research

DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors

arXiv:2606.26707v1 Announce Type: cross Abstract: Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors. In this work, we first show that,

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Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM

DGX agent

arXiv:2606.26120v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirect

researcharxiv-cs-cl

26 Jun 2026

Efficient foundation decoders for fault-tolerant quantum computing

arXiv:2606.27119v1 Announce Type: cross Abstract: Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficien

Efficient learning of bosonic Gaussian unitaries

arXiv:2510.05531v2 Announce Type: replace-cross Abstract: Bosonic Gaussian unitaries are fundamental building blocks of central continuous-variable quantum technologies such as quantum-optic interfero

Einstein World Models

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

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

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

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

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

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

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

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

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

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

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

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

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 Vajrayana Tara to Bengali Baul: A Computational Study of Lexical Transmission Across Buddhist, Shakta, and Vaishnava Traditions in Bengal

arXiv:2606.26803v1 Announce Type: new Abstract: We present a computational corpus study of vocabulary relationships across eight tradition layers of Bengali and Sanskrit devotional literature spanning

fTNN: a tensor neural network for fractional PDEs

arXiv:2606.27140v1 Announce Type: new Abstract: We develop the fTNN, a deterministic tensor neural network subspace method for problems involving the fractional Laplacian on bounded domains, taking th

Full spectrum Unlearnable Examples via Spectral Equalization

arXiv:2606.26719v1 Announce Type: new Abstract: Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In

Generating Special Triangulations with Transformers

arXiv:2606.26660v1 Announce Type: cross Abstract: Triangulations, i.e., well-structured decompositions of geometric objects into triangle-like pieces, are central objects in many domains of mathematic

Generative Models on Analog Hardware with Dynamics

arXiv:2606.27294v1 Announce Type: cross Abstract: Analog hardware platforms such as coupled oscillators and Analog Ising Machines naturally solve differential equations at a fraction of the energy cos

Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning

arXiv:2606.26973v1 Announce Type: new Abstract: Open-set semi-supervised learning aims to leverage unlabeled data that may contain out-of-distribution outliers while maintaining performance on in-dist

Geometry-Aware MCTS for Extremal Problems in Combinatorial Geometry

arXiv:2606.26399v1 Announce Type: new Abstract: We study certain extremal problems in combinatorial geometry that ask about configurations of points in an n imes n grid that satisfy strict, global geo

Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification

arXiv:2606.20390v2 Announce Type: replace Abstract: Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variabili

Goal! ⚽️🇯🇵🚀

This appears to be a celebratory post by David Ha about a goal scored in a Japanese soccer/football context, likely sharing enthusiasm about a sporting achievement or milestone related to Japan's foot

researchdavid-ha--x

Gradient Testing and Estimation by Comparisons

arXiv:2405.11454v3 Announce Type: replace Abstract: We study gradient testing and gradient estimation of smooth functions using only a comparison oracle that, given two points, indicates which one has

Heat waves mess with your brain. Scientists are trying to figure out why.

It’s been hot in London this week. Really hot. A dangerous heat wave has hit Western Europe. Yesterday, the UK recorded its highest ever June temperature at 36.1 °C (about 97 °F). But as the weather a

researchmit-tech-review

Heavy-Ball Q-Learning with Residual Weighting Correction

arXiv:2606.27112v1 Announce Type: cross Abstract: This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence. It also identifies condi

HermesBench full leaderboard coming soon. Stay tuned!

Nous Research announced an upcoming HermesBench full leaderboard, indicating they are developing or expanding a benchmarking system, likely for evaluating their Hermes model family or related language

researchnous-research--x

How Good Can Linear Models Be for Time-Series Forecasting?

arXiv:2606.27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation model

Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction

arXiv:2508.18486v2 Announce Type: replace-cross Abstract: Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by provid

If you wanted to do the same in SF, you could start by extending Treasure Island. Obviously the value of that new land would be considerably…

researchfrancois-chollet--x

If you wanted to do the same in SF, you could start by extending Treasure Island. Obviously the value of that new land would be considerably less than in SF proper. Perhaps 10x less. It would be econo

Interpreting 'Interpretability' and Explaining 'Explainability' in Machine Learning in Physics

arXiv:2606.26228v1 Announce Type: cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning t

Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning

arXiv:2606.26706v1 Announce Type: new Abstract: Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both

Inverse Design of Compact and Wideband Inverted Doherty Power Amplifiers Using Deep Learning

arXiv:2606.27002v1 Announce Type: cross Abstract: This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convo

It doesn't take much skill to game this out and see where we're going if we keep this up. Kneecap our own companies. Gate everything. Let re…

It doesn't take much skill to game this out and see where we're going if we keep this up. Kneecap our own companies. Gate everything. Let regulatory capture and fear mongering win. Have our people and

researchyann-lecun--x

Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs

arXiv:2606.27126v1 Announce Type: new Abstract: Kolmogorov Arnold networks (KAN) have recently been introduced as a (deep) neural network architecture whose trainable parameters adapt the activation f

KRVF: A Source-Aware Semantic Voxel World Representation for Edge Mobile Manipulation

arXiv:2606.26321v1 Announce Type: new Abstract: Mobile manipulators need world models that are current, queryable, semantically meaningful, and usable under edge-compute constraints. This technical re

researcharxiv-cs-ro

Latent-Mark: An Audio Watermark Robust to Neural Codec Compression

arXiv:2603.05310v3 Announce Type: replace-cross Abstract: While existing audio watermarking techniques have achieved strong robustness against traditional digital signal processing (DSP) attacks, they

LearniBridge: Learnable Calibration of Feature Caching for Diffusion Models Acceleration

arXiv:2606.26778v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have driven substantial progress in image and video generation but suffer from prohibitive computational costs. Feature ca

Learning from Equivalence Queries, Revisited

arXiv:2604.04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction,

LithoDreamer: A Physics-Informed World Model for Multi-Stage Computational Lithography

arXiv:2606.26713v1 Announce Type: new Abstract: As semiconductor technology nodes scale, computational lithography is essential for ensuring yield and performance. However, lithography is a continuous

LogicIR: Logic Gate Networks for Image Restoration

arXiv:2606.26609v1 Announce Type: new Abstract: Image restoration aims to reconstruct high-quality images from degraded low-quality inputs. As the computational demands of image restoration models con

MAVFusion: Efficient Infrared and Visible Video Fusion via Motion-Aware Sparse Interaction

arXiv:2604.01958v2 Announce Type: replace Abstract: Infrared and visible video fusion combines the object saliency from infrared images with the texture details from visible images to produce semantic