Search: “models”

Bandits attack function optimization

arXiv:2605.03496v1 Announce Type: new Abstract: We consider function optimization as a sequential decision making problem under budget constraint. This constraint limits the number of objective functi

Agents

Beyond State Machines: Executing Network Procedures with Agentic Tool-Calling Sequences

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DGX agent

arXiv:2605.02584v1 Announce Type: cross Abstract: Agentic AI will be an essential enabling technology for designing future mobile communication systems, which could provide flexible and customized ser

agentsarxiv-cs-ai

6 May 2026

BFORE: Butterfly-Firefly Optimized Retinex Enhancement for Low-Light Image Quality Improvement

arXiv:2605.03509v1 Announce Type: new Abstract: Low-light image enhancement is a fundamental challenge in computer vision and multimedia applications, as images captured under insufficient illuminatio

Local Ai

BIT.UA-AAUBS at ArchEHR-QA 2026: Evaluating Open-Source and Proprietary LLMs via Prompting in Low-Resource QA

arXiv:2605.03618v1 Announce Type: new Abstract: This paper presents the joint participation of the BIT.UA and AAUBS groups in the ArchEHR-QA 2026 shared task, which focuses on clinical question answer

local-aiarxiv-cs-cl

Deciphering Shortcut Learning from an Evolutionary Game Theory Perspective

arXiv:2605.02658v2 Announce Type: new Abstract: Shortcut learning causes deep learning models to rely on non-essential features within the data. However, its formation in deep neural network training

safetyarxiv-cs-ai

Applications

Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR

arXiv:2605.02909v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (

applicationsarxiv-cs-lg

Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding

arXiv:2605.02290v1 Announce Type: new Abstract: Distilling large reasoning models is essential for making Long-CoT reasoning practical, as full-scale inference remains computationally prohibitive. Exi

researcharxiv-cs-ai

Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling

arXiv:2601.21684v2 Announce Type: replace Abstract: Test-Time Scaling enhances the reasoning capabilities of Large Language Models by allocating additional inference compute to broaden the exploration

researcharxiv-cs-cl

Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation

arXiv:2605.02944v1 Announce Type: new Abstract: Reinforcement learning (RL) from unit-test feedback has become a standard post-training recipe for improving large language models (LLMs) on code genera

researcharxiv-cs-lg

FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection

arXiv:2605.03294v1 Announce Type: new Abstract: Open-vocabulary object detection often fails under distribution shifts, as it can be misled by spurious correlations between non-causal visual attribute

FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution

arXiv:2605.03749v1 Announce Type: new Abstract: Ground-to-space astronomical super-resolution requires recovering space-quality images from ground-based observations that are simultaneously limited by

HAAS: A Policy-Aware Framework for Adaptive Task Allocation Between Humans and Artificial Intelligence Systems

model-releasesarxiv-cs-ai

arXiv:2605.02832v1 Announce Type: new Abstract: Deciding how to distribute work between humans and AI systems is a central challenge in organisational design. Most approaches treat this as a binary ch

Height Control and Optimal Torque Planning for Jumping With Wheeled-Bipedal Robots

arXiv:2605.03302v1 Announce Type: new Abstract: This paper mainly studies the accurate height jumping control of wheeled-bipedal robots based on torque planning and energy consumption optimization. Du

safetyarxiv-cs-ro

Agents

Hybrid Inspection and Task-Based Access Control in Zero-Trust Agentic AI

arXiv:2605.02682v1 Announce Type: new Abstract: Authorizing Large Language Model (LLM)-driven agents to dynamically invoke tools and access protected resources introduces significant security risks, a

agentsarxiv-cs-ai

Jiao: Bridging Isolation and Customization in Mixed Criticality Robotics

model-releasesarxiv-cs-ro

arXiv:2605.03641v1 Announce Type: new Abstract: Consumer robotics demands consolidation of safety-critical control, perception pipelines, and user applications on shared multicore platforms. While sta

Kernel Affine Hull Machines for Compute-Efficient Query-Side Semantic Encoding

arXiv:2605.02950v1 Announce Type: new Abstract: Transformer-based semantic retrieval is highly effective, yet in many deployments the dominant cost lies in online query encoding rather than corpus ind

Hardware

Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute

arXiv:2504.17816v3 Announce Type: replace Abstract: Subject-driven video generation (SDV-Gen) aims to produce videos of a specific subject by adapting a pretrained video model, enabling personalized a

hardwarearxiv-cs-cv

LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing

arXiv:2605.03328v1 Announce Type: new Abstract: Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse

Tutorials

LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering

arXiv:2605.02455v1 Announce Type: cross Abstract: State-of-the-art Large Language Models (LLMs) excel in code generation at the function level. However, the output quality significantly declines when

tutorialsarxiv-cs-ai

Low Rank Tensor Completion via Adaptive ADMM

arXiv:2605.03736v1 Announce Type: cross Abstract: We consider a novel algorithm, for the completion of partially observed low-rank tensors, as a generalization of matrix completion. The proposed low-r

MCJudgeBench: A Benchmark for Constraint-Level Judge Evaluation in Multi-Constraint Instruction Following

model-releasesarxiv-cs-cl

arXiv:2605.03858v1 Announce Type: new Abstract: Multi-constraint instruction following requires verifying whether a response satisfies multiple individual requirements, yet LLM judges are often assess

Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection

arXiv:2605.03039v1 Announce Type: new Abstract: Continuous monitoring of bipolar disorder agitation via voice biomarkers requires disentangling stable speaker traits from volatile affective states on

Hardware

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

arXiv:2603.03756v3 Announce Type: replace-cross Abstract: While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, l

hardwarearxiv-cs-cl

Most ReLU Networks Admit Identifiable Parameters

arXiv:2605.03601v1 Announce Type: new Abstract: We study the realization map of deep ReLU networks, focusing on when a function determines its parameters up to scaling and permutation. To analyze hidd

Multilingual Safety Alignment via Self-Distillation

arXiv:2605.02971v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit severe multilingual safety misalignment: they possess strong safeguards in high-resource languages but remain hig

safetyarxiv-cs-cl

NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

arXiv:2605.01189v1 Announce Type: new Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transpar

researcharxiv-cs-ai

Local Ai

On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization

arXiv:2511.11362v2 Announce Type: replace-cross Abstract: On-device fine-tuning is a critical capability for edge AI systems, which must support adaptation to different agentic tasks under stringent m

local-aiarxiv-cs-cl

Optimal control of the future via prospective learning with control

arXiv:2511.08717v4 Announce Type: replace-cross Abstract: Optimal control of the future is the next frontier for AI. Current approaches to this problem are typically rooted in reinforcement learning (

Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI

arXiv:2605.03490v1 Announce Type: new Abstract: The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI dat

safetyarxiv-cs-cv

Parameter-Efficient Multi-View Proficiency Estimation: From Discriminative Classification to Generative Feedback

arXiv:2605.03848v1 Announce Type: new Abstract: Estimating how well a person performs an action, rather than which action is performed, is central to coaching, rehabilitation, and talent identificatio

Power-Softmax: Towards Secure LLM Inference over Encrypted Data

arXiv:2410.09457v2 Announce Type: replace Abstract: Modern cryptographic methods for implementing privacy-preserving LLMs such as gls{HE} require the LLMs to have a polynomial form. Forming such a rep

safetyarxiv-cs-lg

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits

arXiv:2605.03434v1 Announce Type: new Abstract: Reinforcement learning is one of the most challenging learning paradigms where efficacy and efficiency gains are extremely valuable. Hierarchical reinfo

RD-ViT: Recurrent-Depth Vision Transformer for Semantic Segmentation with Reduced Data Dependence Extending the Recurrent-Depth Transformer Architecture to Dense Prediction

arXiv:2605.03999v1 Announce Type: new Abstract: Vision Transformers (ViTs) achieve state-of-the-art segmentation accuracy but require large training datasets because each layer has unique parameters t

Reproducing Complex Set-Compositional Information Retrieval

model-releasesarxiv-cs-cl

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

Retrieval-Augmented LLMs for Security Incident Analysis

model-releasesarxiv-cs-ai

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

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

RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation

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

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

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

Agents

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

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

Sensorless State Estimation and Control for Agile Cable-Suspended Payload Transport by Quadrotors

arXiv:2605.03666v1 Announce Type: new Abstract: This work proposes a novel control and estimation approach for aerial manipulation of a cable-suspended load using Unmanned Aerial Vehicles (UAVs). Comm

researcharxiv-cs-ro

SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification

arXiv:2605.03701v1 Announce Type: new Abstract: Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Larg

safetyarxiv-cs-cl

Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation

arXiv:2510.24211v2 Announce Type: replace Abstract: Autoregressive (AR) modeling has recently emerged as a promising new paradigm in visual generation, but its practical adoption is severely constrain

researcharxiv-cs-cv

Speculative Speculative Decoding

arXiv:2603.03251v3 Announce Type: replace Abstract: Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a

researcharxiv-cs-lg

Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection

arXiv:2605.03303v1 Announce Type: new Abstract: Graph unlearning remains a critical technique for supporting privacy-preserving and sustainable multimodal graph learning. However, we observe that exis

Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation

arXiv:2605.03849v1 Announce Type: new Abstract: Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching

safetyarxiv-cs-cv

SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation

model-releasesarxiv-cs-cl

arXiv:2605.03534v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, but retrieval is not verification: a passage can be topical and still fail t