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PRISM: Efficient and Locally Optimal Probabilistic Planning with Reachability Guarantees

arXiv:2606.26413v1 Announce Type: cross Abstract: Belief-space planning under motion uncertainty and state and control constraints remains a fundamental challenge, largely due to the difficulty of est

researcharxiv-cs-ro

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Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates

DGX agent

arXiv:2602.17683v3 Announce Type: replace-cross Abstract: Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture. Normalized Differenc

researcharxiv-cs-cv

26 Jun 2026

RayPE: Ray-Space Positional Encoding for 3D-Aware Video Generation

arXiv:2606.27345v1 Announce Type: new Abstract: Modern video diffusion transformers position their tokens through RoPE on the (u,v,t) axes -- a description of the camera's sampling grid that says noth

Reasoning Quality Emerges Early: Data Curation for Reasoning Models

arXiv:2606.26797v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) on a small, high-quality set of long reasoning traces is an effective approach for eliciting strong reasoning capabilities

Recovering Governing Equations from Solution Data: Identifiability Bounds for Linear and Nonlinear ODEs

arXiv:2606.27285v1 Announce Type: new Abstract: Learning governing equations from observed solution data is a fundamental challenge in scientific machine learning ite{bruntonDiscoveringGoverningEquati

Rendering Novel Views of MRI Using 3D Gaussian Splatting

arXiv:2606.26236v1 Announce Type: cross Abstract: The objective of this paper is to improve radiological gradings measured on MRIs of spines, by resampling scans so that the new view planes are better

ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration

arXiv:2606.26769v1 Announce Type: new Abstract: The adoption of powerful diffusion models is hindered by their significant inference latency. Recent ``cache-then-forecast'' schemes alleviate this issu

Revealing Mammographic Phenotypes in Deep Learning Breast Cancer Risk Models

arXiv:2606.26431v1 Announce Type: cross Abstract: Mammogram-based deep learning models have improved breast cancer risk prediction, but the learned imaging patterns remain underexplored. Existing inte

Revisiting the Platonic Representation Hypothesis: An Aristotelian View

arXiv:2602.14486v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that representations from neural networks are converging to a common statistical model of real

RoboTales: ROBOTic Anthropomorphic LEarning Systems

arXiv:2606.26213v1 Announce Type: new Abstract: RoboTales is a low-cost robotic storytelling system that animates narratives using expressive sock puppetry. Implemented autonomously on a Baxter robot

researcharxiv-cs-ro

SAM2Matting: Generalized Image and Video Matting

arXiv:2606.27339v1 Announce Type: new Abstract: Despite impressive advances in image matting, video matting remains challenging due to the inherent gap between high-level tracking, which requires fram

Sampling sea state using a diffusion model

arXiv:2606.26389v1 Announce Type: cross Abstract: Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain com

Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy

arXiv:2606.26873v1 Announce Type: cross Abstract: Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the rec

Sculpting NeRF Geometry: Human-Preference Fine-Tuning of a 3D-Aware Face GAN

arXiv:2606.27305v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) for 3D generation is now established across a number of works, but most existing pipelines optimise ex

Self-Supervised Tree-level Biomass Estimation in Urban Environments From Airborne LiDAR and Optical Observations

arXiv:2606.26194v1 Announce Type: new Abstract: Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse pro

SOLAR: AI-Powered Speed-of-Light Performance Analysis

arXiv:2606.26383v1 Announce Type: cross Abstract: How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit? These questions are central to sof

something has definitely shifted in the past few weeks. seeing a huge uptick in large enterprises wanting to secure compute and post-train t…

something has definitely shifted in the past few weeks. seeing a huge uptick in large enterprises wanting to secure compute and post-train their own models in house, frequently on top of GLM-5.2. ever

researchyann-lecun--x

SpaceRipple: Lightweight Semantic Delivery for Mission-Oriented LEO Earth Observation Satellite Networks

arXiv:2606.26559v1 Announce Type: cross Abstract: Earth observation satellite networks generate massive volumes of high-resolution imagery, whereas inter-satellite and downlink resources remain limite

State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models

arXiv:2606.26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasi

SubdivAR: Autoregressive Next-Scale Prediction for Neural Mesh Subdivision

arXiv:2606.27088v1 Announce Type: new Abstract: Mesh subdivision is a fundamental operation for converting coarse, editable meshes into high-resolution surfaces, with broad applications in digital ass

Symplectic Neural Networks for learning Generalized Hamiltonians

arXiv:2606.27029v1 Announce Type: new Abstract: Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample

Syntactic Belief Update as the Driver of Garden Path Processing Difficulty

arXiv:2606.27206v1 Announce Type: new Abstract: Garden path sentences present a processing difficulty for humans -- the sentence prefix leads the listener towards one interpretation, until the listene

Tailor Made Embeddings for Quantum Machine Learning

arXiv:2606.26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning com

TaskTok: Delving into Task Tokens for Task-driven Image Restoration

arXiv:2606.26615v1 Announce Type: new Abstract: While traditional image restoration focuses on perceptual quality, Task-Driven Image Restoration (TDIR) aims to maximize the performance of downstream h

TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation

arXiv:2606.26874v1 Announce Type: new Abstract: Transcatheter Aortic Valve Replacement (TAVR) planning requires meticulous multimodal reasoning. However, adapting Multimodal Large Language Models (MLL

TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models

arXiv:2606.26285v1 Announce Type: cross Abstract: Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target

The decades-long prison sentences imposed on a group of Texas activists who were protesting against ICE, using the guise of 'terrorism,' are…

The decades-long prison sentences imposed on a group of Texas activists who were protesting against ICE, using the guise of 'terrorism,' are an outrage clearly designed to suppress such activism. http

researchyann-lecun--x

The Download: brain-melting heatwaves and unprecedented OpenAI restrictions

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Heat waves mess with your brain. Scientists are trying to figu

researchmit-tech-review

The Governance Inversion Hypothesis: Why More AI Regulation May Produce Less Organisational Control

arXiv:2606.26117v1 Announce Type: cross Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditi

The Riddle Riddle: Testing Flexible Reasoning in Large Language Models and Humans

arXiv:2606.27103v1 Announce Type: new Abstract: Humans flexibly adapt their reasoning strategies to the requirements of a given problem. Large language models (LLMs) have performed well on many cognit

The Role of Input Dimensionality in the Emergence and Targeted Control of Adversarial Examples

arXiv:2606.26207v1 Announce Type: cross Abstract: Several theoretical works have tried to explain the adversarial vulnerability of deep neural networks through properties of high-dimensional geometry.

There will be more and more pressure and more money and lobbying behind open soon. It is not a 'two big companies have all the power in AI' …

There will be more and more pressure and more money and lobbying behind open soon. It is not a 'two big companies have all the power in AI' situation like some imagine. The financial incentives are sh

researchyann-lecun--x

This is also why a lot of land reclamation in Tokyo bay first takes the form of fully-separated islands: you don't want to disrupt establish…

researchfrancois-chollet--x

This post discusses Tokyo Bay's land reclamation practices, specifically explaining why initial reclamation projects are constructed as isolated islands rather than connected to existing land—a strate

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation

arXiv:2508.16159v2 Announce Type: replace-cross Abstract: Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extra

TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference

arXiv:2606.27161v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of

Towards Consistent and Efficient Dataset Distillation via Diffusion-Driven Selection

arXiv:2412.09959v5 Announce Type: replace Abstract: Dataset distillation provides an effective approach to reduce memory and computational costs by optimizing a compact dataset that achieves performan

Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histology

arXiv:2606.26898v1 Announce Type: new Abstract: Diffusion MRI (dMRI) tractography enables non-invasive reconstruction of white-matter pathways, but its accuracy is fundamentally limited by indirect, l

Transformer-Based Classification of Bacterial Raman Spectra with LOOCV

arXiv:2606.27096v1 Announce Type: new Abstract: Transformer-based models have recently attracted increasing attention for Raman spectral classification. In this study, a transformer-based approach was

Trust in Generative AI for Health Information Consumption and the Effect of Learned Dependency: An Experimental Investigation

arXiv:2606.20605v2 Announce Type: replace-cross Abstract: Background: Generative artificial intelligence (GenAI) is increasingly used for health information, yet its influence on users' trust calibrat

UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation

arXiv:2603.01461v2 Announce Type: replace Abstract: Echocardiography is critical for diagnosing cardiovascular diseases, yet the shortage of skilled sonographers hinders timely patient care, due to hi

Uncertainty quantification via conformal prediction in data assimilation

arXiv:2606.27001v1 Announce Type: new Abstract: Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation in numerical weather prediction. In this st

Use What You Know: Causal Foundation Models with Partial Graphs

arXiv:2602.14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models

Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs

arXiv:2606.26485v1 Announce Type: new Abstract: Microblogging platforms generate massive amounts of short, noisy, and dispersed user content, making automatic keyphrase extraction (AKE) an important b

ViQ: Text-Aligned Visual Quantized Representations at Any Resolution

arXiv:2606.27313v1 Announce Type: new Abstract: A unified representation for text and vision is a natural pursuit, as it enables simpler multimodal modeling and more efficient training. However, repre

Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning

arXiv:2509.01412v3 Announce Type: replace Abstract: Large language models (LLMs) show strong reasoning via chain-of-thought (CoT) prompting, but the process is opaque, which makes verification, debugg

VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation

arXiv:2606.26534v1 Announce Type: cross Abstract: Recently, zero-shot text-to-speech (TTS) has enabled high-fidelity and expressive speech synthesis, but it often fails to imitate unseen speaking styl

What Survives When You Compress a Recursive Reasoner for the Edge?

arXiv:2606.26488v1 Announce Type: new Abstract: Recursive reasoning models can solve complex structured tasks with only a few million parameters by repeatedly updating a latent state. Deploying these

When are likely answers right? On Sequence Probability and Correctness in LLMs

arXiv:2606.27359v1 Announce Type: cross Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, ei