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Latent Action Control for Reasoning-Guided Unified Image Generation

arXiv:2605.16961v1 Announce Type: cross Abstract: Unified multimodal models can encode visual understanding and image generation within a shared backbone, yet understanding does not automatically tran

LatentUMM: Dual Latent Alignment for Unified Multimodal Models

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arXiv:2605.17766v1 Announce Type: new Abstract: Unified multimodal models (UMMs) achieve strong performance in both understanding and generation by learning a shared latent space, yet they often exhib

safetyarxiv-cs-cv

19 May 2026

Learning Fill-in Reduction Ordering via Graph Policy Optimization for Sparse Matrices

arXiv:2605.17362v1 Announce Type: new Abstract: Matrix reordering in large sparse solvers seeks a permutation that minimizes factorization fill-in to reduce memory and computation. Because the minimum

Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning

arXiv:2605.17058v1 Announce Type: new Abstract: The combination of exponentially large action spaces, stochastic dynamics, and long-horizon decision-making under limited resources makes Sequential Sto

Learning Native Continuation for Action Chunking Flow Policies

arXiv:2602.12978v2 Announce Type: replace-cross Abstract: Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at

Learning Relative Representations for Fine-Grained Multimodal Alignment with Limited Data

arXiv:2605.16834v1 Announce Type: cross Abstract: Multimodal pre-training demonstrates strong generalization performance, but this paradigm is often impractical in domains where paired data are scarce

Learning to Reason without External Rewards

arXiv:2505.19590v5 Announce Type: replace-cross Abstract: Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited

Learning Transferable Topology Priors for Multi-Agent LLM Collaboration Across Domains

arXiv:2605.17359v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have shown strong potential for complex reasoning by coordinating specialized agents through struct

Learning under Distributional Drift: Prequential Reproducibility as an Intrinsic Statistical Resource

arXiv:2512.13506v4 Announce Type: replace Abstract: Statistical learning under distributional drift remains poorly characterized, especially in closed-loop settings where learning alters the data-gene

LegalCheck: Retrieval- and Context-Augmented Generation for Drafting Municipal Legal Advice Letters

arXiv:2605.12012v2 Announce Type: replace Abstract: Public-sector legal departments in the Netherlands face acute staff shortages, increased case volumes, and increased pressure to meet regulatory com

Linguistic Uncertainty and Reply Engagement on X: A Cross-Domain Replication of the Uncertainty-Reply Asymmetry

arXiv:2605.16289v1 Announce Type: cross Abstract: Linguistic uncertainty is common in social media, but its relationship with engagement remains unclear across languages and topics. Using 2,258 Englis

LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection

arXiv:2510.25799v2 Announce Type: replace Abstract: Human experts often struggle to select the best option from a large set of items with multiple competing objectives, a process bottlenecked by the d

Mamba-VGGT: Persistent Long-Sequence Video Geometry Grounded Transformer via External Sliding Window Mamba Memory

arXiv:2605.17478v1 Announce Type: new Abstract: Visual Geometry Grounded Transformers (VGGT) have set new benchmarks in high-fidelity 3D scene reconstruction. However, as the sequence length increases

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization

arXiv:2605.17997v1 Announce Type: cross Abstract: Recently, residual reconstruction-based model quantization methods have achieved promising performance in low-bit post-training quantization (PTQ) by

Masking Causality and Conditional Dependence

arXiv:2603.06984v2 Announce Type: replace-cross Abstract: Many regulatory and analytic problems require that a prohibited variable influence a decision only through a designated allowable channel -- a

Measuring Changes in Instructor Class Design and Student Learning After the Release of Large Language Models (LLMs)

arXiv:2605.16284v1 Announce Type: cross Abstract: Student use of Generative AI (GenAI) products in completing their classwork, with or without their professors' knowledge and/or approval, has resulted

Medical Context Distorts Decisions in Clinical Vision Language Models

arXiv:2605.17436v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios that require inte

Minor First, Major Last: A Depth-Induced Implicit Bias of Sharpness-Aware Minimization

arXiv:2603.08290v2 Announce Type: replace-cross Abstract: We study the implicit bias of Sharpness-Aware Minimization (SAM) when training L-layer linear diagonal networks on linearly separable binary c

Mitigating Conversational Inertia in Multi-Turn Agents

arXiv:2602.03664v3 Announce Type: replace Abstract: Large language models excel as few-shot learners when provided with appropriate demonstrations, yet this strength becomes problematic in multiturn a

MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation

arXiv:2605.17743v1 Announce Type: new Abstract: Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture

MSIQ: Moment-based Scale-Invariant Quality Measure for Single Image Super-Resolution

arXiv:2605.17588v1 Announce Type: new Abstract: Assessing the quality of single image super-resolution (SISR) results remains an open methodological problem. Common full-reference metrics (PSNR, SSIM,

Multi-Order Matching Network for Alignment-Free Depth Super-Resolution

arXiv:2511.16361v3 Announce Type: replace Abstract: Recent guided depth super-resolution methods are premised on the assumption of strict spatial alignment between depth and RGB, achieving high-qualit

Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages

arXiv:2605.17152v1 Announce Type: new Abstract: Multimodal LLMs are evolving from vision-language to tri-modality that see, hear, and read, yet pipelines and benchmarks remain English-centric and comp

Model Releases

Multilingual jailbreaking of LLMs using low-resource languages

model-releasesarxiv-cs-ai

arXiv:2605.18239v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attempts that circumvent safety guardrails. We investigate whether multi-turn conversation

Natural-Language Agent Harnesses

arXiv:2603.25723v2 Announce Type: replace-cross Abstract: Agent performance is strongly shaped by the surrounding harness: the external execution system around a model that organizes a task run. Yet t

Neural equilibria for long-term prediction of nonlinear conservation laws

arXiv:2501.06933v3 Announce Type: replace Abstract: Nonlinear conservation laws govern a broad class of important physical systems in science and industry and are central to scientific machine learnin

Neural Visual Decoding via Cognitive guided Adaptive Blurring and Information Constrained Alignment

arXiv:2605.16418v1 Announce Type: cross Abstract: EEG-based visual decoding aims to establish a mapping between neural signals and visual semantics. However, it remains constrained by the dual challen

NEWTON: Agentic Planning for Physically Grounded Video Generation

arXiv:2605.18396v1 Announce Type: new Abstract: Video generation models produce visually compelling results but systematically violate physical commonsense -- on VideoPhy-2, the best model achieves on

Old Habits Die Hard: How Conversational History Geometrically Traps LLMs

arXiv:2603.03308v2 Announce Type: replace-cross Abstract: How does the conversational past of large language models (LLMs) influence their future performance? Recent work suggests that LLMs are affect

oldsymbol{f}-OPD: Stabilizing Long-Horizon On-Policy Distillation with Freshness-Aware Control

arXiv:2605.17862v1 Announce Type: cross Abstract: Scaling on-policy distillation (OPD) for large language models (LLMs) confronts a fundamental tension: asynchronous execution is necessary for system

On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression

arXiv:2601.21531v2 Announce Type: replace-cross Abstract: Visual token compression is widely used to accelerate large vision-language models (LVLMs) by pruning or merging visual tokens, yet its advers

OPERA: A Reinforcement Learning--Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval

arXiv:2508.16438v4 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) and dense retrievers have driven significant progress in retrieval-augmented generation (RAG).

Optimal Control of Multiclass Fluid Queueing Networks: A Machine Learning Approach

arXiv:2307.12405v2 Announce Type: replace Abstract: We propose a machine learning approach to the optimal control of multiclass fluid queueing networks (MFQNETs) that provides explicit and insightful

OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence

arXiv:2605.16395v1 Announce Type: cross Abstract: We present OrbiSim, a novel robotic simulation paradigm that redefines world models as a fully differentiable physics engine for embodied intelligence

PAIR: Prefix-Aware Internal Reward Model for Multi-Turn Agent Optimization

arXiv:2605.17877v1 Announce Type: new Abstract: A significant hurdle for current LLMs is the execution of complex, multi-stage tasks. Group Relative Policy Optimization (GRPO) has been emerging as a l

Pairwise Preference Reward and Group-Based Diversity Enhancement for Superior Open-Ended Generation

arXiv:2605.18191v1 Announce Type: new Abstract: Current reinforcement learning(RL) methods are broadly applicable and powerful in verifiable settings where scalar rewards can be provided. However, in

Parameterized 4-Qubit EWL Quantum Game Circuits with Dirac-Solow-Swan Hamiltonian Integration for Quadruple Helix Disruptive Innovation Recommender Systems

arXiv:2605.18080v1 Announce Type: cross Abstract: We present a novel parameterized 4-qubit Eisert-Wilkens-Lewenstein (EWL) quantum game circuit for recommender systems in quadruple helix innovation ec

Patch Ensembles for Robust Salmon Re-Identification with Weak Trajectory Labels

arXiv:2605.18038v1 Announce Type: new Abstract: Salmon re-identification in commercial net-pens is challenging due to large populations, which impose strict accuracy requirements and make large-scale

Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign

arXiv:2605.16452v1 Announce Type: cross Abstract: Accurate peak detection across diverse cardiac physiological signals, including the Electrocardiogram (ECG), Photoplethysmogram (PPG), Ballistocardiog

PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment

arXiv:2605.17671v1 Announce Type: cross Abstract: Non-contrastive self-supervised learning (SSL) is an effective framework for predictive representation learning, but popular (and in practice effectiv

Perceptual implications of automatic anonymization in pathological speech

arXiv:2505.00409v3 Announce Type: replace-cross Abstract: Automatic anonymization is increasingly used to enable ethical sharing of clinical speech, yet its perceptual and clinical consequences remain

PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics

arXiv:2605.18303v1 Announce Type: cross Abstract: World models built on recurrent state space architectures enable efficient latent imagination, yet remain physically unstructured, producing dynamics

Phase Transitions in Driven Informational Systems: A Two-Field Perspective on Learning Theory and Non-Equilibrium Chemistry

arXiv:2605.16325v1 Announce Type: cross Abstract: Phase-transition phenomena in deep learning (grokking, emergent capabilities, and ontological reorganization under context shift) have been studied th

Physics-Aligned Canonical Equivariant Fourier Neural Operator under Symmetry-Induced Shifts

arXiv:2605.18606v1 Announce Type: new Abstract: Neural operators approximate PDE solution maps, but they need not respect the symmetries of the governing equation. In out-of-distribution (OOD) regimes

PhysioSeq2Seq: A Hybrid Physiological Digital Twin and Sequence-to-Sequence LSTM for Long-Horizon Glucose Forecasting in Type 1 Diabetes

arXiv:2605.16860v1 Announce Type: cross Abstract: Accurate long-horizon glucose forecasting is critical for automated insulin delivery systems, which help people with type 1 diabetes (T1D) manage thei

PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift

arXiv:2605.16351v1 Announce Type: cross Abstract: Scientific foundation models are expected to reuse representations under changes in dataset, acquisition protocol, and deployment domain, yet many seq

Pointwise Generalization in Deep Neural Networks

arXiv:2605.18598v1 Announce Type: new Abstract: We address the fundamental question of why deep neural networks generalize by establishing a pointwise generalization theory for fully connected network

Policy-Grounded Dynamic Facet Suggestions for Job Search

arXiv:2605.16479v1 Announce Type: cross Abstract: Job seekers often initiate search with short, underspecified queries. At LinkedIn, over 80% of job-related queries contain three or fewer keywords, ma