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arXiv:2604.18090v1 Announce Type: new Abstract: Functional magnetic composites capable of large deformation, load bearing, and multifunctional motion are essential for next-generation adaptive soft ro

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Applications

NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem

DGX agent

arXiv:2604.16967v1 Announce Type: new Abstract: Path planning is usually solved by addressing either the (high-level) route planning problem (waypoint sequencing to achieve the final goal) or the (low

applicationsarxiv-cs-ro

21 Apr 2026

Navigating the Conceptual Multiverse

arXiv:2604.17815v1 Announce Type: cross Abstract: When language models answer open-ended problems, they implicitly make hidden decisions that shape their outputs, leaving users with uncontextualized a

Neural Adjoint Method for Meta-optics: Accelerating Volumetric Inverse Design via Fourier Neural Operators

arXiv:2604.17425v1 Announce Type: new Abstract: Meta-optics promises compact, high-performance imaging and color routing. However, designing high-performance structures is a high-dimensional optimizat

Neuro-Symbolic Resolution of Recommendation Conflicts in Multimorbidity Clinical Guidelines

model-releasesarxiv-cs-cl

arXiv:2604.17340v1 Announce Type: new Abstract: Clinical guidelines, typically developed by independent specialty societies, inherently exhibit substantial fragmentation, redundancy, and logical contr

Neuromorphic BrailleNet: Accurate and Generalizable Braille Reading Beyond Single Characters through Event-Based Optical Tactile Sensing

arXiv:2601.19079v2 Announce Type: replace Abstract: Conventional robotic Braille readers typically rely on discrete, character-by-character scanning, limiting reading speed and disrupting natural flow

applicationsarxiv-cs-ro

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization

arXiv:2604.18471v1 Announce Type: new Abstract: Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexi

No-Worse Context-Aware Decoding: Preventing Neutral Regression in Context-Conditioned Generation

arXiv:2604.16686v1 Announce Type: new Abstract: Large language models (LLMs) can answer questions and summarize documents when conditioned on external contexts (e.g., retrieved evidence), yet context

Tutorials

Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasets

arXiv:2511.03855v2 Announce Type: replace Abstract: Deep learned (DL) models for image recognition have been shown to fail to generalize to data from different devices, populations, etc. COVID-19 dete

tutorialsarxiv-cs-cv

NullFace: Training-Free Localized Face Anonymization

arXiv:2503.08478v2 Announce Type: replace Abstract: Privacy concerns around ever increasing number of cameras are increasing in today's digital age. Although existing anonymization methods are able to

applicationsarxiv-cs-cv

Agents

Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation

arXiv:2604.00131v2 Announce Type: replace Abstract: Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual

agentsarxiv-cs-cl

OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation

model-releasesarxiv-cs-cv

arXiv:2604.18326v1 Announce Type: new Abstract: Recent advancements in audio-video joint generation models have demonstrated impressive capabilities in content creation. However, generating high-fidel

OmniVLA-RL: A Vision-Language-Action Model with Spatial Understanding and Online RL

arXiv:2604.17706v1 Announce Type: new Abstract: Visual-Language-Action (VLA) models represent a paradigm shift in embodied AI, yet existing frameworks often struggle with imprecise spatial perception,

safetyarxiv-cs-ro

On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting

arXiv:2504.13618v4 Announce Type: replace Abstract: The field of robotic manipulation has advanced significantly in recent years. At the sensing level, several novel tactile sensors have been develope

safetyarxiv-cs-ro

On the Predictive Power of Representation Dispersion in Language Models

model-releasesarxiv-cs-cl

arXiv:2506.24106v2 Announce Type: replace Abstract: We show that a language model's ability to predict text is tightly linked to the breadth of its embedding space: models that spread their contextual

On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability

arXiv:2604.16576v1 Announce Type: cross Abstract: Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substanti

One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection

arXiv:2604.18393v1 Announce Type: new Abstract: Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under t

ONTO: A Token-Efficient Columnar Notation for LLM Input Optimization

arXiv:2604.17512v1 Announce Type: new Abstract: Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large language models consume operation

Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies

arXiv:2604.16516v1 Announce Type: new Abstract: Text-to-Image (T2I) generation models have been widely adopted across various industries, yet are criticized for frequently exhibiting societal stereoty

OPSDL: On-Policy Self-Distillation for Long-Context Language Models

arXiv:2604.17535v1 Announce Type: new Abstract: Extending the effective context length of large language models (LLMs) remains a central challenge for real-world applications. While recent post-traini

PARM: Pipeline-Adapted Reward Model

arXiv:2604.18327v1 Announce Type: cross Abstract: Reward models (RMs) are central to aligning large language models (LLMs) with human preferences, powering RLHF and advanced decoding strategies. While

applicationsarxiv-cs-cl

PaTaRM: Bridging Pairwise and Pointwise Signals via Preference-Aware Task-Adaptive Reward Modeling

arXiv:2510.24235v3 Announce Type: replace Abstract: Reward models (RMs) are central to reinforcement learning from human feedback (RLHF), providing the critical supervision signals that align large la

PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation

model-releasesarxiv-cs-cv

arXiv:2604.17570v1 Announce Type: new Abstract: Peripheral Blood Smear (PBS) is a critical microscopic examination in hematopathology that yields whole-slide imaging (WSI). Unlike solid tissue patholo

Periodic Steady-State Control of a Handkerchief-Spinning Task Using a Parallel Anti-Parallelogram Tendon-driven Wrist

arXiv:2604.17863v1 Announce Type: new Abstract: Spinning flexible objects, exemplified by traditional Chinese handkerchief performances, demands periodic steady-state motions under nonlinear dynamics

researcharxiv-cs-ro

Persistence-Augmented Neural Networks

arXiv:2604.08469v2 Announce Type: replace Abstract: Topological Data Analysis (TDA) provides tools to describe the shape of data, but integrating topological features into deep learning pipelines rema

PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction

arXiv:2604.17278v1 Announce Type: new Abstract: Effective pest recognition and management are crucial for sustainable agricultural development. However, collecting pest data in real scenarios is often

applicationsarxiv-cs-cv

Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle

model-releasesarxiv-cs-lg

arXiv:2604.18481v1 Announce Type: cross Abstract: This paper is a step-by-step, self-contained guide to the complete training cycle of a Physics-Informed Neural Network (PINN) -- a topic that existing

Physics-Informed Tracking (PIT)

arXiv:2604.16895v1 Announce Type: new Abstract: We propose Physics-Informed Tracking (PIT), a video-based framework for tracking a single particle from video, where a neural network autoencoder locali

Hardware

PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning

arXiv:2509.18169v3 Announce Type: replace-cross Abstract: Tasks on complex systems require high-precision numerical computation to support decisions, but current large language models (LLMs) cannot in

hardwarearxiv-cs-cl

Plasticity Loss in Deep Reinforcement Learning: A Survey

arXiv:2411.04832v3 Announce Type: replace-cross Abstract: Plasticity refers to a network's ability to adapt to changing data distributions, which is crucial for the successful training of deep reinfor

Tutorials

Plausibility as Commonsense Reasoning: Humans Succeed, Large Language Models Do not

arXiv:2604.04825v2 Announce Type: replace Abstract: Large language models achieve strong performance on many language tasks, yet it remains unclear whether they integrate world knowledge with syntacti

tutorialsarxiv-cs-cl

Policy Testing in Markov Decision Processes

arXiv:2505.15342v2 Announce Type: replace-cross Abstract: We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model wi

PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs

arXiv:2604.17543v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable success in general-domain tasks, yet their direct application to the legal domain remains challeng

Preparation of Fractal-Inspired Computational Architectures for Automated Neural Design Exploration

arXiv:2511.07329v3 Announce Type: replace-cross Abstract: It introduces FractalNet, a fractal-inspired computational architectures for advanced large language model analysis that mainly challenges mod

PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues

arXiv:2604.18354v1 Announce Type: new Abstract: Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships. Developing negotiation dialog

Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models

arXiv:2601.15220v2 Announce Type: replace Abstract: We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle

ProfVLM: A lightweight video-language model for multi-view proficiency estimation

model-releasesarxiv-cs-cl

arXiv:2509.26278v4 Announce Type: replace-cross Abstract: Most existing approaches formulate action quality assessment and skill proficiency estimation as discriminative prediction tasks, typically pr

ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification

arXiv:2604.18444v1 Announce Type: cross Abstract: Zero-shot vision-language models (VLMs) have shown promise for chest radiograph classification, but their performance is often limited by confounding

R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation

arXiv:2506.07826v2 Announce Type: replace Abstract: Validating autonomous driving (AD) systems requires diverse and safety-critical testing, making photorealistic virtual environments essential. Tradi

RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction

arXiv:2504.07415v2 Announce Type: replace-cross Abstract: Automated radiology report generation (RRG) holds potential to reduce the workload of radiologists, and recent advances in multimodal large la

applicationsarxiv-cs-cl

Hardware

Real-Time Structural Detection for Indoor Navigation from 3D LiDAR Using Bird's-Eye-View Images

arXiv:2603.19830v2 Announce Type: replace Abstract: Efficient structural perception is essential for mapping and autonomous navigation on resource-constrained robots. Existing 3D methods are computati

hardwarearxiv-cs-ro

REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations

arXiv:2604.17289v1 Announce Type: new Abstract: Supervised fine-tuning of large language models relies on human-annotated data, yet annotation pipelines routinely involve multiple crowdworkers of hete

ReconVLA: An Uncertainty-Guided and Failure-Aware Vision-Language-Action Framework for Robotic Control

arXiv:2604.16677v1 Announce Type: new Abstract: Vision-language-action (VLA) models have emerged as generalist robotic controllers capable of mapping visual observations and natural language instructi

applicationsarxiv-cs-ro

RefineStat: Efficient Exploration for Probabilistic Program Synthesis

arXiv:2509.01082v3 Announce Type: replace Abstract: Probabilistic programming offers a powerful framework for modeling uncertainty, yet statistical model discovery in this domain entails navigating an

ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning

model-releasesarxiv-cs-cv

arXiv:2604.17800v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have gained much attention from the research community thanks to their strength in translating multimodal observat

ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction

arXiv:2506.01770v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved tremendous success in various tasks, yet concerns about their safety and security have emerged. In

Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging

arXiv:2604.17222v1 Announce Type: new Abstract: Breast cancer diagnosis demands rapid and precise tools, yet traditional histopathological methods often fall short in intra-operative settings. Deep Ul

RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation

arXiv:2604.17243v1 Announce Type: new Abstract: A robust Multimodal Large Language Model (MLLM) for Earth Observation should maintain consistent interpretation and reasoning under realistic input vari