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Reinforcement Learning Improves Traversal of Parametric Knowledge in LLMs

arXiv:2511.05933v2 Announce Type: replace Abstract: Reinforcement learning (RL) is often credited with improving language model reasoning at the expense of knowledge. We challenge this narrative by sh

researcharxiv-cs-cl

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Safety

Reliability-Asymmetric Spacecraft Autonomy: Co-Designing a Capable Learned GNC Stack with a Verified, Adaptation-Aware Runtime Shield

DGX agent

arXiv:2606.25366v1 Announce Type: new Abstract: Deep-space missions need onboard autonomy that is both capable and certifiable. Rule-based autonomy is certifiable but brittle, while learned autonomy i

safetyarxiv-cs-ro

25 Jun 2026

Applications

Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score

arXiv:2606.24959v1 Announce Type: new Abstract: Ordinal classification (OC) arises in high-stakes domains such as medicine and finance, where uncertainty quantification must account for the severity o

applicationsarxiv-cs-lg

Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection

arXiv:2606.24985v1 Announce Type: new Abstract: Personalization in wearable-based stress detection remains challenging due to substantial inter-individual variability in physiological and behavioral r

RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments

model-releasesarxiv-cs-lg

arXiv:2606.26094v1 Announce Type: new Abstract: For most of scientific history, researchers studying behavior could only infer hidden mechanisms from outward actions: an inverse problem that becomes m

ReviewGuard: Aligning LLM-Assisted Peer Review with Long-Term Scientific Impact

arXiv:2606.24892v1 Announce Type: cross Abstract: Peer review is central to scientific quality control, yet it can undervalue papers that later achieve substantial citation impact. While frontier larg

researcharxiv-cs-ai

REViT: Roto-reflection Equivariant Convolutional Vision Transformer

arXiv:2606.25318v1 Announce Type: new Abstract: In this paper, we propose a discrete roto-reflection group equivariant vision transformer with convolutional attention. Roto-reflection equivariant netw

Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See

arXiv:2606.25127v1 Announce Type: new Abstract: We investigate how reward design shapes the internal attention patterns of reinforcement learning agents trained for autonomous driving. Using three Per

RGB: RL Guided Whole-Body MPPI for Humanoid Control

arXiv:2606.25123v1 Announce Type: new Abstract: Humanoid robots require whole-body controllers that are both robust and precise in contact-rich environments. While deep reinforcement learning (RL) ach

safetyarxiv-cs-ro

Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning

arXiv:2606.25568v1 Announce Type: new Abstract: Recent LLMs demonstrate strong mathematical reasoning capabilities, but existing gains rely heavily on English-centric training resources and benchmarks

researcharxiv-cs-cl

RigPI: Dynamic Parameter Identification of Rigid Body via VLM-Seeded Differentiable Simulation

model-releasesarxiv-cs-ro

arXiv:2606.25212v1 Announce Type: new Abstract: Accurate physical parameter identification of manipulated objects is fundamental to advanced robotic manipulation and the construction of faithful digit

RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning

arXiv:2601.23075v2 Announce Type: replace Abstract: On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and r

ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models

arXiv:2606.25800v1 Announce Type: new Abstract: Effective online adaptation of vision-language-action (VLA) models remains challenging, as sparse rewards provide weak supervision for high-dimensional

RoboAtlas: Contextual Active SLAM

model-releasesarxiv-cs-cv

arXiv:2606.26046v1 Announce Type: cross Abstract: We present RoboAtlas, a contextual Active SLAM framework that adaptively balances geometric exploration and semantic reasoning using a scalable 3D sem

RoboRouter: Training-Free Policy Routing for Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2603.07892v4 Announce Type: replace Abstract: Research on robotic manipulation has developed a diverse set of policy paradigms, including vision-language-action (VLA) models, vision-action (VA)

Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification

arXiv:2201.01973v3 Announce Type: replace-cross Abstract: The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks. Recent advances in th

Robustness assessment of large audio language models in multiple-choice evaluation

arXiv:2510.04584v2 Announce Type: replace Abstract: Recent advances in large audio language models (LALMs) have primarily been assessed using a multiple-choice question answering (MCQA) framework. How

Agents

RoDyn: Taming Interactive Robot-Dynamic 2.5D World Model for Robotic Manipulation

arXiv:2510.09036v2 Announce Type: replace Abstract: Learned world models hold significant potential as neural simulators for robotic manipulation. However, prevalent 2D video-based models inherently l

agentsarxiv-cs-ro

RotRNN: Modelling Long Sequences with Rotations

arXiv:2407.07239v3 Announce Type: replace Abstract: Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performa

RWGBench: Evaluating Scholarly Positioning in Related Work Generation

model-releasesarxiv-cs-ai

arXiv:2606.24894v1 Announce Type: cross Abstract: Large language models have shown strong fluency in scientific writing, yet the evaluation of related work generation (RWG) remains limited. Existing R

S^{2}-FracMix: Label-Preserving Self-Saliency Mixup Augmentation

arXiv:2606.25784v1 Announce Type: new Abstract: Data augmentation is known to improve generalization of deep visual models. Recent methods favor mixup strategies that generate interpolated samples to

Hardware

SA-LIVO: Efficient LiDAR-Inertial-Visual Odometry with Subspace-Aware Degeneracy Handling

arXiv:2606.25699v1 Announce Type: new Abstract: Tightly coupled LiDAR-visual-inertial odometry (LIVO) fuses precise geometric depth with complementary visual measurements, yet its exteroceptive sensor

hardwarearxiv-cs-ro

SAC^2-Net: Semantic Anchoring and Complementary-Consensus Fusion for Multimodal Micro-Expression Recognition

arXiv:2606.25542v1 Announce Type: new Abstract: Micro-expression recognition (MER) is challenging due to subtle facial movements, limited data, and the ambiguous relationship between Action Units (AUs

safetyarxiv-cs-cv

Safe Learning Control with Optimality and Stability Guarantees

arXiv:2501.15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to

SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation

arXiv:2606.25497v1 Announce Type: new Abstract: Object-Goal Navigation (ObjNav) requires embodied agents to autonomously locate specified targets using only egocentric visual observations. Existing mo

safetyarxiv-cs-ro

Same Evidence, Different Answer: Auditing Order Sensitivity in Multimodal Large Language Models

arXiv:2606.26079v1 Announce Type: new Abstract: Standard benchmarks for multimodal large language models (MLLMs) score each item on one canonical ordering and miss whether order-irrelevant shuffling c

Sample complexity of unbalanced entropic OT

arXiv:2606.24987v1 Announce Type: cross Abstract: Optimal transport (OT) has become a central language for comparing probability measures, but exact balanced OT is often both too rigid for data with m

Sampling Strategies for Robust Universal Quadrupedal Locomotion Policies

model-releasesarxiv-cs-ro

arXiv:2510.07094v2 Announce Type: replace Abstract: This work focuses on sampling strategies of configuration variations for generating robust universal locomotion policies for quadrupedal robots. We

SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment

arXiv:2606.25821v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures have emerged as an increasingly influential paradigm as they offer a strategic balance between parameter s

Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis

arXiv:2606.25369v1 Announce Type: cross Abstract: While large language model (LLM)-based text-to-speech (TTS) systems have achieved high-quality speech synthesis, most existing systems focus on Englis

Scalable Peptide Design via Memory-Efficient Equivariant Transformer

arXiv:2606.25006v1 Announce Type: new Abstract: Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints. Latent generative frameworks offer an ef

Scale or Reason? A Compute-Equivalent Analysis of Reasoning Distillation

arXiv:2509.22193v2 Announce Type: replace Abstract: Distilling reasoning traces from strong teacher models has become the standard recipe for building capable small language models. Yet reasoning trac

researcharxiv-cs-cl

Applications

ScaleHP: Estimating Hand Pose in Metric Space

arXiv:2606.25619v1 Announce Type: new Abstract: Accurate metric-space hand pose estimation (HPE) is essential for immersive human-computer interaction and robotics. However, most existing methods pred

applicationsarxiv-cs-cv

ScalingAR: Scaling Confidence for Autoregressive Image Generation

arXiv:2509.26376v3 Announce Type: replace Abstract: Test-time strategies have shown remarkable success in improving large language models, but their application to next-token prediction (NTP) autoregr

safetyarxiv-cs-cv

SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety

model-releasesarxiv-cs-ai

arXiv:2606.18936v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature a

SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease

arXiv:2603.20452v2 Announce Type: replace Abstract: Longitudinal neuroimaging is essential for modeling disease progression in Alzheimer's disease (AD), yet irregular sampling and missing visits pose

SE-AGCNet: An End-to-End Framework for Joint Speech Enhancement and Loudness Control in Meeting Scenarios

arXiv:2606.25959v1 Announce Type: cross Abstract: Conventional audio pipelines typically treat speech enhancement (SE) and automatic gain control (AGC) as discrete modules, which often limits overall

researcharxiv-cs-ai

Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures

arXiv:2601.23147v2 Announce Type: replace Abstract: The integrity of time in distributed Internet of Things (IoT) devices is crucial for reliable operation in energy cyber-physical systems, such as sm

Local Ai

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems

arXiv:2606.25533v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for enhancing large language models with external knowledge. By coupling retri

local-aiarxiv-cs-cl

Self Capacitive Tactile Sensor System designed for Companion Robots

arXiv:2606.25348v1 Announce Type: new Abstract: Tactile sensing is essential for humanoid robots to achieve safe physical interaction, dexterous manipulation, and truly human-like responsiveness. Howe

researcharxiv-cs-ro

Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning

arXiv:2606.24933v1 Announce Type: cross Abstract: Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating

Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning

arXiv:2606.25232v1 Announce Type: cross Abstract: Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones.

Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents

arXiv:2606.25852v1 Announce Type: new Abstract: Group-based reinforcement learning effectively post-trains LLM agents for long-horizon, sparse-reward tasks by deriving step-level credit from trajector

SEMIR: Topology-Preserving Graph Minors for Thin-Structure Segmentation

arXiv:2606.24935v1 Announce Type: new Abstract: Thin-structure segmentation--power lines, cracks, lane markings at 1-3 pixel width--requires preserving connectivity that standard representations precl

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding

arXiv:2606.25552v1 Announce Type: new Abstract: Prompt-based spoken language understanding (SLU) with large language models (LLMs) often suffers from inconsistent intent--slot structures due to decodi

Shapley-Inspired Feature Weighting in k-means with No Additional Hyperparameters

model-releasesarxiv-cs-lg

arXiv:2508.07952v2 Announce Type: replace Abstract: Clustering algorithms often assume all features contribute equally to the data structure, an assumption that usually fails in high-dimensional or no

Shift Variant Image Degradation and Restoration Using Singular Value Decomposition

arXiv:2606.25818v1 Announce Type: new Abstract: Shift-variant image degradation is frequently encountered in practical imaging systems where the point spread function (PSF) varies across the image fie

ShutterMuse: Capture-Time Photography Guidance with MLLMs

model-releasesarxiv-cs-cv

arXiv:2606.25763v1 Announce Type: new Abstract: Real-world photography requires capture-time guidance for both camera framing and subject pose. Yet existing aesthetic cropping benchmarks mainly evalua