Search: “models”

When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models

arXiv:2606.11906v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic varia

11 Jun 2026

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World Pilot: Steering Vision-Language-Action Models with World-Action Priors

DGX agent

arXiv:2606.12403v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models inherit semantic grounding from large-scale pretraining and perform competently across in-distribution manipulation

model-releasesarxiv-cs-ro

11 Jun 2026

Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination

model-releasesarxiv-cs-ro

arXiv:2606.10040v1 Announce Type: new Abstract: World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. Howeve

Tutorials

Entropy, Disagreement, and the Limits of Foundation Models in Genomics

arXiv:2604.04287v2 Announce Type: replace-cross Abstract: Foundation models in genomics have shown mixed success compared to their counterparts in natural language processing. Yet, the reasons for the

tutorialsarxiv-cs-cl

FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model

arXiv:2606.11106v1 Announce Type: cross Abstract: A global shortage of trained sonographers limits prenatal ultrasound screening in low- and middle-income countries, where over half of pregnant women

Safety

Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction

arXiv:2606.10571v1 Announce Type: cross Abstract: Adversarial examples reveal vulnerabilities in Vision-Language Pre-training (VLP) models and provide insights for improving robustness. A key property

safetyarxiv-cs-ai

P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structural Reasoning

arXiv:2606.11152v1 Announce Type: new Abstract: Multimodal large language models can write code to produce complex programs as well as use programs to do 3D modeling, which opens up a new avenue for 3

Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models

arXiv:2606.10949v1 Announce Type: new Abstract: Persistent memory systems promise to make LLMs more helpful by storing user beliefs over time. We show they also make models less correct by systematica

SARM2: Multi-Task Stage Aware Reward Modeling for Self Improving Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.10305v1 Announce Type: new Abstract: Fine-tuning vision-language-action (VLA) policies for long-horizon manipulation still relies heavily on behavior cloning, which requires costly high-qua

The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models

arXiv:2606.11082v1 Announce Type: new Abstract: This study investigates cross-lingual distributional skew (the Shibboleth Effect) in frontier large language models (LLMs) subjected to sustained advers

Are Reasoning Vision-Language Models Robust to Semantic Visual Distractions?

arXiv:2606.08894v1 Announce Type: new Abstract: Reasoning Vision-Language Models (VLMs) achieve strong performance on complex multimodal tasks, but reliable real-world application requires handling vi

Bokeh Diffusion: Defocus Blur Control in Text-to-Image Diffusion Models

arXiv:2503.08434v5 Announce Type: replace-cross Abstract: Recent advances in large-scale text-to-image models have revolutionized creative fields by generating visually captivating outputs from textua

BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference

arXiv:2606.09514v1 Announce Type: new Abstract: Large language models (LLMs) incur high inference cost due to their depth and parameter scale. Depth pruning can reduce latency by skipping redundant Tr

Safety

CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs

arXiv:2606.08420v1 Announce Type: new Abstract: Vision-language models (VLMs) pretrained on large-scale image-text pairs demonstrate strong image-level understanding, but are primarily optimized for g

safetyarxiv-cs-cv

Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets

arXiv:2606.08497v1 Announce Type: new Abstract: As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes param

GlobeAudio: A Multilingual Multicultural Benchmark for Naturalistic Evaluation of Large Audio-Language Models

arXiv:2606.08194v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) integrate audio perception and language understanding within a unified framework, enabling a wide range of real-wo

HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning

arXiv:2606.07621v1 Announce Type: cross Abstract: Edge services increasingly use federated learning to personalize on-device models while keeping sensitive data local. In practice, deployments must ha

Applications

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

arXiv:2310.10196v3 Announce Type: replace-cross Abstract: Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physi

applicationsarxiv-cs-ai

Phase transition in large language models and the criticality of natural languages

arXiv:2406.05335v3 Announce Type: replace-cross Abstract: Generation of text and speech in natural languages can be modeled as a stochastic process. This idea dates back to the seminal work of Markov

TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models

arXiv:2510.27544v2 Announce Type: replace Abstract: Temporal reasoning involves understanding how systems evolve over time through input-driven state transitions. A key aspect is temporal causal reaso

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning

arXiv:2601.04805v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have attracted much attention due to their exceptional performance. However, their performance mainly stems from think

A Four-Condition Diagnostic Protocol for Evidence Utilization in Long-Context and Retrieval-Augmented Language Models

arXiv:2606.06758v1 Announce Type: new Abstract: Final-answer accuracy, retrieval recall, and citation overlap do not by themselves identify whether a long-context or retrieval-augmented language model

Local Ai

CULTURESCORE: Evaluating Cultural Faithfulness in Video Generation Models

arXiv:2606.07311v1 Announce Type: cross Abstract: As video generation models like Veo 3.1 and LTX-2 advance, their ability to accurately represent diverse global cultures remains a critical yet unders

local-aiarxiv-cs-ai

Safety

Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation

arXiv:2606.06712v1 Announce Type: cross Abstract: We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs). Rather than pretraining from scratch, prior work r

safetyarxiv-cs-ai

Local Ai

Generalization of Diffusion Models Arises with a Balanced Representation Space

arXiv:2512.20963v3 Announce Type: replace-cross Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training object

local-aiarxiv-cs-cv

Rethinking Genomic Modeling Through Optical Character Recognition

arXiv:2602.02014v2 Announce Type: replace-cross Abstract: Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However,

researcharxiv-cs-ai

ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets

arXiv:2606.06717v1 Announce Type: cross Abstract: While generative AI models have demonstrated remarkable success in structure-based drug design, they predominantly rely on deep binding pockets and st

CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model

arXiv:2606.06099v1 Announce Type: new Abstract: Whether Large Language Models (LLMs) exhibit covert psychological manipulation in complex human-AI interactions has garnered increasing safety concerns.

6 Jun 2026

AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints

arXiv:2606.05622v1 Announce Type: new Abstract: Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are pro

CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distraction

arXiv:2606.05799v1 Announce Type: cross Abstract: Existing calibration methods for Large Language Models (LLMs) often overlook a critical dimension of trustworthiness: a model's {em behavioral robustn

researcharxiv-cs-cl

Decomposing Factual Sycophancy in Language Models: How Size and Instruction Tuning Shape Robustness

arXiv:2606.06306v1 Announce Type: new Abstract: Factual sycophancy occurs when a language model abandons a correct, verifiable answer under social pressure. Because a flip occurs only when pressure to

researcharxiv-cs-cl

Local Ai

Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations

arXiv:2510.17256v2 Announce Type: replace Abstract: Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a

local-aiarxiv-cs-cl

FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery

arXiv:2602.19190v4 Announce Type: replace Abstract: Research on the intelligent interpretation of all-weather, all-time Synthetic Aperture Radar (SAR) is crucial for advancing remote sensing applicati

Less is MoE: Trimming Experts in Domain-Specialist Language Models

arXiv:2606.05538v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models achieve strong performance through conditional computation, but their large parameter footprint poses deployment chall

Seeing Time: Benchmarking Chronological Reasoning and Shortcut Biases in Vision-Language Models

arXiv:2606.05702v1 Announce Type: cross Abstract: Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capaci

The Tell-Tale Norm: ell_2 Magnitude as a Signal for Reasoning Dynamics in Large Language Models

arXiv:2606.06188v1 Announce Type: new Abstract: Recent work has sought to understand Large Language Models (LLMs) reasoning, yet a principled, model-intrinsic signal that captures its layer-wise reaso

researcharxiv-cs-cl

A Latent Variable Framework for Scaling Laws in Large Language Models

arXiv:2512.06553v2 Announce Type: replace-cross Abstract: We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated b

researcharxiv-cs-lg

Applications

An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization

arXiv:2606.04408v1 Announce Type: cross Abstract: High-dimensional and incomplete (HDI) data are prevalent in many real-world big data scenarios. Latent factor models serve as a common representation

applicationsarxiv-cs-ai

Audio Interaction Model

arXiv:2606.05121v1 Announce Type: cross Abstract: Audio is an inherently interactive modality, yet today's Large Audio Language Models (LALMs) are offline, and streaming audio models each handle only

researcharxiv-cs-ai

Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models

arXiv:2606.04109v1 Announce Type: new Abstract: Context-augmented language model systems often wrap supplied content with labels such as Reference:, Evidence:, Instruction:, Note:, or Example:, but th

Food-R1: A Unified Multi-Task Food Vision-Language Model with Reinforcement Learning

arXiv:2606.04986v1 Announce Type: new Abstract: Recent studies have explored Vision-Language Models (VLMs) for food analysis. However, most existing methods rely primarily on supervised fine-tuning (S

Interfaze: The Future of AI is built on Task-Specific Small Models

arXiv:2602.04101v2 Announce Type: replace Abstract: We present Interfaze, a native hybrid model that fuses task-specific deep neural networks (CNNs and DNNs) directly into a transformer decoder throug

LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models

arXiv:2606.04485v1 Announce Type: new Abstract: Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar toke

LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling

arXiv:2606.04438v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) and looped architectures scale models along two orthogonal axes, namely parameter capacity and effective depth. However, main

QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy

arXiv:2606.04620v1 Announce Type: cross Abstract: LLMs have become the state-of-the-art algorithms for solving NLP tasks. However, they typically come at huge computational and memory costs, thus maki

SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling

arXiv:2606.04000v1 Announce Type: cross Abstract: We present a probabilistic modeling framework for incorporating small-scale spatial heterogeneity into macroscopic descriptions of material behavior f

Applications

Towards Evaluating the Robustness of Visual State Space Models

arXiv:2406.09407v3 Announce Type: replace Abstract: Vision State Space Models (VSSMs), a novel architecture that combines the strengths of recurrent neural networks and latent variable models, have de

applicationsarxiv-cs-cv

UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD

arXiv:2606.05058v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models. However, CAD resea