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arXiv:2605.26933v1 Announce Type: new Abstract: Unsupervised visual object tracking is a challenging task that requires following arbitrary targets in videos without training on ground-truth annotatio

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Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study

DGX agent

arXiv:2502.06567v2 Announce Type: replace-cross Abstract: Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance lev

applicationsarxiv-cs-lg

27 May 2026

MVISTA-4D: View-Consistent 4D World Model with Test-Time Action Inference for Robotic Manipulation

arXiv:2602.09878v2 Announce Type: replace Abstract: World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely im

safetyarxiv-cs-cv

Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models

arXiv:2603.16654v2 Announce Type: replace-cross Abstract: Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, espec

Agents

Real-Time Progress Prediction in Reasoning Language Models

arXiv:2506.23274v4 Announce Type: replace-cross Abstract: Recent reasoning language models, particularly those that employ long latent chains of thought, achieve strong performance on complex agentic

agentsarxiv-cs-ai

The Stability of Singular Distribution: A Spectral Perspective on the Two-Phase Dynamics of Language Model Pre-training

arXiv:2605.26489v1 Announce Type: new Abstract: Large language model pre-training typically exhibits a two-phase trajectory: a fast initial loss drop followed by a prolonged slow improvement. We ident

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models

arXiv:2510.17759v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) extend large language models with visual reasoning, but their multimodal design also introduces new, underexplor

safetyarxiv-cs-cl

Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors

arXiv:2605.23938v1 Announce Type: new Abstract: Large language models (LLMs) increasingly fuse heterogeneous inputs in ubiquitous systems. Yet, how LLMs implicitly allocate authority when sensor measu

safetyarxiv-cs-ai

Agents

Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations

arXiv:2605.25620v1 Announce Type: new Abstract: World models enable agents to predict future dynamics conditioned on actions, making the choice of latent representation central to planning and control

agentsarxiv-cs-ai

BandVQ: Band-Wise Vector-Quantized EEG Foundation Model

arXiv:2605.24921v1 Announce Type: new Abstract: A central challenge in electroencephalography (EEG) foundation modeling is learning transferable representations across recordings with diverse tasks, m

Bayesian Distributional Models of Executive Functioning

arXiv:2510.00387v3 Announce Type: replace Abstract: This study uses controlled simulations with known ground-truth parameters to evaluate how Distributional Latent Variable Models (DLVM) and Bayesian

Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models

arXiv:2605.24053v1 Announce Type: new Abstract: Large Language Models (LLMs) are predominantly governed by probabilistic frameworks in which the sum of outcome probabilities is constrained to unity. T

Local Ai

Divide-and-Conquer Inference for Large-Scale Visual Recognition with Multimodal Large Language Models

arXiv:2605.24799v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities across a wide range of vision language tasks. However, when applied to

local-aiarxiv-cs-ai

Fine-Tuning Language Models to Know What They Know

arXiv:2602.02605v2 Announce Type: replace-cross Abstract: Evaluating true metacognition in Large Language Models (LLMs) is difficult due to biases and heuristics. This paper presents a framework to me

Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion Model

arXiv:2412.07333v2 Announce Type: replace-cross Abstract: Pose-Guided Person Image Synthesis (PGPIS) aims to generate human images in specified poses while preserving the identity and appearance of a

Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty

arXiv:2605.26093v1 Announce Type: new Abstract: Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical setti

Hylos: Operability Contracts for Model-Native Spatial Intelligence

arXiv:2605.24728v1 Announce Type: new Abstract: Foundation models can increasingly describe, reconstruct, and generate 3D objects, assemblies, scenes, and environments, but visually plausible spatial

MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models

arXiv:2605.25342v1 Announce Type: new Abstract: Aligning large language models (LLMs) with diverse and multifaceted user preferences is a fundamental challenge in personalized AI systems. Existing mul

safetyarxiv-cs-cl

Merge-Bench: Resolve Merge Conflicts with Large Language Models

arXiv:2605.25890v1 Announce Type: new Abstract: This paper applies machine learning to the difficult and important task of version control merging. (1) We constructed a dataset, Merge-Bench, of 7938 r

MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing

arXiv:2605.24973v1 Announce Type: cross Abstract: VLM-based OCR models have become the de facto choice for document parsing, as they can accurately extract page-level elements (e.g., paragraphs within

On the Limits of Model Merging for Multilinguality in Pre-Training

arXiv:2605.25846v1 Announce Type: new Abstract: Endowing models with consistent multilingual performance can be achieved by mixing pre-training data, or post-training approaches such as language-speci

researcharxiv-cs-cl

PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization

arXiv:2605.06505v2 Announce Type: replace-cross Abstract: We introduce PACZero, a family of PAC-private zeroth-order mechanisms for fine-tuning large language models that delivers usable utility at I(

PairFlow: Closed-Form Source-Target Coupling for Few-Step Generation in Discrete Flow Models

arXiv:2512.20063v3 Announce Type: replace Abstract: We introduce exttt{PairFlow}, a lightweight preprocessing step for training Discrete Flow Models (DFMs) to achieve few-step sampling without requiri

Reinforcement Learning for Laser Additive Manufacturing Scan-Order Optimisation: A Bilevel Proxy--FEA Diagnostic Framework for Reward and World-Model Diagnosis

arXiv:2605.25063v1 Announce Type: new Abstract: Reinforcement learning offers a promising approach for scan-order optimisation in laser additive manufacturing, where sequential scan decisions critical

Benchmarking Gaslighting Attacks Against Speech Large Language Models

arXiv:2509.19858v2 Announce Type: replace Abstract: As Speech Large Language Models (Speech LLMs) become increasingly integrated into voice-based applications, ensuring their robustness against manipu

researcharxiv-cs-cl

Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems

arXiv:2605.23618v1 Announce Type: new Abstract: We benchmark Google Embeddings (GE2), a Vertex-AI-hosted bi-encoder with 2,048-token context and explicit task-type conditioning, against five open-sour

ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models

arXiv:2605.23694v1 Announce Type: new Abstract: Chart descriptions are essential for accessibility, cross-modal retrieval, and assisting readers in extracting insights from complex visualizations. As

DFKI-MLT at SemEval-2026 TASK 7: Steering Multilingual Models Towards Cultural Knowledge

arXiv:2605.23069v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used across diverse linguistic and cultural contexts, yet their cultural knowledge remains uneven across r

Efficient One-Step Diffusion Restoration Model with Compact Token Compression and Linear Attention

arXiv:2605.23451v1 Announce Type: new Abstract: Real-world image super-resolution aims to recover high-quality images from complex and unknown real-world degradations. However, existing generative Rea

Hybrid Quantum-Classical Corrective Diffusion Modeling for Meteorological Downscaling

arXiv:2605.23403v1 Announce Type: new Abstract: Statistical downscaling is a crucial component of the weather modeling field, where high-resolution outputs must be reconstructed from coarse-resolution

Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most

arXiv:2605.22672v2 Announce Type: replace Abstract: We document inverse scaling in LLMs on forecasting problems whose underlying time series exhibit superlinear growth and tail risk of regime change,

Agents

MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination

arXiv:2605.22949v1 Announce Type: new Abstract: Foundation model agents increasingly operate in multi-agent deployments where a coordinator must decide which agent's response to trust. The standard ap

agentsarxiv-cs-lg

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models

arXiv:2406.09250v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically de

Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling

arXiv:2602.20210v2 Announce Type: replace-cross Abstract: Crystal modeling spans a family of conditional and unconditional generation tasks, including crystal structure prediction (CSP) and de novo ge

Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling

arXiv:2603.01655v2 Announce Type: replace Abstract: Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of c

safetyarxiv-cs-lg

Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction

arXiv:2605.21499v1 Announce Type: cross Abstract: Grid-based neural networks such as convolutional autoencoders are widely used in dimension reduction-based surrogate models for computational fluid dy

Interpreting and Steering State-Space Models via Activation Subspace Bottlenecks

arXiv:2602.22719v2 Announce Type: replace Abstract: State-space models (SSMs) have emerged as an efficient strategy for building powerful language models, avoiding the quadratic complexity of computin

SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis

arXiv:2605.22776v1 Announce Type: new Abstract: Survival analysis aims to estimate a time-to-event distribution from data with censored observations. Many existing methods either impose structural ass

Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation

arXiv:2605.22765v1 Announce Type: new Abstract: Discrete diffusion models are often trained through clean-data prediction, but the prediction can be used in different ways to define the reverse dynami

Discovering Implicit Large Language Model Alignment Objectives

arXiv:2602.15338v2 Announce Type: replace-cross Abstract: Large language model (LLM) alignment relies on complex reward signals that often obscure the specific behaviors being incentivized, creating c

safetyarxiv-cs-cl

Do Factual Recall Mechanisms Carry over from Text to Speech in Multimodal Language Models?

arXiv:2605.22170v1 Announce Type: new Abstract: In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented. The question then emerges abo

researcharxiv-cs-cl

Evaluating Clinical Competencies of Large Language Models with a General Practice Benchmark

arXiv:2503.17599v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated considerable potential in general practice. However, existing benchmarks and evaluation frameworks pr

Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly

arXiv:2605.21625v1 Announce Type: cross Abstract: The emergence of Large Vision-Language Models (LVLMs) has significantly advanced video understanding capabilities. However, existing benchmarks focus

Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models

arXiv:2605.21861v1 Announce Type: new Abstract: Multi-modality medical vision (MV) foundation models (FM) are fundamentally challenged by pronounced Non-IID feature statistics across heterogeneous ima

Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure

arXiv:2605.22591v1 Announce Type: new Abstract: Frozen Vision Foundation Models (VFMs) with lightweight classification heads are increasingly used in medical imaging because they offer efficient and r

Towards Selection of Large Multimodal Models as Engines for Burned-in Protected Health Information Detection in Medical Images

arXiv:2511.02014v2 Announce Type: replace Abstract: The detection of Protected Health Information (PHI) in medical imaging is critical for safeguarding patient privacy and ensuring compliance with reg

Transcription and Recognition of Italian Parliamentary Speeches Using Vision-Language Models

arXiv:2603.28103v2 Announce Type: replace-cross Abstract: Parliamentary proceedings represent a rich yet challenging resource for computational analysis, particularly when preserved only as scanned hi

A Mechanistic Study of Tabular Foundation Models

arXiv:2605.21288v1 Announce Type: new Abstract: Tabular foundation models with different architectures converge in accuracy across a range of classification and regression tasks. This raises questions