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Reactive Motion Generation via Phase-varying Neural Potential Functions

arXiv:2604.26450v1 Announce Type: new Abstract: Dynamical systems (DS) methods for Learning-from-Demonstration (LfD) provide stable, continuous policies from few demonstrations. First-order dynamical

researcharxiv-cs-ro

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Real-time Global Illumination for Dynamic 3D Gaussian Scenes

DGX agent

arXiv:2503.17897v2 Announce Type: replace-cross Abstract: We present a real-time global illumination approach along with a pipeline for dynamic 3D Gaussian models and meshes. Building on a formulated

researcharxiv-cs-cv

30 Apr 2026

Reasoning Gets Harder for LLMs Inside A Dialogue

model-releasesarxiv-cs-cl

arXiv:2603.20133v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve strong performance on many reasoning benchmarks, yet these evaluations typically focus on isolated tasks that d

Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies

arXiv:2604.26903v1 Announce Type: cross Abstract: This paper provides a concise yet comprehensive review of recent advancements in millimeter-wave (mm-wave) oscillators below 100 GHz and sub-terahertz

Recipes for Calibration Checks in Safety-Critical Applications

arXiv:2604.26479v1 Announce Type: cross Abstract: Safety-critical prediction systems, such as autonomous vehicles, weather forecasters, and medical monitors, commonly rely on probabilistic forecasters

safetyarxiv-cs-lg

Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech

arXiv:2604.26242v1 Announce Type: cross Abstract: Digital biomarkers for depression have largely relied on static acoustic descriptors, pooled summary statistics, or conventional machine learning repr

researcharxiv-cs-lg

ReGATE: Learning Faster and Better with Fewer Tokens in MLLMs

arXiv:2507.21420v3 Announce Type: replace-cross Abstract: The computational cost of training multimodal large language models (MLLMs) grows rapidly with the number of processed tokens. Existing effici

researcharxiv-cs-cl

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

arXiv:2602.15983v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that execut

Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding

arXiv:2604.26031v1 Announce Type: new Abstract: This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challen

Resume-ing Control: (Mis)Perceptions of Agency Around GenAI Use in Recruiting Workflows

arXiv:2604.26851v1 Announce Type: cross Abstract: When generative AI (genAI) systems are used in high-stakes decision-making, its recommended role is to aid, rather than replace, human decision-making

Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective

arXiv:2604.25975v1 Announce Type: cross Abstract: Key-value (KV) caching is essential for large language model inference, yet its memory overhead poses a critical bottleneck for long-context generatio

Rethinking the Harmonic Loss via Non-Euclidean Distance Layers

arXiv:2603.10225v3 Announce Type: replace-cross Abstract: Cross-entropy loss has long been the standard choice for training deep neural networks, yet it suffers from interpretability limitations, unbo

Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives

arXiv:2604.05190v2 Announce Type: replace-cross Abstract: Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures.

RetroMotion: Retrocausal Motion Forecasting Models are Instructable

arXiv:2505.20414v2 Announce Type: replace-cross Abstract: Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In pa

agentsarxiv-cs-ai

Revisiting Human-in-the-Loop Object Retrieval with Pre-Trained Vision Transformers

arXiv:2604.00809v2 Announce Type: replace Abstract: Building on existing approaches, we revisit Human-in-the-Loop Object Retrieval, a task that consists of iteratively retrieving images containing obj

reward-lens: A Mechanistic Interpretability Library for Reward Models

arXiv:2604.26130v1 Announce Type: cross Abstract: Every RLHF-trained language model is shaped by a reward model, yet the mechanistic interpretability toolkit -- logit lens, direct logit attribution, a

Risk Reporting for Developers' Internal AI Model Use

arXiv:2604.24966v1 Announce Type: cross Abstract: Frontier AI companies first deploy their most advanced models internally, for weeks or months of safety testing, evaluation, and iteration, before a p

RL unknotter, hard unknots and unknotting number

arXiv:2603.07955v3 Announce Type: replace-cross Abstract: We develop a reinforcement learning pipeline for simplifying knot diagrams. A trained agent learns move proposals and a value heuristic for na

agentsarxiv-cs-lg

Robust Alignment: Harmonizing Clean Accuracy and Adversarial Robustness in Adversarial Training

arXiv:2604.26496v1 Announce Type: new Abstract: Adversarial Training (AT) is one of the most effective methods for developing robust deep neural networks (DNNs). However, AT faces a trade-off problem

safetyarxiv-cs-cv

Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering

arXiv:2508.12672v4 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wh

Tutorials

Robust Representation Learning through Explicit Environment Modeling

arXiv:2604.26128v1 Announce Type: cross Abstract: We consider learning from labeled data collected across multiple environments, where the data distribution may vary across these environments. This pr

tutorialsarxiv-cs-lg

Applications

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare event and Anomaly Detection

arXiv:2509.07523v4 Announce Type: replace Abstract: Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. I

applicationsarxiv-cs-lg

Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training

arXiv:2604.26833v1 Announce Type: cross Abstract: This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios

Safe Navigation using Neural Radiance Fields via Reachable Sets

arXiv:2604.26899v1 Announce Type: cross Abstract: Safe navigation in cluttered environments is an important challenge for autonomous systems. Robots navigating through obstacle ridden scenarios need t

agentsarxiv-cs-ro

SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts

arXiv:2604.26506v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts -- adversarial instruc

researcharxiv-cs-cl

Safety Is Not Universal: The Selective Safety Trap in LLM Alignment

arXiv:2601.04389v2 Announce Type: replace-cross Abstract: Current safety evaluations of large language models (LLMs) create a dangerous illusion of universal protection by aggregating harms under gene

SAGE: A Strategy-Aware Graph-Enhanced Generation Framework For Online Counseling

arXiv:2604.26630v1 Announce Type: new Abstract: Effective mental health counseling is a complex, theory-driven process requiring the simultaneous integration of psychological frameworks, real-time dis

safetyarxiv-cs-cl

Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data

arXiv:2604.26116v1 Announce Type: new Abstract: Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server whil

Local Ai

SAND: Spatially Adaptive Network Depth for Fast Sampling of Neural Implicit Surfaces

arXiv:2604.25936v1 Announce Type: cross Abstract: Implicit neural representations are powerful for geometric modeling, but their practical use is often limited by the high computational cost of networ

local-aiarxiv-cs-cv

SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data

arXiv:2604.26645v1 Announce Type: new Abstract: AI-for-Science (AI4Science) is increasingly transforming scientific discovery by embedding machine learning models into prediction, simulation, and hypo

agentsarxiv-cs-ai

SciMDR: Advancing Scientific Multimodal Document Reasoning

arXiv:2603.12249v2 Announce Type: replace-cross Abstract: Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faith

SD2AIL: Adversarial Imitation Learning from Synthetic Demonstrations via Diffusion Models

arXiv:2512.18583v2 Announce Type: replace Abstract: Adversarial Imitation Learning (AIL) is a dominant framework in imitation learning that infers rewards from expert demonstrations to guide policy op

safetyarxiv-cs-lg

SEAL: Semantic-aware Single-image Sticker Personalization with a Large-scale Sticker-tag Dataset

model-releasesarxiv-cs-cv

arXiv:2604.26883v1 Announce Type: new Abstract: Synthesizing a target concept from a single reference image is challenging in diffusion-based personalized text-to-image generation, particularly for st

Seamless Indoor-Outdoor Mapping for INGENIOUS First Responders

arXiv:2604.26368v1 Announce Type: new Abstract: In several applications it is desired to have 3D models not only from the outdoor spaces but also from inside the building. In the context of First Resp

Local Ai

SecMate: Multi-Agent Adaptive Cybersecurity Troubleshooting with Tri-Context Personalization

arXiv:2604.26394v1 Announce Type: cross Abstract: Recent advances in large language models and agentic frameworks have enabled virtual customer assistants (VCAs) for complex support. We present SecMat

local-aiarxiv-cs-ai

Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation

arXiv:2604.26221v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) in remote sensing images is a promising task that employs textual descriptions for identifying undefined

Local Ai

Select to Think: Unlocking SLM Potential with Local Sufficiency

arXiv:2604.26940v1 Announce Type: new Abstract: Small language models (SLMs) offer computational efficiency for scalable deployment, yet they often fall short of the reasoning power exhibited by their

local-aiarxiv-cs-cl

Self-Jailbreaking: Language Models Can Reason Themselves Out of Safety Alignment After Benign Reasoning Training

model-releasesarxiv-cs-cl

arXiv:2510.20956v2 Announce Type: replace-cross Abstract: We discover a novel and surprising phenomenon of unintentional misalignment in reasoning language models (RLMs), which we call self-jailbreaki

Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery

arXiv:2502.14912v2 Announce Type: replace Abstract: We present a framework for generating universal semantic embeddings of chemical elements to advance materials inference and discovery. This framewor

researcharxiv-cs-cl

Semantic Foam: Unifying Spatial and Semantic Scene Decomposition

arXiv:2604.26262v1 Announce Type: new Abstract: Modern scene reconstruction methods, such as 3D Gaussian Splatting, enable photo-realistic novel view synthesis at real-time speeds. However, their adop

Semi-supervised learning with max-margin graph cuts

arXiv:2604.26818v1 Announce Type: new Abstract: This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels

researcharxiv-cs-lg

SG-UniBuc-NLP at SemEval-2026 Task 6: Multi-Head RoBERTa with Chunking for Long-Context Evasion Detection

arXiv:2604.26375v1 Announce Type: cross Abstract: We describe our system for SemEval-2026 Task 6 (CLARITY: Unmasking Political Question Evasions), which classifies English political interview response

Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens

model-releasesarxiv-cs-cl

arXiv:2604.26355v1 Announce Type: new Abstract: Reasoning in Large Language Models incurs significant inference-time compute, yet the token-level information structure of reasoning traces remains unde

Similarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection

arXiv:2604.26057v1 Announce Type: cross Abstract: Supervised contrastive learning (SupCon) is widely used to shape representations, but has seen limited targeted study for audio deepfake detection. Ex

researcharxiv-cs-lg

SkyReels-Text: Fine-Grained Font-Controllable Text Editing for Poster Design

arXiv:2511.13285v2 Announce Type: replace Abstract: Artistic design, particularly poster design, often demands rapid yet precise modification of textual content while preserving visual harmony and typ

SnapPose3D: Diffusion-Based Single-Frame 2D-to-3D Lifting of Human Poses

arXiv:2604.26620v1 Announce Type: new Abstract: Depth ambiguity and joint uncertainty are the two main obstacles in obtaining accurate human pose predictions by 2D-to-3D lifting methods proposed in th

Sociodemographic Biases in Educational Counselling by Large Language Models

arXiv:2604.25932v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly integrated into educational settings, understanding their potential biases is critical. This study ex

SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment

arXiv:2604.25937v1 Announce Type: cross Abstract: Recent advancements in Text-to-Song generation have enabled realistic musical content production, yet existing evaluation benchmarks lack the professi