Search: “models”

Does Visual Information Play a Decisive Role in Vision-Language-Action Model Driving Behavior?

arXiv:2605.31041v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated promising capability in autonomous driving, highlighting the potential of unified multimodal arc

Research

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training

DGX agent

arXiv:2512.13996v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts architectures are essential for scaling model capacity efficiently, yet the standard Top-k routing imposes a rigid sparsit

researcharxiv-cs-ai

1 Jun 2026

Local Ai

Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation

arXiv:2509.25906v2 Announce Type: replace Abstract: Federated Learning (FL) often adopts differential privacy (DP) to protect client data, but the added noise required for privacy guarantees can subst

local-aiarxiv-cs-lg

Applications

FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment

arXiv:2602.02680v2 Announce Type: replace Abstract: The growing scale of deep neural networks, encompassing large language models (LLMs) and vision transformers (ViTs), has made training from scratch

applicationsarxiv-cs-lg

Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error

arXiv:2605.31438v1 Announce Type: new Abstract: Time series forecasting often requires learning nonlinear and time-delayed dependencies. A paradigmatic class of forecasting models are nonlinear vector

Generalized Intention Modeling in Multi-Agent Reinforcement Learning

arXiv:2605.31318v1 Announce Type: new Abstract: Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learn

agentsarxiv-cs-lg

HARP-VLA: Human-Robot Aligned Representation Learning for Vision-Language-Action Model

arXiv:2605.31234v1 Announce Type: new Abstract: Learning generalizable vision-language-action (VLA) models from large-scale human videos is promising but challenging due to cross-embodiment discrepanc

safetyarxiv-cs-ro

How can embedding models bind concepts?

arXiv:2605.31503v1 Announce Type: new Abstract: Humans easily determine which color belongs to which shape in multi-object scenes, an ability known as concept binding. Vision-language embedding models

tutorialsarxiv-cs-cv

Human-Alignment, Calibration, and Activation Patterns in Large Language Model Uncertainty

arXiv:2605.30675v1 Announce Type: cross Abstract: Uncertainty Quantification is a large and growing subfield of large language model behavioral analysis. Primarily to recognize and combat hallucinatio

Inferring Events from Time Series using Language Models

arXiv:2503.14190v3 Announce Type: replace Abstract: A common goal in analyzing time series data is to understand how events cause observed variations. We study whether Large Language Models (LLMs) can

Tools

Introducing Mellum2: A 12B Mixture-of-Experts Model by JetBrains

Mellum2 is a 12-billion parameter Mixture-of-Experts language model developed by JetBrains, designed to efficiently process complex tasks by selectively activating different expert networks. The model

toolshugging-face

Local Ai

Light Interaction: Training-Free Inference Acceleration for Interactive Video World Models

arXiv:2605.31158v1 Announce Type: new Abstract: Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time ga

local-aiarxiv-cs-cv

Open models!

Open models! Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency

agentsharrison-chase--x

Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration

arXiv:2605.31196v1 Announce Type: cross Abstract: Safe human--robot collaboration requires more than visual description: a monitor must determine whether the robot body is safely separated, already co

Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks

arXiv:2605.31106v1 Announce Type: new Abstract: Riemannian diffusion models generalize score-based generative modeling to manifold-supported data via stochastic diffusion equations on the manifold. Ho

tutorialsarxiv-cs-lg

Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents

arXiv:2605.30723v1 Announce Type: new Abstract: LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time-to improve performance on long-horizon int

safetyarxiv-cs-cl

SPM-Bench: Benchmarking Large Language Models for Scanning Probe Microscopy

arXiv:2602.22971v2 Announce Type: replace Abstract: As LLMs achieved breakthroughs in general reasoning, their proficiency in specialized scientific domains reveals pronounced gaps in existing benchma

The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning

arXiv:2605.31404v1 Announce Type: cross Abstract: Large Language Model (LLM)-based navigation systems commonly construct explicit spatial representations (e.g., topological graphs, semantic raster map

Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling

arXiv:2605.30376v1 Announce Type: cross Abstract: Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical i

Updating the standard neuron model in artificial neural networks

arXiv:2605.30370v1 Announce Type: cross Abstract: From their inception in the 1950s, artificial neural networks (ANNs) started using the so-called point neuron model then prevalent in neuroscience, ho

Local Ai

Vector Linking via Cross-Model Local Isometric Consistency

arXiv:2605.31100v1 Announce Type: new Abstract: We study Vector Linking: given two embedding clouds produced by different black-box encoders over partially overlapping datasets, recover cross-model ob

local-aiarxiv-cs-ai

Why Video Agent models are next — Ethan He, xAI Grok Imagine

Video agent models represent the next frontier in AI by extending language model capabilities to process, understand, and act on video content in real-time, enabling autonomous agents to perceive and

agentslatent-space

The Top AI Papers of the Week (May 24 - May 31) - SkillOpt - AutoScientists - The Efficiency Frontier - Language Models Need Sleep - Adaptin…

The Top AI Papers of the Week (May 24 - May 31) - SkillOpt - AutoScientists - The Efficiency Frontier - Language Models Need Sleep - Adapting the Interface, Not the Model - Forecasting Scientific Prog

agentsdair-ai--x

31 May 2026

A comparative study of transformer-based embeddings for topic coherence

arXiv:2605.28832v1 Announce Type: cross Abstract: Topic modeling is a branch of Natural Language Processing (NLP) that aims to organize large collections of texts into coherent groups according to wor

Archon: A Unified Multimodal Model for Holistic Digital Human Generation

arXiv:2605.30311v1 Announce Type: cross Abstract: Digital humans are fundamental to immersive interaction, yet creating a unified model for holistic modalities, including text, audio, motion, and visu

Applications

BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control

arXiv:2605.29849v1 Announce Type: cross Abstract: Machine learning (ML) is increasingly used for data-driven modeling of buildings to enable downstream tasks such as fault detection and diagnosis, and

applicationsarxiv-cs-lg

Calibrating Generative Models to Distributional Constraints

arXiv:2510.10020v4 Announce Type: replace-cross Abstract: Generative models frequently suffer miscalibration, wherein statistics of the sampling distribution, such as the fraction of generations in a

Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions

arXiv:2605.30153v1 Announce Type: cross Abstract: Score-based diffusion models have demonstrated remarkable empirical success in learning high-dimensional distributions, particularly those exhibiting

FinVerBench: Benchmark Validity and Calibration in Large Language Model Financial Statement Verification

arXiv:2605.29586v1 Announce Type: new Abstract: We introduce FinVerBench, a benchmark and validity study for financial statement verification: determining whether a set of corporate financial statemen

Learning to Feel Materials from Multisensory Tactile Data via Interpretable Models

arXiv:2605.29572v1 Announce Type: new Abstract: Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual r

tutorialsarxiv-cs-ro

Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance

arXiv:2411.14279v2 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. However, despite showing promising per

safetyarxiv-cs-cl

Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models

arXiv:2601.14758v4 Announce Type: replace-cross Abstract: Post-training pretrained autoregressive models (ARMs) into masked diffusion models (MDMs) has emerged as a cost-effective way to overcome the

Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers

arXiv:2601.22139v2 Announce Type: replace-cross Abstract: Reasoning-oriented Large Language Models (LLMs) have achieved remarkable progress with Chain-of-Thought (CoT) prompting, yet they remain funda

Same Evidence, Different Answers: Canonical-Context On-Policy Distillation for Multi-Turn Language Models

arXiv:2605.30251v1 Announce Type: cross Abstract: Large language models (LLMs) often solve a task when all instructions are given in a single prompt, but fail when the same information is revealed gra

The Cognitive Categorical Transformer: Category-Theoretic Inductive Biases for Language Modeling

arXiv:2605.28864v1 Announce Type: new Abstract: The Cognitive Categorical Transformer (CCT) is a 306M-parameter architecture that augments a pretrained GPT-2 Small backbone with cognitively grounded c

Thoughts-as-Planning: Latent World Models for Chain-of-Thoughts Optimization via Reinforcement Planning

arXiv:2605.28842v1 Announce Type: cross Abstract: The success of large language models (LLMs) across diverse NLP tasks has elevated the importance of reasoning chain optimization as a critical step in

tutorialsarxiv-cs-ai

Unlocking the Working Memory of Large Language Models for Latent Reasoning

arXiv:2605.30343v1 Announce Type: cross Abstract: To improve the reasoning capabilities of large language models, test-time compute is typically scaled by generating intermediate tokens before the fin

Unveiling the Visual Counting Bottleneck in Vision-Language Models

arXiv:2605.30170v1 Announce Type: cross Abstract: While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visu

researcharxiv-cs-cv

WaterSearch: A Quality-Aware Search-based Watermarking Framework for Large Language Models

arXiv:2512.00837v2 Announce Type: replace Abstract: Watermarking acts as a critical safeguard in text generated by Large Language Models (LLMs). By embedding identifiable signals into model outputs, w

researcharxiv-cs-cl

.@ylecun’s definition of what is a world model.

Yann LeCun, a pioneering AI researcher, provides his definition of what constitutes a world model in this X post. A world model is an AI system's internal representation of how the physical world work

researchyann-lecun--x

Benchmarking and Mechanistic Analysis of Vision-Language Models for Cross-Depiction Assembly Instruction Alignment

model-releasesarxiv-cs-cl

arXiv:2604.00913v2 Announce Type: replace-cross Abstract: 2D assembly diagrams are often abstract and hard to follow, creating a need for intelligent assistants that can monitor progress, detect error

Beyond Binary Moral Judgment: Modeling Ethical Pluralism in AI

arXiv:2605.28707v1 Announce Type: new Abstract: Critical decision-making in socially consequential spaces is increasingly involving AI systems at varying capacities. Yet, despite the ubiquity of auton

CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models

arXiv:2605.28792v1 Announce Type: new Abstract: Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to mu

Evaluating the Generation Capabilities of Large Chinese Language Models

arXiv:2308.04823v5 Announce Type: replace Abstract: This paper unveils CG-Eval, the first-ever comprehensive and automated evaluation framework designed for assessing the generative capabilities of la

researcharxiv-cs-cl

Explaining Digital Pathology Models via Clustering Activations

arXiv:2511.14558v2 Announce Type: replace Abstract: We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used met

researcharxiv-cs-cv

Neural Weight Compression for Language Models

arXiv:2510.11234v3 Announce Type: replace Abstract: Efficient compression of language model weights is increasingly critical as model scale and deployment grow. Yet, most existing methods rely on hand

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

model-releasesarxiv-cs-lg

arXiv:2605.27527v1 Announce Type: cross Abstract: Astrophysical observations taken from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light cur

Regression Language Models for Code

arXiv:2509.26476v2 Announce Type: replace-cross Abstract: We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of program