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When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution

arXiv:2605.14504v1 Announce Type: new Abstract: Long-horizon household tasks demand robust high-level planning and sustained reasoning capabilities, which are largely overlooked by existing embodied A

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Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement

DGX agent

arXiv:2605.14368v1 Announce Type: cross Abstract: Continuous diffusion language models lag behind autoregressive transformers, partly because diffusion is applied in spaces poorly suited to language d

researcharxiv-cs-ai

15 May 2026

Agents

Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control

arXiv:2512.06471v2 Announce Type: replace-cross Abstract: Goal-conditioned reinforcement learning (RL) concerns the problem of training an agent to maximize the probability of reaching target goal sta

agentsarxiv-cs-ai

Agents

Why Neighborhoods Matter: Traversal Context and Provenance in Agentic GraphRAG

arXiv:2605.15109v1 Announce Type: new Abstract: Retrieval-Augmented Generation can improve factuality by grounding answers in external evidence, but Agentic GraphRAG complicates what it means for cita

agentsarxiv-cs-ai

Why Retrieval-Augmented Generation Fails: A Graph Perspective

arXiv:2605.14192v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become a powerful and widely used approach for improving large language models by grounding generation in ret

Widening the Gap: Exploiting LLM Quantization via Outlier Injection

arXiv:2605.15152v1 Announce Type: cross Abstract: LLM quantization has become essential for memory-efficient deployment. Recent work has shown that quantization schemes can pose critical security risk

local-aiarxiv-cs-ai

WikiCLIP: An Efficient Contrastive Baseline for Open-domain Visual Entity Recognition

arXiv:2603.09921v3 Announce Type: replace Abstract: Open-domain visual entity recognition (VER) seeks to associate images with entities in encyclopedic knowledge bases such as Wikipedia. Recent genera

researcharxiv-cs-cv

Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm

arXiv:2605.13979v1 Announce Type: cross Abstract: Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for

Hardware

Woodelf++: A Fast and Unified Partial Dependence Plot Algorithm for Decision Tree Ensembles

arXiv:2605.14578v1 Announce Type: new Abstract: Partial Dependence Plots (PDPs) visualize how changes in a single feature affect the average model prediction. They are widely used in practice to inter

hardwarearxiv-cs-lg

XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles

arXiv:2605.13922v1 Announce Type: cross Abstract: During the last few years, the term Mechanistic Interpretability, a specific area, under the umbrella of explainable artificial intelligence (XAI), ha

XDomainBench: Diagnosing Reasoning Collapse in High-Dimensional Scientific Knowledge Composition

arXiv:2605.14754v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed for knowledge synthesis, yet their capacity for compositional generalization in scientific knowle

Hardware

XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference

arXiv:2605.14844v1 Announce Type: cross Abstract: We introduce XFP, a dynamic weight quantizer for LLM inference that inverts the conventional workflow: the operator specifies reconstruction quality f

hardwarearxiv-cs-ai

XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations

arXiv:2511.02776v2 Announce Type: replace Abstract: Recent progress in large-scale robotic datasets and vision-language models (VLMs) has advanced research on vision-language-action (VLA) models. Howe

safetyarxiv-cs-ro

You Only Landmark Once: Lightweight U-Net Face Super Resolution with YOLO-World Landmark Heatmaps

arXiv:2605.14166v1 Announce Type: new Abstract: Face image super-resolution aims to recover high-resolution facial images from severely degraded inputs. Under extreme upscaling factors, fine facial de

local-aiarxiv-cs-cv

Your CLIP has 164 dimensions of noise: Exploring the embeddings covariance eigenspectrum of contrastively pretrained vision-language transformers

arXiv:2605.14893v1 Announce Type: cross Abstract: Contrastively pre-trained Vision-Language Models (VLMs) serve as powerful feature extractors. Yet, their shared latent spaces are prone to structural

3D Primitives are a Spatial Language for VLMs

arXiv:2605.12586v1 Announce Type: cross Abstract: Vision-language models (VLMs) exhibit a striking paradox: they can generate executable code that reconstructs a 3D scene from geometric primitives wit

3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots

arXiv:2605.12689v1 Announce Type: new Abstract: In this paper, we present a novel hybrid approach that combines Reinforcement Learning (RL) with Dynamic Window Approach (DWA) for adaptive 3D local nav

local-aiarxiv-cs-ro

3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics Based Appearance-Medium Decoupling

arXiv:2505.21238v3 Announce Type: replace Abstract: Novel view synthesis for underwater scene reconstruction presents unique challenges due to complex light-media interactions. Optical scattering and

researcharxiv-cs-cv

A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data

arXiv:2603.14066v2 Announce Type: replace-cross Abstract: Many real-world multi-party negotiations unfold as sequences of binding, action-level commitments rather than a single final outcome, yet this

A Constraint Programming Approach for n-Day Lookahead Playoff Clinching

arXiv:2605.13142v1 Announce Type: new Abstract: In professional sports, a team has clinched the playoffs if they are guaranteed a postseason spot, regardless of the outcomes of any remaining games. As

A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline

arXiv:2605.12608v1 Announce Type: new Abstract: Object detection in adverse weather is critical for the safety of autonomous vehicles; however, the scarcity of labelled, real-world foggy data remains

safetyarxiv-cs-cv

A Faster Generalized Two-Stage Approximate Top-K

arXiv:2506.04165v3 Announce Type: replace Abstract: We consider the Top-K selection problem, which aims to identify the largest K elements in an array. Top-K selection arises in many machine learning

A Five-Layer MLOps Architecture for Connected Automated Driving

arXiv:2605.12719v1 Announce Type: cross Abstract: The continual assurance of safety and performance of automated driving systems (ADSs) poses significant challenges. ADSs operate in complex, dynamic,

A General Bezier Tree Encoding Counterfactual Framework for Retinal-Vessel-Mediated Disease Analysis

model-releasesarxiv-cs-cv

arXiv:2605.13015v1 Announce Type: cross Abstract: The geometry of the retinal vessel is a key biomarker of vascular diseases, yet clinical evidence remains primarily observational. Existing generative

A Hierarchical Language Model with Predictable Scaling Laws and Provable Benefits of Reasoning

arXiv:2605.13687v1 Announce Type: cross Abstract: We introduce a family of synthetic languages with hierarchical structure -- generated by a broadcast process on trees -- for which the role of context

A Horn extension of DL-Lite with NL data complexity

arXiv:2605.13367v1 Announce Type: cross Abstract: The literature on ontology-mediated query answering (OMQA) has been shaped by two key results: first-order rewritability for DL-Lite, and PTime-hardne

Applications

A Hybrid Tucker-LSTM Tensor Network Model for SOC Prediction in Electric Vehicles

arXiv:2605.13200v1 Announce Type: new Abstract: Accurate state of charge estimation is critical for the success of electric vehicle battery management strategies, but it is well known that conventiona

applicationsarxiv-cs-lg

A Markov Categorical Framework for Language Modeling

arXiv:2507.19247v5 Announce Type: replace-cross Abstract: Autoregressive language models achieve remarkable performance, yet a unified theory explaining their internal mechanisms, how training shapes

Agents

A Multi-Agent Orchestration Framework for Venture Capital Due Diligence

arXiv:2605.13110v1 Announce Type: cross Abstract: We present a fully automated multi-agent framework for corporate due diligence and market analysis in venture capital. The system runs on an event-dri

agentsarxiv-cs-ai

A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data

arXiv:2605.12706v1 Announce Type: new Abstract: RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference, designed to address the lim

A Unified Framework for Critical Scaling of Inverse Temperature in Self-Attention

arXiv:2605.12697v1 Announce Type: cross Abstract: Length-dependent logit rescaling is widely used to stabilize long-context self-attention, but existing analyses and methods suggest conflicting invers

A Unified Perspective for Learning Graph Representations Across Multi-Level Abstractions

arXiv:2605.12685v1 Announce Type: cross Abstract: Graph Self-Supervised Learning (GSSL) has emerged as a powerful paradigm for generating high-quality representations for graph-structured data. While

Applications

A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing

arXiv:2605.13464v1 Announce Type: new Abstract: Diabetes mellitus affects over 537 million adults worldwide and remains a major challenge in preventive healthcare. Existing machine-learning studies pr

applicationsarxiv-cs-lg

A3 : an Analytical Low-Rank Approximation Framework for Attention

arXiv:2505.12942v4 Announce Type: replace-cross Abstract: Large language models have demonstrated remarkable performance; however, their massive parameter counts make deployment highly expensive. Low-

A_3B_2: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning

arXiv:2605.13161v1 Announce Type: new Abstract: Efficient transfer learning methods for large-scale vision-language models (e.g., CLIP) enable strong few-shot transfer, yet existing adaptation methods

safetyarxiv-cs-cv

Accelerating Particle-based Energetic Variational Inference

arXiv:2504.03158v2 Announce Type: replace-cross Abstract: In this work, we propose a new particle-based variational inference (ParVI) method for accelerating the Energetic Variational Inference with I

Achieving epsilon^{-2} Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions

arXiv:2605.13639v1 Announce Type: new Abstract: In this paper, we establish last-iterate convergence rates for off-policy actor--critic methods in reinforcement learning. In particular, under a single

Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling

arXiv:2605.13301v1 Announce Type: new Abstract: Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reachi

AcquisitionSynthesis: Targeted Data Generation using Acquisition Functions

arXiv:2605.13149v1 Announce Type: cross Abstract: Data quality remains a critical bottleneck in developing capable, competitive models. Researchers have explored many ways to generate top quality samp

Action Emergence from Streaming Intent

model-releasesarxiv-cs-cv

arXiv:2605.12622v1 Announce Type: cross Abstract: We formalize action emergence as a target capability for end-to-end autonomous driving: the ability to generate physically feasible, semantically appr

Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations

arXiv:2605.12569v1 Announce Type: cross Abstract: Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning, especially in indoor and multipath-rich environ

safetyarxiv-cs-ai

AdaFocus: Adaptive Relevance-Diversity Sampling with Zero-Cache Look-back for Efficient Long Video Understanding

arXiv:2605.12954v1 Announce Type: cross Abstract: Long video understanding is heavily bottlenecked by a rigid one-shot paradigm: existing methods either densely encode videos at prohibitive memory and

local-aiarxiv-cs-ai

Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization

arXiv:2605.12878v1 Announce Type: cross Abstract: We propose Adam-SHANG, a Lyapunov-guided Adam-type method that couples momentum, adaptive preconditioning, and a curvature-aware correction through a

Adaptive Conformal Prediction for Reliable and Explainable Medical Image Classification

arXiv:2605.12917v1 Announce Type: new Abstract: Deep learning models for medical imaging often exhibit overconfidence, creating safety risks in ambiguous diagnostic scenarios. While Conformal Predicti

safetyarxiv-cs-cv

Adaptive Kernel Density Estimation with Pre-training

arXiv:2605.13092v1 Announce Type: cross Abstract: Density estimation in high-dimensional settings is an important and challenging statistical problem.Traditional methods based on kernel smoothing are

local-aiarxiv-cs-lg

Adaptive mine planning under geological uncertainty: A POMDP framework for sequential decision-making

arXiv:2605.13702v1 Announce Type: new Abstract: Strategic mine production scheduling under geological uncertainty is conventionally formulated as a stochastic optimization problem in which a fixed ext

safetyarxiv-cs-ai

Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization

arXiv:2605.12771v1 Announce Type: cross Abstract: Multi-objective reinforcement learning in robotic domains requires balancing complex, non-convex trade-offs between conflicting objectives. While line

safetyarxiv-cs-ai

AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift

arXiv:2602.01629v2 Announce Type: replace Abstract: Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (