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QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks

model-releasesarxiv-cs-cl

arXiv:2605.24218v1 Announce Type: new Abstract: Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how human

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Human DGX agent

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QUIET: A Multi-Blank Cascaded Story Cloze Benchmark for LLM Creative Generation Capability

DGX agent

arXiv:2605.25955v1 Announce Type: cross Abstract: Large language models (LLMs) face a dual challenge in creative capability evaluation: existing benchmarks (e.g., Story Cloze Test, HellaSwag) measure

model-releasesarxiv-cs-ai

26 May 2026

Applications

QUIVER: A Formal Framework for Quantifying Perturbation Propagation and Bifurcation in Compound AI Systems

arXiv:2605.23956v1 Announce Type: new Abstract: Compound AI systems that chain multiple LLM calls into directed computation graphs are now the dominant architecture for production AI. Although these a

applicationsarxiv-cs-ai

Tutorials

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems

arXiv:2507.14760v2 Announce Type: replace-cross Abstract: While deep learning offers tremendous promise for scientific and medical imaging, any failures and hallucinations (predictions that do not coi

tutorialsarxiv-cs-ai

RAMBA: 4D Radar Mapping by Bundle Adjustment

arXiv:2605.25041v1 Announce Type: new Abstract: 4D radar is increasingly attractive for robotic mapping because it provides range, azimuth, elevation, and Doppler measurements while remaining robust i

researcharxiv-cs-ro

Random Neural Network Expressivity for Non-Linear Partial Differential Equations

arXiv:2605.25057v1 Announce Type: cross Abstract: Neural networks with randomly generated hidden weights (RaNNs) have been extensively studied, both as a standalone learning method and as an initializ

Raon-Speech Technical Report

arXiv:2605.23912v1 Announce Type: cross Abstract: We present Raon-Speech, a top-performing 9B-parameter speech language model (SpeechLM) for English and Korean speech understanding, answering, and gen

Rapid mixing in positively weighted restricted Boltzmann machines

arXiv:2604.00963v2 Announce Type: replace-cross Abstract: We show polylogarithmic mixing time bounds for the alternating-scan sampler for positively weighted restricted Boltzmann machines. This is don

RAW: Robust Avatar Watermarking -- Benchmarking and Baseline

arXiv:2605.23994v1 Announce Type: cross Abstract: Digital avatar watermarking presents unique challenges: avatars are routinely post-processed with background replacement, reframing, and format conver

Applications

RCTs & Human Uplift Studies: Methodological Challenges and Practical Solutions for Frontier AI Evaluation

arXiv:2603.11001v2 Announce Type: replace-cross Abstract: Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar

applicationsarxiv-cs-ai

Re-defining Humor Data Objects for AI Humor Research

arXiv:2605.25171v1 Announce Type: new Abstract: In most existing AI humor research, humor was treated as either 'present' or 'not present.' We explore the concept of humor as a social interaction with

researcharxiv-cs-cl

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles

arXiv:2501.02672v2 Announce Type: replace-cross Abstract: Characterising cause-effect relationships in complex systems is fundamental to understanding their underlying mechanisms. Granger causality (G

ReactEmbed: A Plug-and-Play Module for Unifying Protein-Molecule Representations Guided by Biochemical Reaction Networks

arXiv:2501.18278v3 Announce Type: replace Abstract: State-of-the-art models represent proteins and molecules in separate embedding manifolds, limiting the modeling of systemic biological processes. We

READER: Reasoning-Enhanced AI-Generated Text Detection

arXiv:2605.25281v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have made it increasingly difficult to distinguish human-written text from AI-generated content. Many

Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs

arXiv:2603.09095v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided a

safetyarxiv-cs-cl

Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing

arXiv:2605.25902v1 Announce Type: new Abstract: Narrowly finetuned language models memorize implanted content verbatim, but auditing what a deployed model has been taught, without access to its weight

RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges

arXiv:2605.24945v1 Announce Type: cross Abstract: Accurate evaluation of weather forecasting models is critical for their reliable deployment in real-world applications. However, existing benchmarks p

Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving

arXiv:2605.24004v1 Announce Type: new Abstract: Large language models (LLMs) are promising for autonomous driving, but semantics-only decision policies can yield physically unsafe behavior in dynamic

Reasoning as an Attack Surface: Adaptive Evolutionary CoT Jailbreaks for LLMs

arXiv:2605.24497v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in reasoning and generation tasks and are increasingly deployed in real-world ap

RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment

arXiv:2602.00682v2 Announce Type: replace-cross Abstract: Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains l

RECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selection

arXiv:2605.25095v1 Announce Type: new Abstract: Autonomous driving stacks must pick one trajectory from a multi-modal candidate set; choosing by model confidence ignores safety, traffic-law, and comfo

RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics

model-releasesarxiv-cs-ro

arXiv:2605.24044v1 Announce Type: new Abstract: Robots deployed in dynamic environments must contend with environment-driven changes that reshape computation at runtime: new tasks may appear, preceden

Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environments

arXiv:2602.19450v2 Announce Type: replace-cross Abstract: Trusted Execution Environments (TEEs) (e.g., Intel SGX and ArmTrustZone) aim to protect sensitive computation from a compromised operating sys

Referential Security as a New Paradigm for AI Evaluations

arXiv:2605.25673v1 Announce Type: cross Abstract: Security evaluations inherently depend on stable identifiers. Any finding, audit, or regulatory decision must remain attached to the specific artifact

Refined Analysis of Entropy-Regularized Actor-Critic

arXiv:2605.24357v1 Announce Type: new Abstract: In this paper, we study the role of the critic in actor--critic for entropy-regularized, finite, discounted environments. We establish that, when the cr

safetyarxiv-cs-lg

Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion

arXiv:2602.01183v2 Announce Type: replace-cross Abstract: Biological learning proceeds from easy to difficult tasks, gradually reinforcing perception and robustness. Inspired by this principle, we add

Reflect-Guard: Enhancing LLM Safeguards against Adversarial Prompts via Logical Self-Reflection

arXiv:2605.24834v1 Announce Type: cross Abstract: Large language model (LLM) safety classifiers such as Llama Guard are effective at detecting overtly harmful prompts but remain vulnerable to adversar

Reinforcement Learning for Laser Additive Manufacturing Scan-Order Optimisation: A Bilevel Proxy--FEA Diagnostic Framework for Reward and World-Model Diagnosis

arXiv:2605.25063v1 Announce Type: new Abstract: Reinforcement learning offers a promising approach for scan-order optimisation in laser additive manufacturing, where sequential scan decisions critical

Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality

arXiv:2605.24740v1 Announce Type: new Abstract: Reinforcement learning (RL) for reachability specifications is fundamental in sequential decision-making, yet theoretical guarantees remain less explore

Reinforcement Learning from Denoising Feedback

arXiv:2605.25638v1 Announce Type: new Abstract: Policy loss estimation remains a fundamental and long-standing challenge in reinforcement learning (RL) for diffusion language models (dLLMs). We introd

safetyarxiv-cs-cl

Rejoinder: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review

arXiv:2605.25172v1 Announce Type: cross Abstract: This article is the rejoinder to ``The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review,'' to appear in the Journal

Relative Repairability: A Calibration-Based Diagnostic for High-Sparsity Post-Pruning Allocation

arXiv:2605.25508v1 Announce Type: new Abstract: At very high sparsity, neural network pruning does more than decide which weights remain. It also determines where pruning induced damage is placed acro

Relative Translation Invariant Wasserstein Distance

arXiv:2409.02416v2 Announce Type: replace Abstract: Motivated by the Bures distance, we introduce a new family of distances, relative translation invariant Wasserstein distances, denoted by RW_p, as a

Remote sensing data imputation using deep learning for multispectral imagery

arXiv:2605.24003v1 Announce Type: cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years. A common challenge in using optical satellite data

researcharxiv-cs-ai

Repeated Sequences Reveal Gaps between Large Language Models and Natural Language

arXiv:2605.24850v1 Announce Type: new Abstract: Evaluating whether large language models (LLMs) capture the structure of natural language beyond local fluency remains an open challenge. Existing evalu

researcharxiv-cs-cl

RepetitionCurse: Measuring and Understanding Router Imbalance in Mixture-of-Experts LLMs under DoS Stress

arXiv:2512.23995v2 Announce Type: replace-cross Abstract: Mixture-of-Experts architectures have become the standard for scaling large language models due to their superior parameter efficiency. To acc

RePlan-Bot: Multi-Level Replanning for Embodied Instruction Following

model-releasesarxiv-cs-ro

arXiv:2605.25851v1 Announce Type: new Abstract: Embodied instruction following (EIF) requires agents to understand and execute complex natural language commands within interactive 3D environments. Des

Representation-Guided Discrete Molecular Graph Retrosynthesis

arXiv:2605.24428v1 Announce Type: new Abstract: Stochastic process-based molecular graph generators have become the state of the art for template-free single-step retrosynthesis. However, these models

Representation Without Control: Testing the Realization Effect in Language Models

arXiv:2605.25151v1 Announce Type: new Abstract: Large language models are increasingly used as behavioral simulators, but it remains unclear when their outputs reflect human-like cognitive mechanisms

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation

model-releasesarxiv-cs-ro

arXiv:2605.25495v1 Announce Type: new Abstract: Robotic perception in unstructured environments remains challenging despite the zero-shot capabilities of foundation models such as SAM. This work attri

Residual Drift Dominates Contradiction in Multi-Turn Constraint Reasoning

arXiv:2605.23940v1 Announce Type: new Abstract: How do multi-turn reasoning systems fail? The expected answer is logical contradiction, in which the system's maintained state becomes unsatisfiable. We

Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions

arXiv:2605.24251v1 Announce Type: new Abstract: Continual anomaly detection (CAD) addresses the need for industrial inspection systems to adapt to evolving production conditions, yet existing methods

Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach

arXiv:2605.25429v1 Announce Type: new Abstract: Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining. Nevertheless, existing approaches

safetyarxiv-cs-lg

Rethinking Federated Unlearning via the Lens of Memorization

arXiv:2605.24545v1 Announce Type: cross Abstract: Federated learning (FL) increasingly needs machine unlearning to comply with privacy regulations. However, existing federated unlearning approaches ma

researcharxiv-cs-ai

Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark

arXiv:2605.26068v1 Announce Type: cross Abstract: Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these

Agents

Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki

arXiv:2605.25480v1 Announce Type: new Abstract: LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning: searching, reading, traversing, and deciding when ev

agentsarxiv-cs-cl

Local Ai

Retrieval-Augmented Detection of Potentially Abusive Clauses in Chilean Terms of Service

arXiv:2605.26019v1 Announce Type: cross Abstract: Online Terms of Service often function as contracts of adhesion, creating asymmetries that may expose consumers to potentially abusive clauses. In Chi

local-aiarxiv-cs-ai

Retrieved In-Context Principles from Previous Mistakes

arXiv:2407.05682v2 Announce Type: replace Abstract: In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Re

researcharxiv-cs-cl