Search: “research”

Riemannian MeanFlow for One-Step Generation on Manifolds

arXiv:2603.10718v2 Announce Type: replace Abstract: Flow Matching enables simulation-free training of generative models on Riemannian manifolds, yet sampling typically still relies on numerically inte

RISE: Reliable Improvement in Self-Evolving Vision-Language Models

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DGX agent

arXiv:2605.20914v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong multimodal reasoning capabilities, but further improving them still relies heavily on large-scale hum

researcharxiv-cs-cv

21 May 2026

RoadTones: Tone Controllable Text Generation from Road Event Videos

arXiv:2605.21411v1 Announce Type: new Abstract: Existing video-language models can generate factual descriptions of road events but lack control over how these events are expressed: their tone, urgenc

ROAR-3D: Routing Arbitrary Views for High-Fidelity 3D Generation

arXiv:2605.21121v1 Announce Type: new Abstract: Single-image-to-3D generative models can now produce high-quality geometry, yet conditioning on a single view inevitably introduces ambiguity about unse

Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning

arXiv:2605.20300v1 Announce Type: new Abstract: In this paper, we propose a robust subspace-constrained quadratic model (SCQM) for learning low-dimensional structure from high-dimensional data. Buildi

RoPeSLR: 3D RoPE-driven Sparse-LowRank Attention for Efficient Diffusion Transformers

arXiv:2605.20659v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have revolutionized high-fidelity video generation, yet their O(L^2) attention complexity poses a formidable bottleneck fo

Model Releases

roto 2.0: The Robot Tactile Olympiad

model-releasesarxiv-cs-lg

arXiv:2605.21429v1 Announce Type: cross Abstract: Tactile-based reinforcement learning (RL) is currently hindered by fragmented research and a focus on over-saturated orientation tasks. We introduce v

Same Target, Different Basins: Hard vs. Soft Labels for Annotator Distributions

arXiv:2605.20642v1 Announce Type: new Abstract: When annotators disagree, that disagreement can reflect epistemic uncertainty rather than simple label noise. We study hard-label delivery as an alterna

Sample Complexity of Transfer Learning: An Optimal Transport Approach

arXiv:2605.20545v1 Announce Type: cross Abstract: Transfer learning is an essential technique for many machine learning/AI models of complex structures such as large language models and generative AI.

SAVER: Selective As-Needed Vision Evidence for Multimodal Information Extraction

arXiv:2605.20713v1 Announce Type: new Abstract: Multimodal IE in social media is difficult because a post may attach multiple images that are weakly related, redundant, or even misleading with respect

Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement

arXiv:2605.20395v1 Announce Type: new Abstract: A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large c

Scaling creativity in the age of AI

Storytelling is core to humanity’s DNA, stemming from our impulse to express ideals, warnings, hopes, and experiences. Technology has always been woven through the medium and the distribution: from ea

researchmit-tech-review

Score-Based Causal Discovery of Latent Variable Causal Models

arXiv:2605.20396v1 Announce Type: new Abstract: Identifying latent variables and the causal structure involving them is essential across various scientific fields. While many existing works fall under

SDM: A Powerful Tool for Evaluating Model Robustness

arXiv:2605.20308v1 Announce Type: new Abstract: Gradient-based attacks are important methods for evaluating model robustness. However, since the proposal of APGD, it has been difficult for such method

Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning

arXiv:2502.03752v5 Announce Type: replace Abstract: Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based ap

Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies

arXiv:2605.20602v1 Announce Type: new Abstract: Successive self-training on a language model's own outputs is widely characterized as a process of flattening: diversity drops, distributions narrow, an

Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis

arXiv:2605.20998v1 Announce Type: new Abstract: Aspect-Term Sentiment Analysis (ATSA) in multi-aspect sentences faces a fundamental tradeoff between efficiency and expressiveness. Existing models eith

Sketch2MinSurf: Vision-Language Guided Generation of Editable Minimal Surfaces from Hand-Drawn Sketches

arXiv:2605.20733v1 Announce Type: new Abstract: Converting hand-drawn sketches into structured 3D geometries remains challenging due to the difficulty of representing non-Euclidean surfaces and mainta

Smarter edits? Post-editing with error highlights and translation suggestions

arXiv:2605.21135v1 Announce Type: new Abstract: As MT quality increases, interest in enhanced post-editing features such as QE-derived error highlights is growing, yet evidence for their usefulness re

SmoCap: Unified Scale-Pose Canonicalization with Proxy-Mapped Trust-Region QP

arXiv:2605.20850v1 Announce Type: new Abstract: Objective: Stage-wise workflows that separate model scaling and inverse kinematics can induce morphology-posture compensation, resulting in anatomically

SPARC: Spatial-Aware Path Planning via Attentive Robot Communication

arXiv:2603.02845v3 Announce Type: replace Abstract: Efficient communication is critical for decentralized Multi-Robot Path Planning (MRPP), yet existing learned communication methods treat all neighbo

SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining

arXiv:2605.21075v1 Announce Type: new Abstract: Earth observation (EO) foundation models (FMs) are increasingly trained on multisensor data, spanning multispectral imagery (MSI), synthetic aperture ra

SR-Ground: Image Quality Grounding for Super-Resolved Content

arXiv:2605.21244v1 Announce Type: new Abstract: Super-Resolution (SR) has advanced rapidly in recent years, with diffusion-based models achieving unprecedented fidelity at the cost of introducing new

Stimulus symmetries can confound representational similarity analyses

arXiv:2605.21324v1 Announce Type: cross Abstract: What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the

Strategy-Induct: Task-Level Strategy Induction for Instruction Generation

arXiv:2605.20924v1 Announce Type: new Abstract: Designing effective task-level prompts is crucial for improving the performance of Large Language Models (LLMs). While prior work on instruction inducti

StreamGVE: Training-Free Video Editing via Few-Step Streaming Video Generation

arXiv:2605.21466v1 Announce Type: new Abstract: Although existing video editing methods are generally feasible, they often require many costly iterations and still struggle to deliver high-quality yet

Sutra: Tensor-Op RNNs as a Compilation Target for Vector Symbolic Architectures

arXiv:2605.20919v1 Announce Type: new Abstract: Sutra is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network. The compiler beta-reduces the whole pr

Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels

arXiv:2605.20347v1 Announce Type: new Abstract: Labeling a training set is often expensive and susceptible to errors, making the design of robust loss functions for label noise an important problem. T

TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data

arXiv:2605.20234v1 Announce Type: new Abstract: Prior-Data Fitted networks (PFNs) have been very successful in tabular contexts, handling prediction tasks in context. However, they are designed for si

Temporal Counterfactual Explanations of Behaviour Tree Decisions

arXiv:2509.07674v2 Announce Type: replace Abstract: Explainability, in particular, the ability for robots to explain why they have made a decision or behaved in a certain way, is a critical tool in he

Testing Support Size More Efficiently Than Learning Histograms

arXiv:2410.18915v4 Announce Type: replace-cross Abstract: Consider two problems about an unknown probability distribution p: 1. How many samples from p are required to test if p is supported on n elem

TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization

arXiv:2605.21318v1 Announce Type: new Abstract: Large language models (LLMs) are highly sensitive to the prompts used to specify task objectives and behavioral constraints. Many recent prompt optimiza

The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure?

arXiv:2605.20749v1 Announce Type: new Abstract: Gated Linear Units (GLU) and their variants are widely adopted in modern open-source large language model architectures and consistently outperform thei

Safety

The Download: online safety’s future and climate tech’s big pivot

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. Tech researchers are suing the Trump administration over the f

safetymit-tech-review

The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering

arXiv:2605.20745v1 Announce Type: cross Abstract: Generative verifiers have emerged as a promising paradigm for step-wise verification, but their verification behavior is often poorly calibrated: they

The new issue of Daedalus, the open-access Journal of the American Academy of Arts and Sciences, is chock full of good material on AI. w/@de…

The new issue of Daedalus, the open-access Journal of the American Academy of Arts and Sciences, is chock full of good material on AI. w/@demishassabis @ylecun @alondra @pushmeet and so many others I

Thinking-while-speaking: A Controlled, Interleaved Reasoning Method for Real-Time Speech Generation

arXiv:2605.20946v1 Announce Type: new Abstract: The thinking-while-speaking paradigm aims to make AI communication more human. A key challenge is maintaining fluent speech while performing deep reason

This New York Times piece is worth your time. Here’s what is happening, as simply as I can put it. Back in January, Trump sued the IRS, an a…

This New York Times piece is worth your time. Here’s what is happening, as simply as I can put it. Back in January, Trump sued the IRS, an agency he controls, demanding 10 billion over the leak of his

This New York Times piece on the January 6th slush fund is worth your time. Here’s what is happening, as simply as I can put it. Back in Jan…

This New York Times piece on the January 6th slush fund is worth your time. Here’s what is happening, as simply as I can put it. Back in January, Trump sued the IRS, an agency he controls, demanding 1

Tiny-Engram: Trigger-Indexed Concept Tables for Generative Vision

arXiv:2605.20309v1 Announce Type: new Abstract: Current personalization methods for generative vision models typically encode new concepts through continuous adapters or weight updates, yet provide li

Towards Integrated Rock Support Visualisation in 3D Point Cloud of Underground Mines

arXiv:2605.20973v1 Announce Type: new Abstract: The effectiveness of rock support in underground mines depends on the interaction between installed rock bolts and the structural fabric of the surround

Tracing the ongoing emergence of human-like reasoning in Large Language Models

arXiv:2605.21299v1 Announce Type: new Abstract: Humans effortlessly go beyond literal meanings: If you mow the lawn, I will give you fifty dollars, is typically understood as implying that the speaker

TreeText-CTS: Compact, Source-Traceable Tree-Path Evidence for Irregular Clinical Time-Series Prediction

arXiv:2605.20292v1 Announce Type: new Abstract: Numerical time-series models can effectively process irregular electronic health record (EHR) trajectories, but they do not naturally expose the measure

TriForces: Augmenting Atomistic GNNs for Transferable Representations

arXiv:2605.20581v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) achieve excellent accuracy when trained on large Density Functional Theory (DFT) data. To be useful in p

Trump’s latest corruption seems to breach the constitution, creating a $1.8 billion slush fund for his friends without congressional authori…

Trump’s latest corruption seems to breach the constitution, creating a $1.8 billion slush fund for his friends without congressional authorization and effectively pardoning himself by stopping IRS aud

Trump’s use of taxpayer money to create a $1.8 billion political slush fund may be the episode of presidential corruption that is the most b…

Trump’s use of taxpayer money to create a $1.8 billion political slush fund may be the episode of presidential corruption that is the most blatant and threatening to the constitutional order in modern

Uncertainty-Guided Conservative Propagation for Structured Inference in Vessel Segmentation

arXiv:2605.20543v1 Announce Type: new Abstract: Accurate vessel segmentation is essential for medical image analysis, yet remains challenging due to complex vascular patterns and imaging ambiguity. Mo

Understanding Model Behavior in Monocular Polyp Sizing

arXiv:2605.20461v1 Announce Type: new Abstract: Accurate polyp size stratification guides surveillance decisions, with lesions larger than 5 mm typically requiring closer follow-up. However, monocular