

arXiv:2606.26422v1 Announce Type: new Abstract: Researchers increasingly use text classification--supervised models or large language models--to measure constructs from natural language, providing met

Evaluation Pitfalls and Challenges in Multimedia Event Extraction

arXiv:2606.26775v1 Announce Type: new Abstract: Multimedia event extraction aims to jointly identify events and their arguments across multiple modalities, such as text and images, to support more com

applicationsarxiv-cs-cl

Evaluation-Strategy Gap in Fault Diagnosis of Deep Learning Programs

arXiv:2606.26492v1 Announce Type: cross Abstract: Deep Learning (DL) programs can fail during training for many reasons, and diagnosing the cause is a costly and time-consuming maintenance task. Techn

applicationsarxiv-cs-ai

Event-Aware Instructed Assistant for Referring Video Segmentation

arXiv:2606.26994v1 Announce Type: cross Abstract: Existing referring video segmentation methods often treat a video as a single event consisting of multiple images, overlooking the fact that a video t

Hardware

Event-based Gaze Control System for Accurate Real-time Spin Estimation in Professional Ball Games

arXiv:2606.26780v1 Announce Type: new Abstract: Spin plays a crucial role in many ball sports due to its effect on the trajectory of the ball. Vision-based estimation of the ball's spin during a game

hardwarearxiv-cs-cv

Agents

EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory

arXiv:2606.21649v2 Announce Type: replace Abstract: Existing embedding models are inherently static: they encode text segments in isolation, ignoring their surrounding context and temporal order. This

agentsarxiv-cs-cl

Safety

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

arXiv:2606.26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each cand

safetyarxiv-cs-ai

Agents

EvoOptiGraph: Weakness-Driven Coevolution via Graph-Based Structural Generation for Optimization Modeling

arXiv:2606.26578v1 Announce Type: new Abstract: Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges. First, training corpora lack structur

agentsarxiv-cs-ai

Exact and Deterministic Patch Descriptor Retrieval via Hierarchical Normalization

arXiv:2606.27280v1 Announce Type: new Abstract: We present a patch descriptor retrieval method that returns the exact nearest neighbour -- provably identical to exhaustive full-vector search -- while

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

arXiv:2606.26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually da

Explaining Temporal Graph Neural Networks via Feature-induced Information Flow

arXiv:2606.27201v1 Announce Type: new Abstract: Event-based Temporal Graph Neural Networks (ETGNNs) have demonstrated strong performance across a wide range of applications, including social network a

applicationsarxiv-cs-lg

Exploring the Intrinsic Geometry of Diffusion Models with Constrained Inverse Kinematics

arXiv:2606.26408v1 Announce Type: new Abstract: Recent studies suggest that diffusion models can recover geometric structure in the data manifolds they are trained on, yet the supporting evidence has

researcharxiv-cs-ro

Extracting Neural Materials from Multi-view Images

arXiv:2606.26715v1 Announce Type: new Abstract: Neural materials can represent complex specular reflections and scattering effects in a compact, universal basis. However, acquiring and authoring such

Tutorials

Extracting Problem and Method Sentence from Scientific Papers: A Context-enhanced Transformer Using Formulaic Expression Desensitization

arXiv:2606.26481v1 Announce Type: new Abstract: Billions of scientific papers lead to the need to identify essential parts from the massive text. Scientific research is an activity from putting forwar

tutorialsarxiv-cs-cl

extsc{DiARC}: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models

model-releasesarxiv-cs-ai

arXiv:2606.26530v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC;~itealp{chollet2019measure}) contains tasks that require summarizing patterns from limited grid samples and

Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors

arXiv:2510.00586v3 Announce Type: replace-cross Abstract: Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poiso

researcharxiv-cs-cl

Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity

arXiv:2606.27298v1 Announce Type: cross Abstract: We study the fundamental problem of learning a high-dimensional Gaussian truncated to an unknown halfspace. Lee, Mehrotra and Zampetakis (FOCS'24) rec

Local Ai

Fast LeWorldModel

arXiv:2606.26217v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs), including recent LeWorldModel (LeWM), have become a promising foundation for reconstruction-free vis

local-aiarxiv-cs-cv

FBK's Long-form SpeechLLMs for IWSLT 2026 Instruction Following

arXiv:2606.26819v1 Announce Type: new Abstract: This paper describes our submission to the IWSLT 2026 Instruction Following shared task. SpeechLLMs are developed for both short-form and long-form spee

researcharxiv-cs-cl

FC-Vision: Real-Time Visibility-Aware Replanning for Occlusion-Free Aerial Target Structure Scanning in Unknown Environments

model-releasesarxiv-cs-ro

arXiv:2602.13720v2 Announce Type: replace Abstract: Autonomous aerial scanning of target structures is crucial for practical applications, requiring online adaptation to unknown obstacles during fligh

Federated Hash Projected Latent Factor Learning

arXiv:2606.26192v1 Announce Type: new Abstract: Hash Learning (HL) is an efficient representation learning approach that maps real-valued data into compact binary representations. Traditional HL metho

applicationsarxiv-cs-lg

Finding Stationary Points by Comparisons

arXiv:2606.27082v1 Announce Type: new Abstract: We study the problem of finding stationary points of non-convex functions when access to the objective is provided only through a comparison oracle that

researcharxiv-cs-lg

Safety

Finding the Time to Think: Learning Planning Budgets in Real-Time RL

arXiv:2606.26463v1 Announce Type: new Abstract: Deliberating takes time. In real-time settings, that time is not free. Standard reinforcement learning (RL) sidesteps this as the environment waits inde

safetyarxiv-cs-lg

FlameVQA: A Physically-Grounded UAV Wildfire VQA Benchmark with Radiometric Thermal Supervision

model-releasesarxiv-cs-cv

arXiv:2606.27128v1 Announce Type: new Abstract: Wildfire monitoring from UAVs requires reliable reasoning over complex aerial scenes, where smoke, scale variation, and occlusions often limit RGB-only

Focusing on What Matters: Saliency-Harnessing Accurate Routing for Diffusion MoE

arXiv:2606.26938v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling diffusion models in visual generation. Recent advancements have f

Safety

Forecasting With LLMs: Improved Generalization Through Feature Steering

arXiv:2606.27199v1 Announce Type: new Abstract: Successful forecasting involves identifying patterns between historical and future states of the world which generalize to future observations. We apply

safetyarxiv-cs-cl

ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2606.27079v1 Announce Type: new Abstract: In embodied intelligence, safety is a prerequisite for reliable robot deployment in the physical world. Current vision-language-action (VLA) models cont

Tutorials

Forget, Anticipate and Adapt: Test Time Training for Long Videos

arXiv:2606.26515v1 Announce Type: new Abstract: Test Time Training (TTT) is a mechanism in which a model adapts to an incoming test-sample by performing some self-supervised (SSL) task and updating it

tutorialsarxiv-cs-cv

Fortress and Gatekeeper: Theorizing Transitive Trust in Third-Party Cybersecurity Risk Governance

arXiv:2606.26866v1 Announce Type: cross Abstract: Third-party vendors, such as analytics platforms, cloud services, identity providers, and software suppliers, are increasingly embedded in digital ser

FracEvent: Event-Camera Simulation via Fractional-Relaxation Pixel Dynamics

arXiv:2606.26636v1 Announce Type: new Abstract: Event cameras asynchronously report brightness changes with microsecond-level temporal resolution, but real event data remain difficult to collect at sc

From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan

arXiv:2606.27234v1 Announce Type: cross Abstract: AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals. Prior work has exami

applicationsarxiv-cs-ai

From Clicks to Intent: Cross-Platform Session Embeddings with LLM-Distilled Taxonomy for Financial Services Recommendations

arXiv:2606.26277v1 Announce Type: cross Abstract: Sequential user behavior modeling is widely adopted in industrial recommender systems; however, significant gaps remain in financial services, where p

applicationsarxiv-cs-ai