Search: “research”

Information-theoretic Limits of Learning and Estimation

arXiv:2605.06710v1 Announce Type: cross Abstract: Information theory plays a central role in establishing fundamental limits on what any learning or estimation algorithm can -- and cannot -- achieve,

Innovation abounds in device charging

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The changes may be less perceptible than in smartphones, tablets, or wearables, but chargers have also been quietly reinvented over the last decade. At one time a bulky mix of tangled cables and conne

researchmit-tech-review

11 May 2026

INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy

arXiv:2605.07930v1 Announce Type: cross Abstract: Differential privacy (DP) is widely employed in machine learning to protect confidential or sensitive training data from being revealed. As data owner

InsHuman: Towards Natural and Identity-Preserving Human Insertion

arXiv:2605.07402v1 Announce Type: new Abstract: Human insertion aims to naturally place specific individuals into a target background. Although existing image editing models may have such ability, the

Interactive Jensen–Shannon Divergence Visualisation [P]

researchr-machinelearning

Jensen-Shannon divergence is a method of measuring the similarity between two probability distributions. It is based on the Kullback-Leibler divergence, with the notable difference that it is symmetri

Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features

arXiv:2603.03096v2 Announce Type: replace-cross Abstract: How do speech models trained through self-supervised learning structure their representations? Previous studies have looked at how information

Into the Rabbit Hull: From Task-Relevant Concepts in DINO to Minkowski Geometry

arXiv:2510.08638v3 Announce Type: replace-cross Abstract: DINOv2 is routinely deployed to recognize objects, scenes, and actions; yet the nature of what it perceives remains unknown. As a working base

Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization

arXiv:2512.23032v2 Announce Type: replace-cross Abstract: Recent work, using the Biasing Features metric, labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. We

It Just Takes Two: Scaling Amortized Inference to Large Sets

arXiv:2605.07972v1 Announce Type: cross Abstract: Neural posterior estimation has emerged as a powerful tool for amortized inference, with growing adoption across scientific and applied domains. In ma

Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests

arXiv:2605.06883v1 Announce Type: cross Abstract: The Maximum Mean Discrepancy (MMD) is a cornerstone statistic for nonparametric two-sample testing, but its test power is dictated entirely by the cho

Knowledge Transfer Scaling Laws for 3D Medical Imaging

arXiv:2605.06859v1 Announce Type: cross Abstract: Vision foundation models are increasingly moving beyond 2D to volumetric domains such as 3D medical imaging, where unified pretraining across differen

Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting

arXiv:2602.02832v3 Announce Type: replace Abstract: Forecasting physical systems over long horizons from irregularly sampled observations demands models that are stable, computationally efficient, and

Latent Order Bandits

arXiv:2605.07304v1 Announce Type: new Abstract: Bandit algorithms solve diverse sequential decision-making problems, but are often too sample-inefficient for from-scratch personalization. To substanti

Latent Reasoning VLA: Latent Thinking and Prediction for Vision-Language-Action Models

arXiv:2602.01166v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models benefit from chain-of-thought (CoT) reasoning, but existing approaches incur high inference overhead and rely on

researcharxiv-cs-ro

Latent-Space Causal Discovery from Indirect Neuroimaging Observations

arXiv:2602.09034v2 Announce Type: replace-cross Abstract: Neuroimaging does not observe causal variables directly: hemodynamics and volume conduction distort signals so that statistical dependence nee

LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification

arXiv:2605.07315v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves large language models (LLMs) on difficult tasks, but it also makes inference expensive because every intermedi

Learning Image-Adaptive Scale Fields for Metric Depth Recovery

arXiv:2605.07418v1 Announce Type: new Abstract: Monocular depth estimation (MDE) typically produces depth estimations that are defined up to an unknown scale or shift. When only sparse metric anchors

Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations

arXiv:2605.07460v1 Announce Type: new Abstract: Accurate Monte Carlo (MC) modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observe

Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis

arXiv:2511.09907v5 Announce Type: replace Abstract: Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-q

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling

arXiv:2605.07253v1 Announce Type: new Abstract: Distilled diffusion models accelerate image generation by reducing the number of denoising steps, but often suffer from degraded image quality. To mitig

LensVLM: Selective Context Expansion for Compressed Visual Representation of Text

arXiv:2605.07019v1 Announce Type: cross Abstract: Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into lo

Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?

arXiv:2605.07072v1 Announce Type: new Abstract: Poisson subsampling is the default sampling scheme in differentially private machine learning, largely because its unstructured randomness yields tracta

Limitations on Accurate, Trusted, Human-level Reasoning

arXiv:2509.21654v2 Announce Type: replace-cross Abstract: We identify a fundamental incompatibility between the goals of accuracy, trust, and human-level reasoning in artificial intelligence (AI) syst

Linear Response Estimators for Singular Statistical Models

arXiv:2605.07970v1 Announce Type: cross Abstract: We define susceptibilities as a measure of the response of an observable quantity of a parameterized statistical model to a perturbation of the data f

LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs

arXiv:2605.06915v1 Announce Type: new Abstract: Modern AI systems are being deployed in complex domains such as medicine, science, and law, where it is important that they not only produce correct ans

Lossy Common Information in a Learnable Gray-Wyner Network

arXiv:2601.21424v3 Announce Type: replace-cross Abstract: Many computer vision tasks share substantial overlapping information, yet conventional codecs tend to ignore this, leading to redundant and in

LR-SGS: Robust LiDAR-Reflectance-Guided Salient Gaussian Splatting for Self-Driving Scene Reconstruction

arXiv:2603.12647v2 Announce Type: replace-cross Abstract: Recent 3D Gaussian Splatting (3DGS) methods have demonstrated the feasibility of self-driving scene reconstruction and novel view synthesis. H

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

arXiv:2605.05225v2 Announce Type: replace-cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs) suffer from a significant efficiency bottleneck during Expert Parallelism (EP)

Making AI Evaluation Deployment Relevant Through Context Specification

arXiv:2603.06811v3 Announce Type: replace Abstract: With many organizations struggling to gain value from AI deployments, pressure to evaluate AI in an informed manner has intensified. Status quo AI e

MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weighting

arXiv:2602.20974v2 Announce Type: replace Abstract: In engineering design and scientific computing, computational cost and predictive accuracy are intrinsically coupled. High-fidelity simulations prov

Mean Mode Screaming: Mean--Variance Split Residuals for 1000-Layer Diffusion Transformers

arXiv:2605.06169v1 Announce Type: cross Abstract: Scaling Diffusion Transformers (DiTs) to hundreds of layers introduces a structural vulnerability: networks can enter a silent, mean-dominated collaps

Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors

arXiv:2605.07847v1 Announce Type: new Abstract: As user simulators are increasingly used for interactive training and evaluation of AI assistants, it is essential that they represent the diverse behav

Mechanistic Interpretability with Sparse Autoencoder Neural Operators

arXiv:2509.03738v4 Announce Type: replace-cross Abstract: We introduce sparse autoencoder neural operators (SAE-NOs), a new class of sparse autoencoders that operate in function spaces rather than fix

Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models

arXiv:2605.07721v1 Announce Type: cross Abstract: Recurrent LLM architectures have emerged as a promising approach for improving reasoning, as they enable multi-step computation in the embedding space

Minerva: Reinforcement Learning with Verifiable Rewards for Cyber Threat Intelligence LLMs

arXiv:2602.00513v3 Announce Type: replace Abstract: Cyber threat intelligence (CTI) analysts routinely convert noisy, unstructured security artifacts into standardized, automation-ready representation

Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM

arXiv:2605.05927v2 Announce Type: replace Abstract: Speech large language models (SLMs) are typically built from text large language model (TLM) checkpoints, yet they still suffer from a substantial m

Mixture of Masters: Sparse Chess Language Models with Player Routing

arXiv:2602.04447v2 Announce Type: replace-cross Abstract: Modern chess language models are dense transformers trained on millions of games played by thousands of high-rated individuals. However, these

Motion-o: Trajectory-Grounded Video Reasoning

arXiv:2603.18856v2 Announce Type: replace-cross Abstract: Recent video reasoning models increasingly produce spatio-temporal evidence chains that localize objects at specific timestamps. While these t

Multi-Dimensional Evaluation of LLMs for Grammatical Error Correction

arXiv:2605.07635v1 Announce Type: new Abstract: Automated assistants for Grammatical Error Correction are now embedded in educational platforms serving millions of learners, yet three critical gaps re

Multimodal Diffusion Transformer with Memory Bank for Scalable Long-Duration Talking Video Generation

arXiv:2411.16748v5 Announce Type: replace Abstract: Long-duration talking video synthesis faces enduring challenges in achieving high video quality, portrait consistency, temporal coherence, and compu

Multimodal Latent Reasoning via Hierarchical Visual Cues Injection

arXiv:2602.05359v2 Announce Type: replace Abstract: The advancement of multimodal large language models (MLLMs) has enabled impressive perception capabilities. However, their reasoning process often r

Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer

arXiv:2605.07561v1 Announce Type: new Abstract: Pathological complete response (pCR) is a key prognostic factor in breast cancer patients undergoing neoadjuvant therapy, strongly associated with long-

Multispectral Indices for Wildfire Management

arXiv:2309.01751v3 Announce Type: replace-cross Abstract: The increasing frequency and severity of wildfires necessitates advanced methods for effective surveillance and management, as traditional gro

Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition

arXiv:2605.06884v1 Announce Type: cross Abstract: Most first-order optimizers treat matrix-valued parameters as vectors, ignoring the intrinsic geometry of hidden-layer weights in neural networks. Muo

Neurosymbolic Imitation Learning with Human Guidance: A Privileged Information Approach

arXiv:2605.07166v1 Announce Type: new Abstract: Imitation learning is widely used for learning to act in complex environments. While pure neural-based methods handle high dimensional data effectively,

News organizations are increasingly blocking the Wayback Machine even as their reporters still depend on it 📰 In PRESERVING THE WEB IN THE …

News organizations are increasingly blocking the Wayback Machine even as their reporters still depend on it 📰 In PRESERVING THE WEB IN THE AGE OF AI, Mark Graham, Director of the Wayback Machine at th

researchyann-lecun--x

Normalized Maximum Likelihood Code-Length on Riemannian Data Spaces

arXiv:2508.21466v2 Announce Type: replace Abstract: In recent years, with the large-scale expansion of graph data, there has been an increased focus on Riemannian manifold data spaces other than Eucli

Normalizing Trajectory Models

arXiv:2605.08078v1 Announce Type: new Abstract: Diffusion-based models decompose sampling into many small Gaussian denoising steps -- an assumption that breaks down when generation is compressed to a