Search: “research”

SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning

arXiv:2604.06636v1 Announce Type: cross Abstract: Process supervision has emerged as a promising approach for enhancing LLM reasoning, yet existing methods fail to distinguish meaningful progress from

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Research

Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications

DGX agent

arXiv:2603.13970v2 Announce Type: replace Abstract: In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our underst

researcharxiv-cs-lg

10 Apr 2026

SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression

arXiv:2604.07316v1 Announce Type: new Abstract: The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) of

SleepNet and DreamNet: Enriching and Reconstructing Representations for Consolidated Visual Classification

arXiv:2409.01633v4 Announce Type: replace-cross Abstract: An effective integration of rich feature representations with robust classification mechanisms remains a key challenge in visual understanding

Smart Commander: A Hierarchical Reinforcement Learning Framework for Fleet-Level PHM Decision Optimization

arXiv:2604.07171v1 Announce Type: new Abstract: Decision-making in military aviation Prognostics and Health Management (PHM) faces significant challenges due to the 'curse of dimensionality' in large-

Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization

arXiv:2307.03571v4 Announce Type: replace Abstract: We present a framework for smooth optimization of explicitly regularized objectives for (structured) sparsity. These non-smooth and possibly non-con

SMT-AD: a scalable quantum-inspired anomaly detection approach

arXiv:2604.06265v1 Announce Type: new Abstract: Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Her

SonoSelect: Efficient Ultrasound Perception via Active Probe Exploration

arXiv:2604.05933v3 Announce Type: replace Abstract: Ultrasound perception typically requires multiple scan views through probe movement to reduce diagnostic ambiguity, mitigate acoustic occlusions, an

Sparse-Aware Neural Networks for Nonlinear Functionals: Mitigating the Exponential Dependence on Dimension

arXiv:2604.06774v1 Announce Type: cross Abstract: Deep neural networks have emerged as powerful tools for learning operators defined over infinite-dimensional function spaces. However, existing theori

Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)

arXiv:2604.06482v1 Announce Type: cross Abstract: Time-resolved volumetric MR imaging that reconstructs a 3D MRI within sub-seconds to resolve deformable motion is essential for motion-adaptive radiot

SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training

arXiv:2601.23155v2 Announce Type: replace-cross Abstract: Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monot

Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution

arXiv:2604.07725v1 Announce Type: cross Abstract: We show that verifier-free evolution is bottlenecked by both diversity and efficiency: without external correction, repeated evolution accelerates col

researcharxiv-cs-cl

Stabilizing Unsupervised Self-Evolution of MLLMs via Continuous Softened Retracing reSampling

arXiv:2604.03647v2 Announce Type: replace-cross Abstract: In the unsupervised self-evolution of Multimodal Large Language Models, the quality of feedback signals during post-training is pivotal for st

Stochastic Gradient Descent in the Saddle-to-Saddle Regime of Deep Linear Networks

arXiv:2604.06366v1 Announce Type: new Abstract: Deep linear networks (DLNs) are used as an analytically tractable model of the training dynamics of deep neural networks. While gradient descent in DLNs

STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training

arXiv:2604.06836v2 Announce Type: new Abstract: Quantization is an effective way to reduce the memory cost of large-scale model training. However, most existing methods adopt fixed-precision policies,

Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning

arXiv:2604.06990v1 Announce Type: cross Abstract: Psychological stress is clinically relevant in cardio-oncology, yet it is typically assessed only through patient-reported outcome measures (PROMs) an

STRIDE-ED: A Strategy-Grounded Stepwise Reasoning Framework for Empathetic Dialogue Systems

arXiv:2604.07100v1 Announce Type: cross Abstract: Empathetic dialogue requires not only recognizing a user's emotional state but also making strategy-aware, context-sensitive decisions throughout resp

SurfelSplat: Learning Efficient and Generalizable Gaussian Surfel Representations for Sparse-View Surface Reconstruction

arXiv:2604.08370v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has demonstrated impressive performance in 3D scene reconstruction. Beyond novel view synthesis, it shows great potential f

SVGFusion: A VAE-Diffusion Transformer for Vector Graphic Generation

arXiv:2412.10437v3 Announce Type: replace Abstract: Generating high-quality Scalable Vector Graphics (SVGs) from text remains a significant challenge. Existing LLM-based models that generate SVG code

SyncBreaker:Stage-Aware Multimodal Adversarial Attacks on Audio-Driven Talking Head Generation

arXiv:2604.08405v1 Announce Type: new Abstract: Diffusion-based audio-driven talking-head generation enables realistic portrait animation, but also introduces risks of misuse, such as fraud and misinf

Syntax Is Easy, Semantics Is Hard: Evaluating LLMs for LTL Translation

arXiv:2604.07321v1 Announce Type: cross Abstract: Propositional Linear Temporal Logic (LTL) is a popular formalism for specifying desirable requirements and security and privacy policies for software,

Model Releases

Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity

model-releasesarxiv-cs-ai

arXiv:2509.09794v4 Announce Type: replace Abstract: Computational models have emerged as powerful tools for multi-scale energy modeling research at the building and urban scale, supporting data-driven

Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation

arXiv:2604.07916v1 Announce Type: new Abstract: Referring Expression Segmentation (RES) aims to segment image regions described by natural-language expressions, serving as a bridge between vision and

Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking

arXiv:2604.06424v1 Announce Type: cross Abstract: This paper presents a transformer-based approach to solving the SympTEMIST named entity recognition (NER) and entity linking (EL) tasks. For NER, we f

The Detection-Extraction Gap: Models Know the Answer Before They Can Say It

arXiv:2604.06613v2 Announce Type: cross Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three ben

The Download: an exclusive Jeff VanderMeer story and AI models too scary to release

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. Constellations —Constellations is a short story by Jeff Vander

researchmit-tech-review

The Human Condition as Reflected in Contemporary Large Language Models

arXiv:2604.06206v1 Announce Type: cross Abstract: This study seeks to uncover evidence of a latent structure in evolved human culture as it is refracted through contemporary large language models (LLM

The Illusion of Superposition? A Principled Analysis of Latent Thinking in Language Models

arXiv:2604.06374v1 Announce Type: cross Abstract: Latent reasoning via continuous chain-of-thoughts (Latent CoT) has emerged as a promising alternative to discrete CoT reasoning. Operating in continuo

The Persistence of Cultural Memory: Investigating Multimodal Iconicity in Diffusion Models

arXiv:2511.11435v3 Announce Type: replace Abstract: The ambiguity between generalization and memorization in TTI diffusion models becomes pronounced when prompts invoke culturally shared visual refere

The reason symmetry is so important in physics is because symmetry is a highly effective compression operator. If a system is invariant unde…

researchfrancois-chollet--x

The reason symmetry is so important in physics is because symmetry is a highly effective compression operator. If a system is invariant under some symmetry, you only need to explain one axis of it. Sc

The Rhetoric of Machine Learning

arXiv:2604.06754v1 Announce Type: new Abstract: I examine the technology of machine learning from the perspective of rhetoric, which is simply the art of persuasion. Rather than being a neutral and 'o

The Unreasonable Effectiveness of Data for Recommender Systems

arXiv:2604.06420v2 Announce Type: cross Abstract: In recommender systems, collecting, storing, and processing large-scale interaction data is increasingly costly in terms of time, energy, and computat

Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach

arXiv:2602.18377v3 Announce Type: replace-cross Abstract: Quantum reservoir computers (QRCs) have emerged as a promising approach to quantum machine learning, since they utilize the natural dynamics o

Thinking in Graphs with CoMAP: A Shared Visual Workspace for Designing Project-Based Learning

arXiv:2604.06200v1 Announce Type: cross Abstract: Designing project-based learning (PBL) demands managing highly interdependent components, a task that both traditional linear tools and purely convers

🚨🇫🇷🇪🇺 This is big: the French government and agencies are officially getting out of Windows & non-EU tech. Each ministry has to present…

🚨🇫🇷🇪🇺 This is big: the French government and agencies are officially getting out of Windows & non-EU tech. Each ministry has to present their exit plan before Autumn: collaboration tools, antivirus, A

researchyann-lecun--x

Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding

arXiv:2503.10183v4 Announce Type: replace Abstract: Existing vision-language models (VLMs) often suffer from visual hallucination, where the generated responses contain inaccuracies that are not groun

Time-Series Classification with Multivariate Statistical Dependence Features

arXiv:2604.06537v1 Announce Type: new Abstract: In this paper, we propose a novel framework for non-stationary time-series analysis that replaces conventional correlation-based statistics with direct

Toward a Tractability Frontier for Exact Relevance Certification

arXiv:2604.07349v1 Announce Type: cross Abstract: Exact relevance certification asks which coordinates are necessary to determine the optimal action in a coordinate-structured decision problem. The tr

Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times

arXiv:2604.06251v1 Announce Type: new Abstract: This article presents the results of a data science study conducted at a container terminal, aimed at reducing unproductive container moves through the

Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models

arXiv:2503.13551v5 Announce Type: replace Abstract: Recent studies show that Large Language Models (LLMs) achieve strong reasoning capabilities through supervised fine-tuning or reinforcement learning

researcharxiv-cs-cl

Towards Robust Content Watermarking Against Removal and Forgery Attacks

arXiv:2604.06662v1 Announce Type: cross Abstract: Generated contents have raised serious concerns about copyright protection, image provenance, and credit attribution. A potential solution for these p

Transformer See, Transformer Do: Copying as an Intermediate Step in Learning Analogical Reasoning

arXiv:2604.06501v1 Announce Type: new Abstract: Analogical reasoning is a hallmark of human intelligence, enabling us to solve new problems by transferring knowledge from one situation to another. Yet

Trump's pointless war-of-choice just cost an estimated $1 trillion -- enough to provide universal pre-K in America for all 3- and 4-year-old…

Trump's pointless war-of-choice just cost an estimated 1 trillion -- enough to provide universal pre-K in America for all 3- and 4-year-olds, or make college accessible to every family earning 125,000

researchyann-lecun--x

U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations

arXiv:2604.08295v1 Announce Type: cross Abstract: As AI models grow more complex, explainability is essential for building trust, yet concept-based counterfactual methods still face a trade-off betwee

Uni-ViGU: Towards Unified Video Generation and Understanding via A Diffusion-Based Video Generator

arXiv:2604.08121v1 Announce Type: new Abstract: Unified multimodal models integrating visual understanding and generation face a fundamental challenge: visual generation incurs substantially higher co

Weakly-Supervised Lung Nodule Segmentation via Training-Free Guidance of 3D Rectified Flow

arXiv:2604.08313v1 Announce Type: new Abstract: Dense annotations, such as segmentation masks, are expensive and time-consuming to obtain, especially for 3D medical images where expert voxel-wise labe

Weaves, Wires, and Morphisms: Formalizing and Implementing the Algebra of Deep Learning

arXiv:2604.07242v1 Announce Type: new Abstract: Despite deep learning models running well-defined mathematical functions, we lack a formal mathematical framework for describing model architectures. Ad

Weight Group-wise Post-Training Quantization for Medical Foundation Model

arXiv:2604.07674v1 Announce Type: new Abstract: Foundation models have achieved remarkable results in medical image analysis. However, its large network architecture and high computational complexity