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16,109 papers

#machine learning Preprint Open access Oct 2026

JitterVLA: A Replay Benchmark for Motion Quality in VLA-Driven Human-Robot Manipulation

Vision-language-action (VLA) policies issue sparse targets whose arrival at the robot controller is delayed and irregular, so a reasonable predicted action can still produce abrupt motion near people. Remedies are evaluated on different streams, delays and metrics. JitterVLA replays identical commands and arrival trace...

Faith Ogunwoye, Didar Seyidov, Konstantin Gubernatorov et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Kernel-based External Validity of Experimental Studies

The external validity of a study is the extent to which its findings generalize to unseen experimental conditions, such as datasets. We formalize the results of a study together with its research question, and define external validity as the probability that two independent and same-sized replications of the study prod...

Federico Matteucci, Gerhard Neumann · 0 citations
#machine learning Preprint Open access Oct 2026

Some remarks on the connection between generative diffusion and optimal transport

We discuss three simple connections between generative diffusion and optimal transport. First, the joint law of the data and of their Ornstein-Uhlenbeck noising is, at every time, the solution of an entropic optimal-transport problem, at a temperature set by the noise level. Backward diffusion can consequently be read...

Giulio Biroli, Marc M\'ezard · 0 citations
#machine learning Preprint Open access Oct 2026

Vocal Intensity Conversion with Continuous SPL Control via Adversarial Training

Vocal intensity conversion renders a recorded utterance at a different vocal intensity, rather than simply scaling its amplitude, while preserving its content and speaker identity. Existing systems control intensity through a reference utterance or a few discrete categories. Instead, we control the conversion with any...

Quentin Le Tellier, Albert Rilliard, Olivier Perrotin et al. · 0 citations
#machine learning Preprint Oct 2026

CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope

Correlation matrices combine nonlinear positive-definiteness constraints with a simultaneous row-column permutation symmetry. Their generation therefore involves two distinct problems. First, every output must remain on the correlation elliptope, irrespective of how its variables are ordered. Second, the generator must...

G. Marti · 0 citations
#machine learning Preprint Oct 2026

Lower Bounds for Randomized Linear-Oracle Optimization over Strongly Convex Sets

Recent results [arXiv:2602.04378, arXiv:2602.22608] have established worst-case objective-error lower bounds of order $1/T^2$ after $T$ linear minimization oracle (LMO) calls, matching the upper bound [arXiv:1406.1305] in its dependence on $T$ and thereby resolving a long-standing open question. Upon close inspection,...

Sebastian Pokutta · 0 citations
#machine learning Preprint Open access Oct 2026

Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure

Uncertainty quantification for electric vehicle (EV) charging demand is essential to turn the increasing pressure of EV adoption on power systems into a source of flexibility. We study a large-scale dataset of public charging stations in Scotland, where demand exhibits a hierarchical structure, organized across multipl...

Guillaume Principato, Yvenn Amara-Ouali, Yannig Goude et al. · 0 citations
#machine learning Preprint Open access Oct 2026

RARA: Robust Policy Fine-Tuning through Retrieval-Anchored Representation Alignment for Robotic Manipulation

Pre-trained robot policies, such as Vision-Language-Action (VLA) models, provide a strong foundation for generalist robotic manipulation, but adapting them to new tasks with only a few demonstrations remains challenging. Directly fine-tuning a pre-trained policy often achieves strong in-distribution performance, yet it...

Xuhui Kang, Sung-Wook Lee, Yan Zhuang et al. · 0 citations
#machine learning Preprint Oct 2026

S-Space: Exploring Spatial Workspace in Multimodal Language Models

Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood. We uncover S-Space, a linear representation subspace in which object-token representations encode continuous coordinates along horizontal, vertical, and distance axe...

Zhi-Yuan Shi, Xiang Li, Xing-Zhuo Guo et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Asymptotic Analysis of Discrete-Time Self-Attention Dynamics in Transformers

In machine learning, a self-attention dynamics describes the attention mechanism of transformers, and is obtained treating the layer index of a transformer as time index. Even though naturally discrete-time, this dynamical system is most often studied in continuous-time. Scope of this paper is to provide a thorough inv...

Claudio Altafini · 0 citations
#machine learning Preprint Oct 2026

A Scalable Learned Iterative Solver for Nonlinear MPC via Temporal Decomposition

Nonlinear model predictive control (NMPC) can be applied to large nonlinear systems with complex objectives while enforcing constraints, but the cost of solving the underlying optimization problems limits real-time application for systems with fast dynamics or many states. Neural networks can reduce this online cost by...

Lukas Lüken, Sergio Lucia · 0 citations
#machine learning Preprint Open access Oct 2026

Harmonic analysis and circuit approximation of Sobolev functions on quantum input spaces

We derive approximation rates for Sobolev functions defined on the complex unit sphere and complex projective space, representing quantum states, and on the special and projective unitary groups, representing quantum unitary operations. By using harmonic analysis on compact groups and compact homogeneous spaces, we cha...

Philipp Schmocker · 0 citations

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MIT News · Artificial Intelligence Oct 28, 2026

Making machines that can listen

Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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