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Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging

arXiv:2606.23944v1 Announce Type: cross Abstract: State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture hea

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Stripe, Anthropic, and OpenAI are backing an effort to stop respiratory infections

DGX agent

The common cold comes for us all—often more than once a year. And there is no way to prevent it. The best you can do is take vitamin C and stay away from people with the sniffles. Now the payment comp

researchmit-tech-review

24 Jun 2026

Subtyping patients with chronic disease using longitudinal BMI patterns

arXiv:2111.05385v3 Announce Type: replace Abstract: Obesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke. While the role of ob

Supervise What Survives: Geometry-Guided VLA Adaptation from Synthetic Robot Videos

arXiv:2606.24448v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models require large-scale video-action pairs, yet real teleoperation remains scarce. While generated robot videos offer a

Talos: Scaling rare disease diagnosis with automated, iterative genomic reanalysis

researchmicrosoft-research

Talos was built to help resolve a major bottleneck in genomic medicine: human review time. The open-source system recovered 90% of in-scope diagnoses while surfacing just 1.3 candidate variants per pa

Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic

arXiv:2502.01015v5 Announce Type: replace Abstract: Task arithmetic, representing downstream tasks through linear operations on task vectors, has emerged as a simple yet powerful paradigm for transfer

Tensor-based second-order causal discovery

arXiv:2606.18074v2 Announce Type: replace-cross Abstract: Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Secon

Thank you all for the overwhelming interest in our new Member of Technical Staff (RSI Lab) role. Just to clarify many questions asked, the r…

Thank you all for the overwhelming interest in our new Member of Technical Staff (RSI Lab) role. Just to clarify many questions asked, the role is not remote. We expect candidates to move to Tokyo, Ja

researchdavid-ha--x

The best way to understand a complex system is via edge cases and failure modes, because they define the contour of the system.

researchfrancois-chollet--x

Edge cases and failure modes reveal the fundamental boundaries and constraints of complex systems more effectively than typical operations, making them valuable for understanding system behavior and l

The Download: introducing the Engineering issue

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. Introducing: the Engineering issue We can’t fix everything, bu

The emergence of the web data infrastructure layer for AI

AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstru

The Evaluation Cost of Task Specialization in Evolutionary Multi-Robot Systems

arXiv:2606.24191v1 Announce Type: new Abstract: Task specialization can improve the efficiency of multi-robot systems (MRSs). Previous works have investigated the emergence of task-specialist robot co

The Geometry Behind Diffusion and Flow Matching: Gradient Flows and Geodesics in Wasserstein Space

arXiv:2606.24157v1 Announce Type: new Abstract: The space P_2(R^d) of probability measures with finite second moment carries a natural geometry: the quadratic Wasserstein distance W_2 makes it a compl

The mathbf{P}-Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs

arXiv:2601.18747v2 Announce Type: replace-cross Abstract: Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows. These workflows often comp

The Measurable Majority

arXiv:2606.23853v1 Announce Type: cross Abstract: This paper studies strict majority reasoning in finite electorates using so-called extit{social decision frames}: finite sets of voters equipped with

The most complex phenomena arise from scalable recombination of very simple rules. Whether it's galaxies, chips, or neural networks, if you …

researchfrancois-chollet--x

The most complex phenomena arise from scalable recombination of very simple rules. Whether it's galaxies, chips, or neural networks, if you find the right primitive building blocks, the complexity tak

The Professor: Multi-Teacher Unsupervised Prompt Distillation for Vision-Language Models

arXiv:2606.23897v1 Announce Type: cross Abstract: Prompt distillation compresses large vision-language models (VLMs) such as CLIP into lightweight student models by matching teacher predictions on unl

The Warrant Gap: Claim-Conditioned Re-scoring for Fact-Checking

arXiv:2606.24627v1 Announce Type: new Abstract: Fact-checking systems built on LLMs achieve high verdict accuracy on standard benchmarks, yet routinely output Supports labels whose cited evidence does

researcharxiv-cs-cl

This flying solar-powered platform could deliver better internet from the air

As soon as August, a giant silver bullet will cut its way through the dry air of the southwestern US and cross the Pacific to reach the coast of Japan. Once there, the roughly 200-foot-long craft, bui

To enable creative-ideation and our other optional skills, run 'hermes skills config'

Nous Research recommends using the 'hermes skills config' command to enable creative-ideation and other optional skills in their system. This command appears to be a configuration tool for activating

researchnous-research--x

Token Complexity of Certifying Stochastic-Oracle Reliability

arXiv:2606.24074v1 Announce Type: cross Abstract: Wang~ite{Wang2026} introduced the Stochastic-Oracle Turing Machine (SOTM) framework and defined token complexity as the minimum expected cost of inter

Topological Online Learning for Displacement-based Formation Control

arXiv:2606.23901v1 Announce Type: new Abstract: This paper addresses the problem of robust formation control by introducing Topological Online Learning for Displacement-based (TOLD) formation control,

Towards Version-aware Operations and Transaction Memories for Multi-layer MeMo

arXiv:2606.24040v1 Announce Type: cross Abstract: MeMo proposes language models with explicit multi-layer correlation matrix memories (CMMs), where memorization, retrieval, and forgetting are architec

Tractable Reasoning and Conjunctive Query Answering for Defeasible DL-Lite under Rational Closure

arXiv:2606.24279v1 Announce Type: new Abstract: In Description Logics (DLs), reasoning under Rational Closure (RC) is a well-known and widely accepted non-monotonic formalism to handle defeasible know

Training-free Cross-domain Few-shot Segmentation via Robust Semantic Representation and Matching

arXiv:2606.24297v1 Announce Type: new Abstract: Cross-domain Few-shot Segmentation (CD-FSS) aims to transfer knowledge learned from source domain to distinct target domains, segmenting unseen target c

Transformation Behavior of Images in Latent Space

arXiv:2606.24430v1 Announce Type: cross Abstract: Training of neural networks for histopathology classification tasks typically relies on data encoding into latent space, which reduces complexity and

Trustworthy Image Authentication using Forensic Knowledge Graphs

arXiv:2606.23917v1 Announce Type: new Abstract: Advances in generative AI have made image falsification highly realistic, demanding trustworthy authentication systems. Existing forensic detectors can

Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning

arXiv:2606.24604v1 Announce Type: new Abstract: Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how

Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination

arXiv:2311.02960v5 Announce Type: replace-cross Abstract: Over the past decade, deep learning has proven to be a highly effective tool for learning meaningful features from raw data. However, it remai

Uniform Sampling from High-dimensional Spectral Norm Balls

arXiv:2606.24134v1 Announce Type: cross Abstract: Motivated by an application in machine learning optimization, this paper focuses on the challenges of sampling a matrix uniformly from the unit spectr

VeryTrace: Verifying Reasoning Traces through Compilable Formalism and Structured Verification

arXiv:2606.24124v1 Announce Type: new Abstract: Multi-step reasoning with Chain-of-Thought (CoT) prompting remains fragile: logical errors or hallucinations in early steps silently propagate, producin

VieSpeaker: A Large-Scale Vietnamese Speaker Recognition Dataset Beyond Visual Dependency

arXiv:2606.24066v1 Announce Type: cross Abstract: Speaker recognition has advanced rapidly with large-scale training datasets, yet Vietnamese remains under-resourced, with existing corpora limited in

researcharxiv-cs-cl

Visualizing 'We the People': Bridging the Perception Gap through Pluralistic Data Storytelling

arXiv:2606.24635v1 Announce Type: cross Abstract: Traditional visual data storytelling relies on binary graphics that depict two simplified groups in conflict. This can increase political polarization

VSANet: View-aware Sparse Attention Network for Light Field Image Denoising

arXiv:2606.24737v1 Announce Type: new Abstract: Light field (LF) image denoising is challenging due to the high-dimensional structure of LF data. While noise is independent across sub-aperture images,

Weight-Space Geometry of Offline Reasoning Training

arXiv:2606.23740v1 Announce Type: cross Abstract: Offline reinforcement-learning losses (RFT, RIFT, DFT, Offline GRPO, DPO) are widely used to distill reasoning from large teachers into smaller studen

What's Missing in Vision-Language Models? Probing Their Struggles with Causal Order Reasoning

arXiv:2506.00869v3 Announce Type: replace Abstract: Despite the impressive performance of vision-language models (VLMs) on downstream tasks, their ability to understand and reason about causal relatio

researcharxiv-cs-cl

Which Spaces can be Embedded in L_p-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy

arXiv:2410.11116v4 Announce Type: replace-cross Abstract: In this paper, we establish a novel connection between the metric entropy growth and the embeddability of function spaces into reproducing ker

Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding

arXiv:2606.23706v1 Announce Type: cross Abstract: The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective ne

Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring

arXiv:2606.24178v1 Announce Type: cross Abstract: Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object clas

2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models

arXiv:2606.21414v1 Announce Type: cross Abstract: The ability to synthesize realistic X-ray images has catalyzed the development of AI models for X-ray image-guided procedures, which otherwise suffer

A breath test could diagnose pneumonia in minutes

With a test being developed at MIT, diagnosing pneumonia and other lung conditions could someday be as easy as breathing into a tube. The test, dubbed PlasmoSniff, is a portable, chip-scale sensor tha

A Causal DAG Prior for Synthetic Time-Series Classification Datasets

arXiv:2606.21776v1 Announce Type: new Abstract: A Prior-data fitted Network learns the posterior predictive induced by its training prior; bringing this paradigm to multivariate time-series classifica

A Comprehensive Study on Visual Token Redundancy for Discrete Diffusion-based Multimodal Large Language Models

arXiv:2511.15098v2 Announce Type: replace Abstract: Discrete diffusion-based multimodal large language models (dMLLMs) have emerged as a promising alternative to autoregressive MLLMs thanks to their a

A Framework for Directed Acyclic Hypergraph Learning

arXiv:2606.21668v1 Announce Type: new Abstract: Continuous optimization methods for learning Directed Acyclic Graphs (DAGs) operate on weighted adjacency matrices and are therefore limited to pairwise

A Generalization Bound for Nearly-Linear Networks

arXiv:2407.06765v2 Announce Type: replace Abstract: We consider nonlinear networks as perturbations of linear ones. Based on this approach, we present novel generalization bounds that become non-vacuo

A Generalized Formalism of Auto-Regressive Decoding for Speech Processing

arXiv:2606.20714v1 Announce Type: cross Abstract: In speech processing, most state-of-the-art sequence prediction models rely on auto-regressive (AR) strategies to generate output sequences based on t

A geometric and deep learning reproducible pipeline for monitoring floating anthropogenic debris in urban rivers using in situ cameras

arXiv:2510.23798v2 Announce Type: cross Abstract: The proliferation of floating anthropogenic debris in rivers has emerged as a pressing environmental concern, exerting a detrimental influence on biod

A Geometric Task-Space Port-Hamiltonian Formulation for Redundant Manipulators

arXiv:2512.14349v2 Announce Type: replace-cross Abstract: We present a novel geometric port-Hamiltonian formulation of redundant manipulators performing a differential kinematic task eta=J(q)ot{q}, wh