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12,457 papers

#machine learning Preprint Open access Oct 2026

BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation

Humanoid household manipulation requires the arms to act while the body balances, steps and changes posture. We present BiGym 2.0, an adaptation of BiGym for the Unitree G1 across 20 household tasks using a unified whole-body controller for demonstration and evaluation. The suite provides 60 native human virtual-realit...

Zexi Zhang, Zecheng Zhu, Zidong Chen et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Recurrent Looped Transformer

State tracking requires an update at every input, but the depth a Transformer applies to each token is fixed regardless of sequence length. We introduce the Recurrent Looped Transformer (RLT), which splits its layers between a parallel causal encoder and a recurrent decoder. At each token, the decoder merges the encode...

Yi-Fan Zhang, Ji-Chen Feng, Shi-Han Qin · 0 citations
#artificial intelligence Preprint Oct 2026

Personal-Agent Mediated Recommendation with Cross-Platform User History

Modern recommendation is shifting from platform-centric personalization toward user-governed personalization, where a personal LLM agent can act on the user's behalf across services. We formalize this emerging paradigm as Personal-Agent Mediated Recommendation: a platform recommender ranks a candidate set using platfor...

Yu Xia, Jiang-Fan Zhang, Jun Xiao et al. · 0 citations
#machine learning Preprint Open access Oct 2026

REViT-v2: Hierarchical Windowed Roto-reflection Equivariant ViT for Equivariant Feature Extraction

We propose a scalable roto-reflection-group-equivariant vision transformer based on windowed group-convolutional self-attention and a hierarchical feature architecture. We demonstrate that our approach can be scaled to group-equivariant vision transformers (ViTs) with millions of parameters and large datasets with prac...

Sheir A. Zaheer, Jihwan Moon, Chan Y. Park · 0 citations
#machine learning Preprint Open access Oct 2026

CETUS: How Far Do Representations Trained on Earth Transfer to Cassini SAR of Titan?

Cassini synthetic aperture radar (SAR) images reveal the dunes, plains, and lake basins of Titan, providing an instance of representations learned from Earth imagery for planetary terrain classification. Cross-domain Evaluation of Earth-to-Titan Transfer Using SAR (CETUS) compares features from DINOv2, DOFA and CROMA w...

Kevin Lee · 0 citations
#machine learning Preprint Open access Oct 2026

Two Vectors Replace In-Context Demos: Structured Task Adaptation via Embeddings

In-context learning (ICL) adapts frozen large multimodal models (LMMs) to new tasks from a few demonstrations (demos), but re-encodes them at every query, where each demo image adds up to hundreds of visual tokens. Demo-free methods remove this cost with a compact task state. However, they add it at locations searched...

Xi Ding, Naichen Shi, Jiawei Zhang · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Seeing the Invisible: Physics-Guided Visual Prompting for Temperature- and Radiation-Aware VLA Navigation

Vision-Language-Action (VLA) models have become a major paradigm for Vision-and-Language Navigation (VLN). However, in safety-critical facilities, invisible risks such as radiation or temperature spikes cannot be detected by an RGB camera, and handling each risk is expensive, requiring a new encoder, new data, and mode...

Hojoon Son, Fan Zhang · 0 citations
#machine learning Preprint Open access Oct 2026

Is $\sqrt{d}$ Separation Necessary for Gradient EM to Learn Gaussian Mixtures in High Dimensions?

Learning Gaussian mixture models (GMMs) using the Expectation-Maximization (EM) algorithm and its gradient-based variants is a fundamental problem in machine learning. It is known that randomly initialized (gradient) EM fails to learn multi-component GMMs in the exact-parameterized setting, where the number of componen...

Yiran Zhang, Mo Zhou, Weihang Xu et al. · 0 citations
#machine learning Preprint Open access Oct 2026

SkillFormer: Skill-Decomposed Adaptation for Audio Language Models

Audio language models must handle dozens of distinct skills, from pitch comparison and speaker counting to musical tempo estimation and emotion recognition. Joint training on all skills at once causes interference: gains on one skill often come at the cost of another. We propose \textbf{SkillFormer}, which decomposes a...

Lee Seung-woo, Bowen Qi, Kim Min-jun et al. · 0 citations
#machine learning Preprint Open access Oct 2026

MobileVISTA: Generative Data Augmentation for Pose Generalization in Mobile Manipulation

Mobile manipulators such as humanoid robots are increasingly deployed in dynamic, unstructured environments to perform dexterous manipulation tasks. However, end-to-end manipulation policies trained to imitate demonstration data collected from a single robot pose are brittle: even centimeter-scale deviations in robot p...

Suzannah Wistreich, Stephen Tian, Isabella Huang et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Two-Sample Testing for Random Graphs without Vertex Correspondence

Two populations of graphs often have to be compared without any correspondence between their vertices, for instance when networks come from different communities, or when a graph generative model is evaluated against held-out graphs. We study how many graphs such an unaligned two-sample test needs, and which graph stat...

Soham Dan · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Does Muon Need Fine-Grained Spectral Shaping?

Muon combines current and past gradients into matrix momentum. For $M=U\Sigma V^\top$, the idealized polar update $Q=UV^\top$ gives every singular direction the same weight. We refer to this as the flat profile. Several recent optimizers replace this flat profile with fine-grained spectral maps that give each direction...

Meher Chaitanya, Tianyi Zhou, Aristides Gionis · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 7, 2026

Discovering the value of humanistic inquiry

Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.

Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

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