Search: “framework”

Lost in Translation? Exploring the Shift in Grammatical Gender from Latin to Occitan

arXiv:2605.09156v1 Announce Type: cross Abstract: The diachronic evolution from Latin to the Romance languages involved a restructuring of the grammatical gender system from a tripartite configuration

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Agents

Low-Cost Stereo Vision for Robust 3D Positioning of Thin Radiata Pine Branches in Autonomous Drone Pruning

DGX agent

arXiv:2605.08213v1 Announce Type: new Abstract: Manual pruning of radiata pine, a species of major economic importance to New Zealand forestry, is hazardous, labour-intensive, and increasingly constra

agentsarxiv-cs-cv

12 May 2026

LPT: Less-overfitting Prompt Tuning for Vision-Language Model

arXiv:2410.10247v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated exceptional generalization capabilities for downstream tasks. Due to its efficiency, prompt le

M^3: Reframing Training Measures for Discretized Physical Simulations

arXiv:2605.08843v1 Announce Type: new Abstract: Neural surrogate models for physical simulations are trained on discretized samples of continuous domains, where the induced empirical measure leads to

safetyarxiv-cs-ai

MAG-VLAQ: Multi-modal Aerial-Ground Query Aggregation for Cross-View Place Recognition

arXiv:2605.09418v1 Announce Type: new Abstract: Multi-modal cross-view place recognition remains a fundamental challenge in computer vision and robotics due to the severe viewpoint, modality, and spat

safetyarxiv-cs-cv

MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs

arXiv:2605.10064v1 Announce Type: new Abstract: Self-evolving language-model agents must decide what to learn next and how to preserve what they have learned across iterations. Existing systems typica

agentsarxiv-cs-ai

MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection

arXiv:2605.10240v1 Announce Type: cross Abstract: Software vulnerability detection is critical for ensuring software security and reliability. Despite recent advances in deep learning, real-world vuln

applicationsarxiv-cs-lg

Marrying Generative Model of Healthcare Events with Digital Twin of Social Determinants of Health for Disease Reasoning

arXiv:2605.09771v1 Announce Type: new Abstract: Despite the central role of sensor-derived measurements such as imaging traits and plasma biomarkers in biomedical research and clinical practice, exist

applicationsarxiv-cs-ai

Masked Generative Transformer Is What You Need for Image Editing

arXiv:2605.10859v1 Announce Type: new Abstract: Diffusion models dominate image editing, yet their global denoising mechanism entangles edited regions with surrounding context, causing modifications t

researcharxiv-cs-cv

MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study

arXiv:2605.10763v1 Announce Type: new Abstract: LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors)

agentsarxiv-cs-ai

MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing

arXiv:2605.08697v1 Announce Type: new Abstract: The emerging collaborative information-based knowledge tracing (KT) has been a promising way to enhance modeling of learners' knowledge states. The core

MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching

arXiv:2605.08557v1 Announce Type: cross Abstract: Parameter-efficient adaptation of pretrained vision models is commonly performed through linear probes, prompts, low-rank updates, or lightweight resi

MDGYM: Benchmarking AI Agents on Molecular Simulations

arXiv:2605.08941v1 Announce Type: new Abstract: The promise of AI-driven scientific discovery hinges on whether AI agents can autonomously design and execute the computational workflows that underpin

Local Ai

MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering

arXiv:2605.08759v1 Announce Type: new Abstract: Existing granular-ball generation methods are still mainly driven by handcrafted quality measures and heuristic splitting or stopping criteria, which we

local-aiarxiv-cs-lg

Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests

arXiv:2510.22170v2 Announce Type: replace Abstract: Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure o

Measuring and Decomposing Mode Separation via the Canonical Diffusion

arXiv:2605.08777v1 Announce Type: cross Abstract: Mode separation, namely how sharply a distribution fragments into barrier-separated clusters, is a fundamental geometric property of densities, diffic

researcharxiv-cs-lg

Measuring What Matters: Benchmarking Generative, Multimodal, and Agentic AI in Healthcare

arXiv:2605.08445v1 Announce Type: new Abstract: AI models are increasingly deployed in live clinical environments where they must perform reliably across complex, high-stakes workflows that standard t

MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shift

arXiv:2605.09025v1 Announce Type: new Abstract: Federated learning enables hospitals to collaboratively train segmentation models without sharing patient data. However, current evaluation protocols re

safetyarxiv-cs-cv

MedMeta: A Benchmark for LLMs in Synthesizing Meta-Analysis Conclusion from Medical Studies

arXiv:2605.09661v1 Announce Type: cross Abstract: Large language models (LLMs) have saturated standard medical benchmarks that test factual recall, yet their ability to perform higher-order reasoning,

MedThink: Enhancing Diagnostic Accuracy in Small Models via Teacher-Guided Reasoning Correction

arXiv:2605.08094v1 Announce Type: cross Abstract: Accurate clinical diagnosis requires extensive domain knowledge and complex clinical reasoning capabilities. Although large language models (LLMs) hol

Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology

model-releasesarxiv-cs-cl

arXiv:2605.09152v1 Announce Type: new Abstract: Deciphering animal intent is a fundamental challenge in computational ethology, largely because of semantic aliasing, the phenomenon where identical ext

MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups

arXiv:2603.13452v2 Announce Type: replace Abstract: Fairness in machine learning is predominantly evaluated through outcome-oriented metrics, such as Demographic parity, which measure whether predicti

MeshFIM: Local Low-Poly Mesh Editing via Fill-in-the-Middle Autoregressive Generation

arXiv:2605.08744v1 Announce Type: cross Abstract: Autoregressive (AR) models can generate high-quality low-poly meshes from point clouds, but they still operate in an all-or-nothing manner: when a loc

Metacognitive Behavioral Tuning of Large Language Models for Multi-Hop Question Answering

arXiv:2602.22508v2 Announce Type: replace Abstract: Large Language Models (LLMs) often produce incorrect answers on multi-hop question answering even when the reasoning trace already contains a correc

Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization

arXiv:2605.10067v1 Announce Type: cross Abstract: Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing ap

Metropolis-Adjusted Diffusion Models

arXiv:2605.09654v1 Announce Type: cross Abstract: Sampling from score-based diffusion models incurs bias due to both time discretisation and the approximation of the score function. A common strategy

safetyarxiv-cs-lg

Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts

arXiv:2605.09296v1 Announce Type: cross Abstract: Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing real and AI-generated images. Yet exist

MicroFuse: Protein-to-Genome Expert Fusion for Microbial Operon Reasoning

model-releasesarxiv-cs-lg

arXiv:2605.08815v1 Announce Type: new Abstract: Predicting microbial operon co-membership requires integrating two complementary biological signals: protein-scale molecular identity and genome-context

MicroWorld: Empowering Multimodal Large Language Models to Bridge the Microscopic Domain Gap with Multimodal Attribute Graph

arXiv:2605.10120v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) show remarkable potential for scientific reasoning, yet their performance in specialized domains such as micr

Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models

arXiv:2605.08472v1 Announce Type: new Abstract: The effectiveness of Reinforcement Learning (RL) in Large Language Models (LLMs) depends on the nature and diversity of the data used before and during

safetyarxiv-cs-ai

Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in Spoken Language Models

model-releasesarxiv-cs-cl

arXiv:2510.09592v2 Announce Type: replace Abstract: Real-time Spoken Language Models (SLMs) struggle to leverage Chain-of-Thought (CoT) reasoning due to the prohibitive latency of generating the entir

MIND-Skill: Quality-Guaranteed Skill Generation via Multi-Agent Induction and Deduction

arXiv:2605.08670v1 Announce Type: new Abstract: Large language model (LLM) powered AI agents have emerged as a promising paradigm for autonomous problem-solving, yet they continue to struggle with com

agentsarxiv-cs-ai

MineEvolve: Self-Evolution with Accumulated Knowledge for Long-Horizon Embodied Minecraft Agents

arXiv:2603.13131v3 Announce Type: replace Abstract: Long-horizon embodied intelligence requires agents to improve through interaction, not merely to execute plans generated from static goals. A centra

Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansions

arXiv:2605.08275v1 Announce Type: cross Abstract: Conventional MRI reconstruction methods treat images and coil sensitivities as discrete objects, leading to high memory demands and limited structural

researcharxiv-cs-cv

Modular Retrieval-Augmented Generalization for Human Action Recognition

arXiv:2605.08117v1 Announce Type: cross Abstract: Inertial Measurement Unit (IMU)-based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Rec

applicationsarxiv-cs-cv

Local Ai

MolWorld: Molecule World Models for Actionable Molecular Optimization

arXiv:2605.08954v1 Announce Type: cross Abstract: Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires mo

local-aiarxiv-cs-ai

Morphology-Aware Graph Reinforcement Learning for Tensegrity Robot Locomotion

arXiv:2510.26067v2 Announce Type: replace Abstract: Tensegrity robots combine rigid rods and elastic cables, offering high resilience and deployability but at the same time posing major challenges for

safetyarxiv-cs-ro

MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning

arXiv:2605.10177v1 Announce Type: cross Abstract: Robust urban autonomous driving requires reliable 3D scene understanding and stable decision-making under dense interactions. However, existing end-to

safetyarxiv-cs-ai

Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning

arXiv:2605.09364v1 Announce Type: new Abstract: This paper investigates robust representation learning in offline goal-conditioned reinforcement learning (GCRL). Particularly in sparse reward scenario

safetyarxiv-cs-lg

Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning

model-releasesarxiv-cs-lg

arXiv:2605.08949v1 Announce Type: new Abstract: A central challenge in continual learning for large language models (LLMs) is catastrophic forgetting, where adapting to new tasks can substantially deg

NaiAD: Initiate Data-Driven Research for LLM Advertising

arXiv:2605.09918v1 Announce Type: cross Abstract: Reconciling platform revenue with user experience in LLM advertising motivates a data-centric foundation. We introduce NaiAD, the first comprehensive

Nano-U: Efficient Terrain Segmentation for Tiny Robot Navigation

arXiv:2605.10210v1 Announce Type: cross Abstract: Terrain segmentation is a fundamental capability for autonomous mobile robots operating in unstructured outdoor environments. However, state-of-the-ar

agentsarxiv-cs-cv

NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation

arXiv:2605.10813v1 Announce Type: new Abstract: LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automati

Navigating the Sea of LLM Evaluation: Investigating Bias in Toxicity Benchmarks

arXiv:2605.10639v1 Announce Type: new Abstract: The rapid adoption of LLMs in both research and industry highlights the challenges of deploying them safely and reveals a gap in the systematic evaluati

Neural Distance-Guided Path Integral Control for Tractor-Trailer Navigation

arXiv:2605.09939v1 Announce Type: new Abstract: Autonomous and safe navigation of tractor-trailer systems requires accurate, real-time collision avoidance and dynamically feasible control, particularl

agentsarxiv-cs-ro

Neural Information Causality

arXiv:2605.09316v1 Announce Type: cross Abstract: Query-separated computation forces a representation to play an operational role: data are encoded before a query is known, and a later decoder can ans

Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data

model-releasesarxiv-cs-lg

arXiv:2605.08179v1 Announce Type: cross Abstract: Radar sounders are electromagnetic instruments that can probe deep into the subsurface of Earth and other planetary bodies by processing the echo of t

Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration

arXiv:2506.01250v2 Announce Type: replace Abstract: In this paper, we address the contextual dueling bandit problem by proposing variance-aware algorithms that leverage neural networks to approximate

applicationsarxiv-cs-lg