Search: “framework”

When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content

arXiv:2512.17738v2 Announce Type: replace Abstract: User-generated content (UGC) is characterised by frequent use of non-standard language, from spelling errors to expressive choices such as slang, ch

researcharxiv-cs-cl

13 May 2026

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WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting

DGX agent

arXiv:2605.11696v1 Announce Type: new Abstract: Recent single-image relighting methods, powered by advanced generative models, have achieved impressive photorealism on synthetic benchmarks. However, t

model-releasesarxiv-cs-cv

13 May 2026

World Action Models: The Next Frontier in Embodied AI

arXiv:2605.12090v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-

safetyarxiv-cs-cl

13 May 2026

YFPO: A Preliminary Study of Yoked Feature Preference Optimization with Neuron-Guided Rewards for Mathematical Reasoning

model-releasesarxiv-cs-cl

arXiv:2605.11906v1 Announce Type: new Abstract: Preference optimization has become an important post-training paradigm for improving the reasoning abilities of large language models. Existing methods

13 May 2026

A Computational Operationalisation of Competing Maturational Theories of Syntactic Development via Statistical Grammar Induction

arXiv:2605.08476v1 Announce Type: new Abstract: This paper is concerned with what intermediate syntactic categories children acquire during first language development, and in what order. Maturational

researcharxiv-cs-cl

A Controlled Diagnostic Study of Hardware-Induced Distortions in Hardware-Aware Training

arXiv:2605.09416v1 Announce Type: new Abstract: Hardware-aware training (HAT) is widely used to improve the robustness of neural networks on non-ideal AI accelerators, such as analog in-memory computi

A Game Theoretic Free Energy Analysis of Higher Order Synergy in Attention Heads of Large Language Models

arXiv:2605.09515v1 Announce Type: new Abstract: Large language models rely on multihead attention, but interactions among heads remain poorly understood. We apply the Game Theoretic Free Energy Princi

A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation

arXiv:2602.00834v4 Announce Type: replace-cross Abstract: Score-based methods are powerful across machine learning, but they face a paradox: theoretically path-independent, yet practically path-depend

A new initialisation to Control Gradients in Sinusoidal Neural network

model-releasesarxiv-cs-lg

arXiv:2512.06427v2 Announce Type: replace Abstract: Proper initialisation strategy is of primary importance to mitigate gradient explosion or vanishing when training neural networks. Yet, the impact o

A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline

arXiv:2605.08282v1 Announce Type: cross Abstract: Purpose: We aim to enhance the image quality of point-of-care ultrasound (POCUS) devices using deep learning and a novel paired dataset of POCUS and h

A Physical Theory of Backpropagation: Exact Gradients from the Least-Action Principle

arXiv:2602.02281v2 Announce Type: replace-cross Abstract: Backpropagation is typically presented as a symbolic procedure: a backward pass topologically distinct from inference, with non-local error si

local-aiarxiv-cs-ai

A Qualitative Test-Risk Mechanism for Scaling Behavior in Normalized Residual Networks

arXiv:2605.08297v1 Announce Type: cross Abstract: The scaling behavior, in which test performance often improves as model size and data increase, is a central empirical phenomenon in modern deep learn

A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization

arXiv:2605.09094v1 Announce Type: new Abstract: In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing w

A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment

arXiv:2605.06375v1 Announce Type: cross Abstract: Large language model (LLM) alignment via reinforcement learning from human preferences (RLHF) suffers from unstable policy updates, ambiguous gradient

local-aiarxiv-cs-ai

A Unified Representation of Neural Networks Architectures

model-releasesarxiv-cs-lg

arXiv:2512.17593v3 Announce Type: replace Abstract: In this paper we consider the limiting case of neural networks (NNs) architectures when the number of neurons in each hidden layer and the number of

A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization

arXiv:2605.06226v2 Announce Type: replace Abstract: Accurate and timely diagnosis is essential for effective treatment, particularly in the context of rare diseases. However, current diagnostic workfl

Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution

arXiv:2605.08887v1 Announce Type: new Abstract: Self-evolving agents present a promising path toward continual adaptation by distilling task interactions into reusable knowledge artifacts. In practice

ActivationReasoning: Logical Reasoning in Latent Activation Spaces

arXiv:2510.18184v3 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at generating fluent text, but their internal reasoning remains opaque and difficult to control. Sparse aut

safetyarxiv-cs-ai

AdaPreLoRA: Adafactor Preconditioned Low-Rank Adaptation

arXiv:2605.08734v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) reparameterizes a weight update as a product of two low-rank factors, but the Jacobian J_{G} of the generator mapping the f

Adaptive Context Matters: Towards Provable Multi-Modality Guidance for Super-Resolution

arXiv:2605.10470v1 Announce Type: new Abstract: Super-resolution (SR) is a severely ill-posed problem with inherent ambiguity, as widely recognized in both empirical and theoretical studies. Although

safetyarxiv-cs-cv

Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks

model-releasesarxiv-cs-lg

arXiv:2511.06216v4 Announce Type: replace Abstract: Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods typically r

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning

arXiv:2410.13181v2 Announce Type: replace Abstract: Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality

local-aiarxiv-cs-cl

Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III

arXiv:2605.08161v1 Announce Type: new Abstract: Tumor segmentation in whole-body PET/CT imaging is crucial for precise disease evaluation and treatment planning. However, it remains challenging due to

researcharxiv-cs-cv

Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment

arXiv:2605.09902v1 Announce Type: new Abstract: Adversarial perturbations can mislead Multimodal Large Language Models (MLLMs) recognize a benign image as a specific target object, posing serious risk

safetyarxiv-cs-cv

Agent-Omit: Adaptive Context Omission for Efficient LLM Agents

arXiv:2602.04284v2 Announce Type: replace Abstract: Managing agent context (e.g., thought and observation) during multi-turn agent-environment interactions is an emerging strategy to improve agent eff

safetyarxiv-cs-ai

Agent-X: Full Pipeline Acceleration of On-device AI Agents

arXiv:2605.10380v1 Announce Type: new Abstract: LLM-based agents deliver state-of-the-art performance across tasks but incur high end-to-end latency on edge devices. We introduce Agent-X, a software-o

local-aiarxiv-cs-ai

AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems

arXiv:2605.08715v1 Announce Type: cross Abstract: LLM-based multi-agent systems are increasingly deployed on long-horizon tasks, but a single decisive error is often accepted by downstream agents and

AgentPSO: Evolving Agent Reasoning Skill via Multi-agent Particle Swarm Optimization

arXiv:2605.08704v1 Announce Type: new Abstract: Multi-agent reasoning has shown promise for improving the problem-solving ability of large language models by allowing multiple agents to explore divers

AgentReview: Exploring Peer Review Dynamics with LLM Agents

arXiv:2406.12708v3 Announce Type: replace Abstract: Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exp

safetyarxiv-cs-cl

AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems

arXiv:2605.08813v1 Announce Type: new Abstract: Large Language Model-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex tasks. However, manually designing optimal com

agentsarxiv-cs-lg

AIPO: : Learning to Reason from Active Interaction

arXiv:2605.08401v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have demonstrated remarkable reasoning capabilities, largely stimulated by Reinforcement Learning with

safetyarxiv-cs-ai

AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2601.01762v2 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. W

Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis

arXiv:2605.10415v1 Announce Type: new Abstract: Large language models for subjectivity analysis are typically trained with aggregated labels, which compress variations in human judgment into a single

safetyarxiv-cs-cl

AllocMV: Optimal Resource Allocation for Music Video Generation via Structured Persistent State

arXiv:2605.10723v1 Announce Type: cross Abstract: Generating long-horizon music videos (MVs) is frequently constrained by prohibitive computational costs and difficulty maintaining cross-shot consiste

AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment

arXiv:2603.26680v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) evolve into lifelong AI assistants, LLM personalization has become a critical frontier. However, progress is c

An Empirical Study of Multi-Agent Collaboration for Automated Research

arXiv:2603.29632v2 Announce Type: replace-cross Abstract: As AI agents evolve, the community is rapidly shifting from single Large Language Models (LLMs) to Multi-Agent Systems (MAS) to overcome cogni

Anatomical Landmark-Guided Deep Reinforcement Learning for Autonomous Gastric Navigation

arXiv:2605.08269v1 Announce Type: new Abstract: Wireless capsule endoscopy (WCE) enables painless visualization of the gastrointestinal tract, but its diagnostic potential is limited by incomplete muc

safetyarxiv-cs-ro

ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models

arXiv:2605.10328v1 Announce Type: new Abstract: A central challenge in large-scale decision-making under incomplete information is estimating reliable probabilities. Recent approaches leverage Large L

researcharxiv-cs-cl

Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning

arXiv:2605.08764v1 Announce Type: cross Abstract: Deep vision models degrade sharply in low-data regimes, particularly in medical imaging where labeled samples are scarce. We show this arises not mere

researcharxiv-cs-cv

Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI

arXiv:2605.09575v1 Announce Type: cross Abstract: Background: Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment. M

researcharxiv-cs-cv

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study

arXiv:2605.09622v1 Announce Type: cross Abstract: Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often st

AnyDepth-DETR/-YOLO: Any-depth object detection with a single network

model-releasesarxiv-cs-cv

arXiv:2605.09407v1 Announce Type: new Abstract: Modern object detectors are static, fixed-depth networks optimized for a single operating point, requiring separate models for different deployment scen

Tutorials

APCD: Adaptive Path-Contrastive Decoding for Reliable Large Language Model Generation

arXiv:2605.09492v1 Announce Type: cross Abstract: Large language models (LLMs) often suffer from hallucinations due to error accumulation in autoregressive decoding, where suboptimal early token choic

tutorialsarxiv-cs-ai

APEX: Audio Prototype EXplanations for Classification Tasks

arXiv:2605.10153v1 Announce Type: cross Abstract: Explainable AI (XAI) has achieved remarkable success in image classification, yet the audio domain lacks equally mature solutions. Current methods app

Applying Graph Analysis for Unsupervised Fast Malware Fingerprinting

arXiv:2510.12811v2 Announce Type: replace-cross Abstract: Malware proliferation is increasing at a tremendous rate, with hundreds of thousands of new samples identified daily. Manual investigation of

ASIA: an Autonomous System Identification Agent

arXiv:2605.10480v1 Announce Type: new Abstract: Over the years, research in system identification has provided a rich set of methods for learning dynamical models, together with well-established theor

Applications

Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications

arXiv:2605.09533v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly employed in enterprise question-answering (QA) systems, requiring adaptation to domain-specific knowledg

applicationsarxiv-cs-ai

AtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models and Synthetic Data Augmentation

arXiv:2605.09425v1 Announce Type: cross Abstract: Recent conditional image generation methods can improve controllability by generating images that are faithful to conditions such as sketches, human p