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No Test Cases, No Problem: Distillation-Driven Code Generation for Scientific Workflows

arXiv:2604.23106v1 Announce Type: cross Abstract: Existing multi-agent Large Language Model (LLM) frameworks for code generation typically use execution feedback and improve iteratively using Input/Ou

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Research

'Noisier' Noise Contrastive Eestimation is (Almost) Maximum Likelihood

DGX agent

arXiv:2405.16730v2 Announce Type: replace-cross Abstract: Noise Contrastive Estimation (NCE) has fueled major breakthroughs in representation learning and generative modeling. Yet a long-standing chal

researcharxiv-cs-ai

28 Apr 2026

Hardware

Non-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models

arXiv:2604.22788v1 Announce Type: cross Abstract: Post-harvest fruit quality assessment is essential for reducing food waste, yet reliable non-destructive methods typically depend on expensive hypersp

hardwarearxiv-cs-lg

Nonlinear Non-Gaussian Density Steering with Input and Noise Channel Mismatch: Sinkhorn with Memory for Solving the Control-affine Schrodinger Bridge Problem

arXiv:2604.23370v1 Announce Type: cross Abstract: Solutions to the Schrodinger bridge problem and its generalizations yield feedback control policies for optimal density steering over a controlled dif

Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation

arXiv:2603.14228v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limi

NVILA: Efficient Frontier Visual Language Models

arXiv:2412.04468v3 Announce Type: replace Abstract: Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention

researcharxiv-cs-cv

OAMVOS:2nd Report for 5th PVUW MOSE Track

arXiv:2604.22837v1 Announce Type: cross Abstract: SAM-based dense trackers provide strong short-term mask propagation but remain fragile under long occlusion, fast motion, viewpoint change, and distra

safetyarxiv-cs-ai

ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D Gaussian Splatting

arXiv:2506.05480v4 Announce Type: replace-cross Abstract: We introduce ODE-GS, a novel approach that integrates 3D Gaussian Splatting with latent neural ordinary differential equations (ODEs) to enabl

OLaPh: Optimal Language Phonemizer

model-releasesarxiv-cs-cl

arXiv:2509.20086v2 Announce Type: replace Abstract: Phonemization is a critical component in text-to-speech synthesis. Traditional approaches rely on deterministic transformations and lexica, while ne

Omni-o3: Deep Nested Omnimodal Deduction for Deliberative Audio-Visual Reasoning

arXiv:2604.24191v1 Announce Type: new Abstract: Omnimodal understanding entails a massive, highly redundant search space of cross-modal interactions, demanding focused and deliberative reasoning. Curr

safetyarxiv-cs-cv

OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning

arXiv:2604.00270v2 Announce Type: replace Abstract: Recent large multimodal models (LMMs) have made rapid progress in visual grounding, document understanding, and diagram reasoning tasks. However, th

OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer

arXiv:2604.24762v1 Announce Type: new Abstract: Shot Boundary Detection (SBD) aims to automatically identify shot changes and divide a video into coherent shots. While SBD was widely studied in the li

On Common Misconceptions in Classical Vehicle Dynamics

arXiv:2604.22815v1 Announce Type: cross Abstract: Classical vehicle dynamics contains several widely adopted misconceptions that, while intuitively appealing, may lead to inconsistencies when examined

researcharxiv-cs-ro

On-Device Vision Training, Deployment, and Inference on a Thumb-Sized Microcontroller

arXiv:2604.23012v1 Announce Type: cross Abstract: This paper presents a complete, end-to-end on-device vision machine learning pipeline, comprising data acquisition, two-layer CNN training with Adam o

On Emergent Social World Models -- Evidence for Functional Integration of Theory of Mind and Pragmatic Reasoning in Language Models

arXiv:2602.10298v2 Announce Type: replace Abstract: This paper investigates whether LMs recruit shared computational mechanisms for general Theory of Mind (ToM) and language-specific pragmatic reasoni

researcharxiv-cs-cl

On (not) learning the Mobius function

arXiv:2604.23427v1 Announce Type: cross Abstract: We prove lower bounds on learning the Mobius or Liouville function with a variety of standard learning techniques, including kernel methods, noisy gra

researcharxiv-cs-lg

On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification

arXiv:2604.22903v1 Announce Type: cross Abstract: The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high

Agents

On the Convergence of Jacobian-Free Backpropagation for Optimal Control Problems with Implicit Hamiltonians

arXiv:2602.00921v2 Announce Type: replace-cross Abstract: Optimal feedback control with implicit Hamiltonians poses a fundamental challenge for learning-based value function methods due to the absence

agentsarxiv-cs-lg

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training

arXiv:2410.15155v3 Announce Type: replace Abstract: Aiming to accelerate the training of large deep neural networks (DNN) in an energy-efficient way, analog in-memory computing (AIMC) emerges as a sol

researcharxiv-cs-lg

On the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation

arXiv:2604.22958v1 Announce Type: new Abstract: Preference-based argumentation frameworks (PAFs) extend Dung's approach to abstract argumentation (AAFs) by encoding preferences over arguments. Such pr

On the Memorization of Consistency Distillation for Diffusion Models

arXiv:2604.23552v1 Announce Type: cross Abstract: Diffusion models are central to modern generative modeling, and understanding how they balance memorization and generalization is critical for reliabl

On the Peril of (Even a Little) Nonstationarity in Satisficing Regret Minimization

arXiv:2603.18514v2 Announce Type: replace-cross Abstract: Motivated by the principle of satisficing in decision-making, we study satisficing regret guarantees for nonstationary K-armed bandits. We sho

researcharxiv-cs-lg

On the Reasoning Abilities of Masked Diffusion Language Models

arXiv:2510.13117v3 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) for text offer a compelling alternative to traditional autoregressive language models. Parallel generation make

On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning

model-releasesarxiv-cs-lg

arXiv:2507.06542v4 Announce Type: replace Abstract: Decentralized learning provides a scalable alternative to parameter-server-based training, yet its performance is often hindered by limited peer-to-

One Identity, Many Roles: Multimodal Entity Coreference for Enhanced Video Situation Recognition

arXiv:2604.23173v1 Announce Type: new Abstract: Video Situation Recognition (VidSitu) addresses the challenging problem of 'who did what to whom, with what, how, and where' in a video. It tests thorou

researcharxiv-cs-cv

One-Shot Real-World Demonstration Synthesis for Scalable Bimanual Manipulation

arXiv:2512.09297v3 Announce Type: replace Abstract: Learning dexterous bimanual manipulation policies critically depends on large-scale, high-quality demonstrations, yet current paradigms face inheren

safetyarxiv-cs-ro

Applications

One Size Fits None: Heuristic Collapse in LLM Investment Advice

arXiv:2604.23837v1 Announce Type: new Abstract: Large language models are increasingly deployed as advisors in high-stakes domains -- answering medical questions, interpreting legal documents, recomme

applicationsarxiv-cs-cl

OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models

arXiv:2510.01409v2 Announce Type: replace Abstract: System logs represent a valuable source of Cyber Threat Intelligence (CTI), capturing attacker behaviors, exploited vulnerabilities, and traces of m

Applications

Open-Vocabulary Semantic Segmentation Network Integrating Object-Level Label and Scene-Level Semantic Features for Multimodal Remote Sensing Images

arXiv:2604.24125v1 Announce Type: new Abstract: Semantic segmentation of multi-modal remote sensing imagery plays a pivotal role in land use/land cover (LULC) mapping, environmental monitoring, and pr

applicationsarxiv-cs-cv

OpenPodcar2: a robust, ROS2 vehicle for self-driving research

arXiv:2604.24242v1 Announce Type: new Abstract: OpenPodcar2 is a robust, ROS2-interfaced, low-cost, open source hardware and software, autonomous vehicle platform based on an off-the-shelf, hard-canop

safetyarxiv-cs-ro

Agents

OpenVO: Open-World Visual Odometry with Temporal Dynamics Awareness

arXiv:2602.19035v2 Announce Type: replace Abstract: We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectivel

agentsarxiv-cs-cv

Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws

model-releasesarxiv-cs-lg

arXiv:2603.12365v2 Announce Type: replace-cross Abstract: History-dependent constitutive models serve as macroscopic closures for the aggregated effects of micromechanics. Their parameters are typical

OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving

arXiv:2604.23712v1 Announce Type: cross Abstract: Recent advances in formal theorem proving have focused on Olympiad-level mathematics, leaving undergraduate domains largely unexplored. Optimization,

Oracle Noise: Faster Semantic Spherical Alignment for Interpretable Latent Optimization

arXiv:2604.23540v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable generative capabilities, yet accurately aligning complex textual prompts with synthesized layout

safetyarxiv-cs-cv

Orthogonal Representation Learning for Estimating Causal Quantities

arXiv:2502.04274v4 Announce Type: replace Abstract: End-to-end representation learning has become a powerful tool for estimating causal quantities from high-dimensional observational data, but its eff

safetyarxiv-cs-lg

OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents

arXiv:2604.24348v1 Announce Type: new Abstract: The evolution of Multimodal Large Language Models (MLLMs) has shifted the focus from text generation to active behavioral execution, particularly via OS

safetyarxiv-cs-cl

Otherness as a Quality in Designing Expressive Robotic Touch

arXiv:2604.23402v1 Announce Type: cross Abstract: Haptic technologies have advanced rapidly, yet exploration of robotic touch remains dominated by replicating realistic environmental cues or hand gest

researcharxiv-cs-ro

Tutorials

Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations

arXiv:2407.14974v2 Announce Type: replace-cross Abstract: Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relat

tutorialsarxiv-cs-ai

Overcoming Copyright Barriers in Corpus Distribution Through Non-Reversible Hashing

arXiv:2604.23412v1 Announce Type: new Abstract: While annotated corpora are crucial in the field of natural language processing (NLP), those containing copyrighted material are difficult to exchange a

safetyarxiv-cs-cl

Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation

arXiv:2604.22783v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) has become the standard for adapting large language models (LLMs). In this work we challenge the wide-spread as

Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation

arXiv:2509.19602v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning methods have emerged as a promising solution for adapting pre-trained models to various downstream tasks. While thes

Agents

PARASITE: Conditional System Prompt Poisoning to Hijack LLMs

arXiv:2505.16888v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed via third-party system prompts downloaded from public marketplaces. We identify a criti

agentsarxiv-cs-ai

ParkingScenes: A Structured Dataset for End-to-End Autonomous Parking in Simulation Scenes

arXiv:2604.22835v1 Announce Type: cross Abstract: Autonomous parking remains a critical yet challenging task in intelligent driving systems, particularly within constrained urban environments where ma

Partition-of-Unity Gaussian Kolmogorov-Arnold Networks

arXiv:2604.23599v1 Announce Type: cross Abstract: Gaussian basis functions provide an efficient and flexible alternative to spline activations in KANs. In this work, we introduce the partition-of-unit

Passage-Aware Structural Mapping for RGB-D Visual SLAM

arXiv:2604.24707v1 Announce Type: new Abstract: Doorways and passages are critical structural elements for indoor robot navigation, yet they remain underexplored in modern Visual SLAM (VSLAM) framewor

researcharxiv-cs-ro

Patching LLM Like Software: A Lightweight Method for Improving Safety Policy in Large Language Models

arXiv:2511.08484v2 Announce Type: replace Abstract: We propose patching for large language models (LLMs) like software versions, a lightweight and modular approach for addressing safety vulnerabilitie

PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction

arXiv:2604.24371v1 Announce Type: cross Abstract: Cancer survival prediction from multi-omics data remains challenging because prognostic signals are high-dimensional, heterogeneous, and distributed a

Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person Narratives

arXiv:2512.20298v2 Announce Type: replace-cross Abstract: Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. Thi