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Reasoning Models Don't Just Think Longer, They Move Differently

arXiv:2605.15454v1 Announce Type: new Abstract: Reasoning-trained language models often spend more tokens on harder problems, but longer chains of thought do not show whether a model is merely computi

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Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains

DGX agent

arXiv:2602.16274v2 Announce Type: replace Abstract: We present the first regret bound for classical online Q-learning in infinite-horizon discounted Markov decision processes (MDPs), without relying o

researcharxiv-cs-lg

18 May 2026

Rethinking Neural Network Learning Rates: A Stackelberg Perspective

arXiv:2605.15530v1 Announce Type: new Abstract: Neural networks are typically trained with a single learning rate across all layers. While recent empirical evidence suggests that assigning layer-speci

RIDE: Retinex-Informed Decoupling for Exposing Concealed Objects

arXiv:2605.15450v1 Announce Type: cross Abstract: Concealed Object Segmentation (COS) encompasses a family of dense-prediction tasks, including camouflaged object detection, polyp segmentation, transp

Robust Prior-Guided Segmentation for Editable 3D Gaussian Splatting

arXiv:2605.16065v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3D-GS) enables real-time 3D scene reconstruction but lacks robust segmentation for editing tasks such as object removal, extrac

RoiMAM: Region-of-Interest Medical Attention Model for Efficient Vision-Language Understanding

arXiv:2605.15561v1 Announce Type: new Abstract: Vision-Language Models (VLMs) facilitate medical visual question answering (MedVQA) by jointly interpreting images and text. However, existing models ty

SAE-RNA: A Sparse Autoencoder Model for Interpreting RNA Language Model Representations

arXiv:2510.02734v2 Announce Type: replace-cross Abstract: Deep learning, particularly with the advancement of Large Language Models, has transformed biomolecular modeling, with protein language models

Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming

arXiv:2507.17572v3 Announce Type: replace Abstract: Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical r

researcharxiv-cs-ro

Self-Prompting Diffusion Transformer for Open-Vocabulary Scene Text Editing via In-Context Learning

arXiv:2605.15523v1 Announce Type: new Abstract: Scene text editing aims to modify text in a target region of an image while preserving surrounding background style and texture. Existing methods rely s

Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps

arXiv:2603.15269v2 Announce Type: replace Abstract: The tortuosity of corneal nerve fibers are used as indication for different diseases. Current state-of-the-art methods for grading the tortuosity he

SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts

arXiv:2602.18801v2 Announce Type: replace Abstract: Autoregressive neural PDE surrogates predict future states by repeatedly applying a learned one-step operator. This is a simple and widely used meth

Sharp Spectral Thresholds for Logit Fixed Points

arXiv:2605.15651v1 Announce Type: cross Abstract: Softmax feedback systems are a common mathematical core of entropy-regularized reinforcement learning, logit game dynamics, population choice, and mea

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models

arXiv:2602.01970v2 Announce Type: replace Abstract: Reinforcement learning enhances the reasoning capabilities of large language models but often involves high computational costs due to rollout-inten

Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation

arXiv:2505.18853v2 Announce Type: replace Abstract: Diffusion models have achieved state-of-the-art performance in generating images, audio, and video, but their adaptation to text remains challenging

Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models

arXiv:2605.15961v1 Announce Type: new Abstract: Large-scale pre-trained vision-language models like CLIP demonstrate remarkable zero-shot performance across diverse tasks. However, fine-tuning these m

Spectral Priors vs. Attention: Investigating the Utility of Attention Mechanisms in EEG-Based Diagnosis

arXiv:2605.15433v1 Announce Type: new Abstract: Electroencephalograph (EEG) timeseries signals are characterized by significant noise and coarse spatial resolution, which complicates the classificatio

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR

arXiv:2507.15778v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large La

Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe

arXiv:2605.15350v1 Announce Type: cross Abstract: Stochastic compositional optimization minimizes objectives of the form min_{m{x} in X} F(m{f}(m{x}), m{x}), where m{f} is accessible only through nois

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients

arXiv:2605.15522v1 Announce Type: cross Abstract: Much of the existing theory on first-order non-smooth optimization is built on a restrictive assumption that the gradients of the objective function a

Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis

arXiv:2603.05917v3 Announce Type: replace-cross Abstract: Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market e

Structured Jacobian Construction for Motion Optimization with High-Order Time Derivatives in Multi-Link Systems

arXiv:2605.15845v1 Announce Type: new Abstract: This paper presents a novel framework for Jacobian computation in motion optimization problems involving multi-link systems, where physical quantities a

researcharxiv-cs-ro

Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems

arXiv:2605.15881v1 Announce Type: cross Abstract: The modeling and simulation of infinite-dimensional Hamiltonian systems are central problems in mathematical physics and engineering, however they pos

Syntax Without Semantics: Teaching Large Language Models to Code in an Unseen Language

arXiv:2605.15607v1 Announce Type: new Abstract: Large language models (LLMs) achieve high pass rates on code generation benchmarks, yet whether they can transfer this ability to languages absent from

Testing properties of trees in graphical models with covariance queries

arXiv:2605.15996v1 Announce Type: cross Abstract: We consider the problem of testing properties of graphs underlying high-dimensional graphical models. We adopt the model of covariance queries introdu

Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction

arXiv:2605.15558v1 Announce Type: cross Abstract: High-resolution remote sensing imagery is critical for environmental monitoring, urban mapping, and land cover analysis, but its transmission is often

The Download: Musk v. Altman week 3, and Trump’s tech trading

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. Musk v. Altman week 3: Musk and Altman traded blows over each

researchmit-tech-review

The OncoReach Stylet for Brachytherapy: Design Evaluation and Pilot Study

arXiv:2601.13529v2 Announce Type: replace Abstract: Cervical cancer accounts for a significant portion of the global cancer burden among women. Interstitial brachytherapy (ISBT) is a standard procedur

researcharxiv-cs-ro

The Privacy Price of Tail-Risk Learning: Effective Tail Sample Size in Differentially Private CVaR Optimization

arXiv:2605.16219v1 Announce Type: new Abstract: Differential privacy changes the effective sample size governing CVaR learning. For tail mass au, the privacy-relevant sample size is not n, but nau; eq

This is the single most important chart. If AI were driving prices, you'd see a cluster top-right. You don't. States with huge load growth (…

This is the single most important chart. If AI were driving prices, you'd see a cluster top-right. You don't. States with huge load growth (VA, TX, NV, ND, IA) sit at ~0c change in 5y. States with mas

researchyann-lecun--x

Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning

arXiv:2605.15692v1 Announce Type: new Abstract: We study episodic reinforcement learning with fixed reward and transition functions, but with episode-dependent admissible action sets that are observed

Time-Varying Deep State Space Models for Sequences with Switching Dynamics

arXiv:2605.15311v1 Announce Type: new Abstract: The identification and modeling of time-varying systems is a fundamental challenge in signal processing and system identification. To address this chall

Toward LLMs Beyond English-Centric Development

arXiv:2605.15613v1 Announce Type: new Abstract: Through an analysis of sequences generated by open-weight large language models (LLMs), we demonstrate that LLMs are heavily biased toward English. Whil

Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks

arXiv:2605.16085v1 Announce Type: cross Abstract: Relational databases store much of the world's structured information, and they are essential for driving complex predictive applications. However, de

Transformer-like Inference from Optimal Control

arXiv:2605.15608v1 Announce Type: new Abstract: Decoder-only transformers compute the conditional probability of the next token from a sequence of past observations. This paper derives, from first pri

Transformers are Inherently Succinct

arXiv:2510.19315v3 Announce Type: replace-cross Abstract: We study succinctness as a measure of the expressive power of transformers. Succinctness -- how compactly a formalism can describe a language

Two-Dimensional Quantization for Geometry-Aware Audio Coding

arXiv:2512.01537v2 Announce Type: replace-cross Abstract: Recent neural audio codecs have achieved impressive reconstruction quality, typically relying on quantization methods such as Residual Vector

U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable exploration

arXiv:2605.15421v1 Announce Type: new Abstract: In this study, we explore in depth a few under-studied topics at the intersection of uncertainty estimation and segmentation. Prior work has shown that

Uncertainty-Aware Wildfire Smoke Density Classification from Satellite Imagery via CBAM-Augmented EfficientNet with Evidential Deep Learning

arXiv:2605.15894v1 Announce Type: cross Abstract: Rapid and accurate wildfire smoke severity assessment from satellite images is essential for emergency response, air quality modeling, and human healt

Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy

arXiv:2605.16230v1 Announce Type: cross Abstract: Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic struc

'Unlimited Realm of Exploration and Experimentation': Methods and Motivations of AI-Generated Sexual Content Creators

arXiv:2601.21028v2 Announce Type: replace-cross Abstract: AI-generated media is radically changing the way content is both consumed and produced on the internet, and in no place is this potentially mo

Unsupervised 3D Human Pose Estimation via Conditional Multi-view Ancestral Sampling

arXiv:2605.15583v1 Announce Type: new Abstract: We propose a method of estimating a 3D human pose from a single view without 3D supervision. The key to our method is to leverage the 2D diffusion prior

Unsupervised Domain Shift Detection with Interpretable Subspace Attribution

arXiv:2605.15920v1 Announce Type: cross Abstract: We developed a tool for detecting domain shifts, namely subtle differences in the probability distributions of datasets. We identify these shifts usin

Variational Autoregressive Networks with probability priors

arXiv:2605.16020v1 Announce Type: new Abstract: Monte Carlo methods are essential across diverse scientific fields, yet their efficiency is frequently hampered by critical slowing down-a sharp increas

Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity

arXiv:2511.03606v2 Announce Type: replace-cross Abstract: The study of self-normalized processes plays a crucial role in a wide range of applications, from sequential decision-making to econometrics.

Viability of perturbative expansion for quantum field theories on neurons

arXiv:2508.03810v4 Announce Type: replace-cross Abstract: Neural Network (NN) architectures that break statistical independence of parameters have been proposed as a new approach for simulating local

ViewBridge: Curriculum Knowledge Distillation for Activity View-Invariance Under Extreme Viewpoint Changes

arXiv:2504.05451v2 Announce Type: replace Abstract: Traditional methods for view-invariant learning rely on controlled multi-view training data with minimal scene clutter. However, they struggle with

Visual Compositional Tuning

arXiv:2504.21850v3 Announce Type: replace Abstract: Visual instruction tuning (VIT) datasets have grown rapidly in scale, yet the informativeness of individual training samples has largely been overlo

What to expect from Google this week

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. When Google opens its doors tomorrow for its annual developer

researchmit-tech-review