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Refining Compositional Diffusion for Reliable Long-Horizon Planning

arXiv:2605.03075v1 Announce Type: cross Abstract: Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score compositio

local-aiarxiv-cs-lg

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Human DGX agent

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Reinforcement Learning Trained Observer Control for Bearings-Only Tracking

DGX agent

arXiv:2605.02120v1 Announce Type: new Abstract: This paper develops a deep reinforcement learning based observer control policy for autonomous bearings-only tracking of a moving target. The observer m

safetyarxiv-cs-ai

6 May 2026

ReLeaf: Benchmarking Leaf Segmentation across Domains and Species

model-releasesarxiv-cs-cv

arXiv:2605.03784v1 Announce Type: new Abstract: Rising global food demand and growing climate pressure increase the need for sustainable, precise agricultural practices. Automated, individualized plan

Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective

arXiv:2605.02010v1 Announce Type: new Abstract: This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (

researcharxiv-cs-ai

Reproducing Complex Set-Compositional Information Retrieval

arXiv:2605.03824v1 Announce Type: new Abstract: Complex information needs may involve set-compositional queries using conjunction, disjunction, and exclusion, yet it remains unclear whether current re

Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning

arXiv:2605.01834v1 Announce Type: cross Abstract: Contrastive learning (CL) reduces annotation cost via auto-derived supervisory signals. Since large-scale in-house CL datasets are infeasible, relianc

researcharxiv-cs-ai

Reservoir property image slices from the Groningen gas field for image translation and segmentation

arXiv:2605.03942v1 Announce Type: new Abstract: Reservoir characterization workflows increasingly rely on image-based and machine-learning/deep learning or even generative AI approaches, but openly av

researcharxiv-cs-cv

Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement

arXiv:2602.00815v2 Announce Type: replace Abstract: Large language models (LLMs) have exhibited remarkable performance on complex reasoning tasks, with reinforcement learning under verifiable rewards

safetyarxiv-cs-ai

Rethinking Explanations: Formalizing Contrast in Description Logics

arXiv:2605.01442v1 Announce Type: new Abstract: There has been a growing interest in explaining entailments over description logic (DL) knowledge bases. The existing explanation formalisms focus on ju

researcharxiv-cs-ai

Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems

arXiv:2605.04018v1 Announce Type: new Abstract: Reasoning-intensive retrieval aims to surface evidence that supports downstream reasoning rather than merely matching topical similarity. This capabilit

Rethinking Temporal Consistency in Video Object-Centric Learning: From Prediction to Correspondence

arXiv:2605.03650v1 Announce Type: new Abstract: The de facto approach in video object-centric learning maintains temporal consistency through learned dynamics modules that predict future object repres

researcharxiv-cs-cv

Rethinking the Rank Threshold for LoRA Fine-Tuning

arXiv:2605.03724v1 Announce Type: new Abstract: A recent landscape analysis of LoRA fine-tuning in the neural tangent kernel regime establishes a sufficient condition r(r+1)/2 > KN on the LoRA rank r

safetyarxiv-cs-lg

Retrieval and Multi-Hop Reasoning in 1M-Token Context Windows: Evaluating LLMs on Classical Chinese Text

arXiv:2605.02173v1 Announce Type: new Abstract: We evaluate the long-context retrieval and reasoning capabilities of five frontier large language models with advertised 1M-token context windows on a c

Retrieval-Augmented LLMs for Security Incident Analysis

arXiv:2603.18196v3 Announce Type: replace-cross Abstract: Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection aler

Tutorials

Retrieving Floods without Floodlights: Topic Models as Binary Classifiers for Extreme Climate Events in German News

arXiv:2605.03450v1 Announce Type: new Abstract: In studies of media coverage of extreme climate events, NLP methods have become indispensable for identifying relevant texts in large news databases. St

tutorialsarxiv-cs-cl

Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding

arXiv:2605.03514v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a wid

researcharxiv-cs-cl

Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use

arXiv:2605.02964v1 Announce Type: new Abstract: Reinforcement learning (RL) trained language model agents with tool access are increasingly deployed in coding assistants, research tools, and autonomou

RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification

arXiv:2605.03279v1 Announce Type: new Abstract: Automatic modulation classification (AMC) in real-world deployments demands robustness to distribution shifts arising from hardware impairments, unseen

RLDX-1 Technical Report

arXiv:2605.03269v1 Announce Type: cross Abstract: While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intellig

safetyarxiv-cs-lg

RoboAlign-R1: Distilled Multimodal Reward Alignment for Robot Video World Models

model-releasesarxiv-cs-ro

arXiv:2605.03821v1 Announce Type: new Abstract: Existing robot video world models are typically trained with low-level objectives such as reconstruction and perceptual similarity, which are poorly ali

RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation

model-releasesarxiv-cs-cv

arXiv:2507.00435v2 Announce Type: replace-cross Abstract: We introduce RoboEval, a structured evaluation framework and benchmark for robotic manipulation that augments binary success with principled b

Robust Language Identification for Romansh Varieties

arXiv:2603.15969v2 Announce Type: replace Abstract: The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic div

Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach

arXiv:2605.03260v1 Announce Type: new Abstract: Model Predictive Path Integral (MPPI) control is a powerful sampling-based strategy for nonlinear autonomous systems. However, its performance is often

agentsarxiv-cs-ro

Hardware

Robust Visual SLAM for UAV Navigation in GPS-Denied and Degraded Environments: A Multi-Paradigm Evaluation and Deployment Study

arXiv:2605.03678v1 Announce Type: new Abstract: Reliable localization in GPS-denied, visually degraded environments is critical for autonomous UAV opera- tions. This paper presents a systematic compar

hardwarearxiv-cs-ro

Applications

Robustness and Transferability of Pix2Geomodel for Bidirectional Facies Property Translation in a Complex Reservoir

arXiv:2605.03919v1 Announce Type: cross Abstract: Reservoir geomodeling is central to subsurface characterization, but it remains challenging because conditioning data are sparse, geological heterogen

applicationsarxiv-cs-cv

Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL

arXiv:2605.03720v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mat

researcharxiv-cs-cl

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs

arXiv:2605.02946v1 Announce Type: new Abstract: Safety alignment is critical for the responsible deployment of large language models (LLMs). As Mixture-of-Experts (MoE) architectures are increasingly

local-aiarxiv-cs-lg

RPBA-Net: An Interpretable Residual Pyramid Bilateral Affine Network for RAW-Domain ISP Enhancement

arXiv:2605.03626v1 Announce Type: new Abstract: To address module fragmentation, uninterpretable mappings, and deployment constraints in RAW-domain demosaicing, color correction, and detail enhancemen

local-aiarxiv-cs-cv

Runtime Evaluation of Procedural Content Generation in an Endless Runner Game Using Autonomous Agents

arXiv:2605.01783v1 Announce Type: new Abstract: Procedural Content Generation (PCG) enables game content to be created algorithmically without direct manual level-design effort, but it introduces a se

agentsarxiv-cs-ai

S2O: Early Stopping for Sparse Attention via Online Permutation

arXiv:2602.22575v2 Announce Type: replace Abstract: Attention scales quadratically with sequence length, fundamentally limiting long-context inference. Existing block-granularity sparsification can re

S^2tory: Story Spine Distillation for Movie Script Summarization

arXiv:2605.03244v1 Announce Type: new Abstract: Movie scripts pose a fundamental challenge for automatic summarization due to their non-linear, cross-cut narrative structure, which makes surface-level

agentsarxiv-cs-cl

Safety and accuracy follow different scaling laws in clinical large language models

arXiv:2605.04039v1 Announce Type: new Abstract: Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation

Safety-critical Control Under Partial Observability: Reach-Avoid POMDP meets Belief Space Control

arXiv:2603.10572v2 Announce Type: replace Abstract: Partially Observable Markov Decision Processes (POMDPs) provide a principled framework for robot decision-making under uncertainty. Solving reach-av

safetyarxiv-cs-ro

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

arXiv:2605.02900v1 Announce Type: cross Abstract: Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, saf

safetyarxiv-cs-cv

SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition

arXiv:2605.03706v1 Announce Type: new Abstract: Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large l

Sample-Efficient Optimization over Generative Priors via Coarse Learnability

arXiv:2503.06917v5 Announce Type: replace Abstract: We study zeroth-order optimization where solutions must minimize a cost d(s) while maintaining high probability under a complex generative prior L(s

safetyarxiv-cs-lg

Say the Mission, Execute the Swarm: Agent-Enhanced LLM Reasoning in the Web-of-Drones

arXiv:2605.03788v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly explored as high-level reasoning engines for cyber-physical systems, yet their application to real-time

agentsarxiv-cs-ro

Applications

SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning

arXiv:2601.04809v5 Announce Type: replace Abstract: Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on

applicationsarxiv-cs-ai

Scaling Laws and Symmetry, Evidence from Neural Force Fields

arXiv:2510.09768v2 Announce Type: replace Abstract: We present an empirical study in the geometric task of learning interatomic potentials, which shows equivariance matters even more at larger scales;

researcharxiv-cs-lg

Scaling Unsupervised Multi-Source Federated Domain Adaptation through Group-Wise Discrepancy Minimization

arXiv:2510.08150v3 Announce Type: replace Abstract: Unsupervised multi-source domain adaptation (UMDA) leverages labeled data from multiple source domains to generalize to an unlabeled target. While f

SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing

arXiv:2605.02617v2 Announce Type: new Abstract: Capturing semantic consistency among nodes is crucial for effective graph representation learning. Existing approaches typically rely on k-nearest neigh

SCION: Size-aware Policy Orchestration for Nonstationary Object Caches (Long Paper Version)

arXiv:2605.01055v1 Announce Type: cross Abstract: Object caches underpin cloud and edge services, but production workloads are heterogeneous, nonstationary, and throughput-constrained. Recent simple n

safetyarxiv-cs-ai

Scoring Edit Impact in Grammatical Error Correction via Embedded Association Graphs

arXiv:2604.06573v2 Announce Type: replace Abstract: A Grammatical Error Correction (GEC) system produces a sequence of edits to correct an erroneous sentence. The quality of these edits is typically e

researcharxiv-cs-cl

Tutorials

SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering

arXiv:2605.02819v1 Announce Type: new Abstract: Large language models excel at complex reasoning, yet evaluating their intermediate steps remains challenging. Although process reward models provide st

tutorialsarxiv-cs-ai

SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue

arXiv:2602.03548v3 Announce Type: replace Abstract: Large Language Models have demonstrated remarkable capabilities in open-domain dialogues. However, current methods exhibit suboptimal performance in

agentsarxiv-cs-cl

Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?

arXiv:2605.00964v1 Announce Type: cross Abstract: Much research on LLMs has focused on increasing benchmark performance. However, the evaluation of such models in real-world collaborative human-AI wor

Segmenting Human-LLM Co-authored Text via Change Point Detection

arXiv:2605.03723v1 Announce Type: new Abstract: The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity a

local-aiarxiv-cs-cl

Self-Mined Hardness for Safety Fine-Tuning

arXiv:2605.03226v1 Announce Type: new Abstract: Safety fine-tuning of language models typically requires a curated adversarial dataset. We take a different approach: score each candidate prompt's diff