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QuantSightBench: Evaluating LLM Quantitative Forecasting with Prediction Intervals

arXiv:2604.15859v1 Announce Type: cross Abstract: Forecasting has become a natural benchmark for reasoning under uncertainty. Yet existing evaluations of large language models remain limited to judgme

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Qwen3.5-Omni Technical Report

DGX agent

arXiv:2604.15804v1 Announce Type: new Abstract: In this work, we present Qwen3.5-Omni, the latest advancement in the Qwen-Omni model family. Representing a significant evolution over its predecessor,

model-releasesarxiv-cs-cl

20 Apr 2026

Ragged Paged Attention: A High-Performance and Flexible LLM Inference Kernel for TPU

arXiv:2604.15464v1 Announce Type: cross Abstract: Large Language Model (LLM) deployment is increasingly shifting to cost-efficient accelerators like Google's Tensor Processing Units (TPUs), prioritizi

RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration

arXiv:2604.15945v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to augment the input to Large Language Models (LLMs) with external information, such as recent or do

researcharxiv-cs-cl

Ranking XAI Methods for Head and Neck Cancer Outcome Prediction

arXiv:2604.16034v1 Announce Type: new Abstract: For head and neck cancer (HNC) patients, prognostic outcome prediction can support personalized treatment strategy selection. Improving prediction perfo

researcharxiv-cs-cv

ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams

arXiv:2604.15994v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at recognizing individual visual elements and reasoning over simple linear diagrams. However, when faced

Reading Between the Lines: The One-Sided Conversation Problem

arXiv:2511.03056v2 Announce Type: replace-cross Abstract: Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded, such as telemedicine, call cen

applicationsarxiv-cs-ai

Reasoning over Video: Evaluating How MLLMs Extract, Integrate, and Reconstruct Spatiotemporal Evidence

model-releasesarxiv-cs-cv

arXiv:2603.13091v2 Announce Type: replace Abstract: The growing interest in embodied agents increases the demand for spatiotemporal video understanding, yet existing benchmarks largely emphasize extra

Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing

arXiv:2604.15725v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have demonstrated strong capabilities in generating step-by-step reasoning chains alongside final answers, enabling thei

Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability

arXiv:2604.16106v1 Announce Type: cross Abstract: The Project of AI is a world-building endeavor, wherein those who fund and develop AI systems both operate through and seek to sustain networks of pow

RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2601.03699v2 Announce Type: replace Abstract: As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount.

RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees

model-releasesarxiv-cs-cl

arXiv:2604.15736v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) excel at generic video understanding, their ability to support specialized, rule-grounded decision-maki

RelativeFlow: Taming Medical Image Denoising Learning with Noisy Reference

arXiv:2604.15459v1 Announce Type: cross Abstract: Medical image denoising (MID) lacks absolutely clean images for supervision, leading to a noisy reference problem that fundamentally limits denoising

Repurposing 3D Generative Model for Autoregressive Layout Generation

model-releasesarxiv-cs-cv

arXiv:2604.16299v1 Announce Type: new Abstract: We introduce LaviGen, a framework that repurposes 3D generative models for 3D layout generation. Unlike previous methods that infer object layouts from

Resource-efficient equivariant quantum convolutional neural networks

arXiv:2410.01252v2 Announce Type: replace-cross Abstract: Equivariant quantum neural networks (QNNs) are promising variational models that exploit symmetries to improve machine learning capabilities.

researcharxiv-cs-lg

Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking

arXiv:2604.15621v1 Announce Type: cross Abstract: Adaptive Retrieval-Augmented Generation aims to mitigate the interference of extraneous noise by dynamically determining the necessity of retrieving s

Tutorials

Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift

arXiv:2604.15838v1 Announce Type: new Abstract: Distribution shift severely degrades the performance of deep forecasting models. While this issue is well-studied for individual time series, it remains

tutorialsarxiv-cs-lg

Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning

arXiv:2510.10959v3 Announce Type: replace-cross Abstract: Reasoning ability has become a defining capability of Large Language Models (LLMs), with Reinforcement Learning with Verifiable Rewards (RLVR)

Local Ai

Revisiting the Uniform Information Density Hypothesis in LLM Reasoning

arXiv:2510.06953v3 Announce Type: replace Abstract: The Uniform Information Density (UID) hypothesis proposes that effective communication is achieved by maintaining a stable flow of information. In t

local-aiarxiv-cs-ai

Reward Modeling for Scientific Writing Evaluation

arXiv:2601.11374v2 Announce Type: replace Abstract: Scientific writing is an expert-domain task that demands deep domain knowledge, task-specific requirements and reasoning capabilities that leverage

researcharxiv-cs-cl

Reward Weighted Classifier-Free Guidance as Policy Improvement in Autoregressive Models

arXiv:2604.15577v1 Announce Type: cross Abstract: Consider an auto-regressive model that produces outputs x (e.g., answers to questions, molecules) each of which can be summarized by an attribute vect

Robust Fleet Sizing for Multi-UAV Inspection Missions under Synchronized Replacement Demand

arXiv:2604.15890v1 Announce Type: new Abstract: Multi-UAV inspection missions require spare drones to replace active drones during recharging cycles. Existing fleet-sizing approaches often assume stea

researcharxiv-cs-ro

Robust Multispectral Semantic Segmentation under Missing or Full Modalities via Structured Latent Projection

arXiv:2604.15856v1 Announce Type: cross Abstract: Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unav

Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure

arXiv:2604.16090v1 Announce Type: cross Abstract: Probabilistic Synchronous Parallel (PSP) is a technique in distributed learning systems to reduce synchronization bottlenecks by sampling a subset of

Robustness Verification of Polynomial Neural Networks

model-releasesarxiv-cs-lg

arXiv:2602.06105v2 Announce Type: replace-cross Abstract: We study robustness verification of neural networks via metric algebraic geometry. For polynomial neural networks, certifying a robustness rad

RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs' Contextual Sensitivity

arXiv:2509.25897v2 Announce Type: replace-cross Abstract: People often encounter role conflicts -- social dilemmas where the expectations of multiple roles clash and cannot be simultaneously fulfilled

Safe and Energy-Aware Multi-Robot Density Control via PDE-Constrained Optimization for Long-Duration Autonomy

arXiv:2604.15524v1 Announce Type: cross Abstract: This paper presents a novel density control framework for multi-robot systems with spatial safety and energy sustainability guarantees. Stochastic rob

safetyarxiv-cs-ro

Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility

arXiv:2604.16033v1 Announce Type: cross Abstract: Buildings account for approximately 40% of global energy consumption, and with the growing share of intermittent renewable energy sources, enabling de

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

arXiv:2604.16111v1 Announce Type: new Abstract: We study the sample complexity of learning an epsilon-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bo

safetyarxiv-cs-lg

Sampling-Based Multi-Modal Multi-Robot Multi-Goal Path Planning

model-releasesarxiv-cs-ro

arXiv:2503.03509v3 Announce Type: replace Abstract: In many robotics applications, multiple robots are working in a shared workspace to complete a set of tasks as fast as possible. Such settings can b

Saturation-Aware Space-Variant Blind Image Deblurring

arXiv:2604.16200v1 Announce Type: new Abstract: This paper presents a novel saturation aware space variant blind image deblurring framework designed to address challenges posed by saturated pixels in

applicationsarxiv-cs-cv

Scalable Maximum Entropy Population Synthesis via Persistent Contrastive Divergence

model-releasesarxiv-cs-lg

arXiv:2603.27312v2 Announce Type: replace Abstract: Maximum entropy (MaxEnt) modelling provides a principled framework for generating synthetic populations from aggregate census data, without access t

Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents

arXiv:2504.13541v5 Announce Type: replace-cross Abstract: Training resource-constrained autonomous agents on multiple tasks simultaneously is crucial for adapting to diverse real-world environments. R

Hardware

Scalable Posterior Uncertainty for Flexible Density-Based Clustering

arXiv:2603.03188v2 Announce Type: replace-cross Abstract: We introduce a novel framework for uncertainty quantification in clustering that combines martingale posterior distributions with density-base

hardwarearxiv-cs-lg

Scalable spatial point process models for forensic footwear analysis

arXiv:2602.07006v2 Announce Type: replace Abstract: Shoe print evidence recovered from crime scenes plays a key role in forensic investigations. By examining shoe prints, investigators can determine d

researcharxiv-cs-cv

Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integration

arXiv:2503.05578v4 Announce Type: replace Abstract: Pose estimation-guided unseen object 6-DoF robotic manipulation is a key task in robotics. However, the scalability of current pose estimation metho

safetyarxiv-cs-cv

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning

arXiv:2509.25300v4 Announce Type: replace-cross Abstract: While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement lear

SCHK-HTC: Sibling Contrastive Learning with Hierarchical Knowledge-Aware Prompt Tuning for Hierarchical Text Classification

model-releasesarxiv-cs-cl

arXiv:2604.15998v1 Announce Type: new Abstract: Few-shot Hierarchical Text Classification (few-shot HTC) is a challenging task that involves mapping texts to a predefined tree-structured label hierarc

SCRIPT: Implementing an Intelligent Tutoring System for Programming in a German University Context

arXiv:2604.16117v1 Announce Type: cross Abstract: Practice and extensive exercises are essential in programming education. Intelligent tutoring systems (ITSs) are a viable option to provide individual

applicationsarxiv-cs-ai

SecureRouter: Encrypted Routing for Efficient Secure Inference

arXiv:2604.15499v1 Announce Type: cross Abstract: Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabli

applicationsarxiv-cs-ai

Agents

Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP

arXiv:2602.11327v2 Announce Type: replace-cross Abstract: The rapid development of the AI agent communication protocols, including the Model Context Protocol (MCP), Agent2Agent (A2A), Agora, and Agent

agentsarxiv-cs-ai

Seed1.8 Model Card: Towards Generalized Real-World Agency

arXiv:2603.20633v3 Announce Type: replace Abstract: We present Seed1.8, a foundation model aimed at generalized real-world agency: going beyond single-turn prediction to multi-turn interaction, tool u

Seeing the imagined: a latent functional alignment in visual imagery decoding from fMRI data

arXiv:2604.15374v1 Announce Type: cross Abstract: Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diff

Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories

arXiv:2308.10562v2 Announce Type: cross Abstract: The field of Computer Vision (CV) is increasingly shifting towards ``high-level'' visual sensemaking tasks, yet the exact nature of these tasks remain

SegMix:Shuffle-based Feedback Learning for Semantic Segmentation of Pathology Images

arXiv:2604.15777v1 Announce Type: cross Abstract: Segmentation is a critical task in computational pathology, as it identifies areas affected by disease or abnormal growth and is essential for diagnos

Self-Aligned Reward: Towards Effective and Efficient Reasoners

arXiv:2509.05489v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has significantly advanced reasoning in large language models (LLMs), but such signals remain coarse,

researcharxiv-cs-lg

Self-Distillation as a Performance Recovery Mechanism for LLMs: Counteracting Compression and Catastrophic Forgetting

arXiv:2604.15794v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success, underpinning diverse AI applications. However, they often suffer from performance degra

Self-Supervised Angular Deblurring in Photoacoustic Reconstruction via Noisier2Inverse

arXiv:2604.15681v1 Announce Type: new Abstract: Photoacoustic tomography (PAT) is an emerging imaging modality that combines the complementary strengths of optical contrast and ultrasonic resolution.

researcharxiv-cs-cv