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Lost in Delusion: Examining LLM Safety Under User Delusions and Distress

arXiv:2606.00975v1 Announce Type: new Abstract: LLM chatbots increasingly serve as a first source of support for people in psychological distress, including those whose distress is entangled with delu

safetyarxiv-cs-cl

Low-Pass Flow Matching

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arXiv:2606.02177v1 Announce Type: new Abstract: Flow Matching typically relies on white noise sources, a choice often misaligned with the power spectra of natural data, which tend to decay with freque

safetyarxiv-cs-lg

2 Jun 2026

Low-Resource Safety Failures Are Action Failures, Not Representation Failures

arXiv:2606.01196v1 Announce Type: cross Abstract: Safety alignment learned in high-resource languages transfers poorly to low-resource languages. Models refuse harmful prompts in English but fail to r

LP5X-PIM Sim: A High-Fidelity HW/SW Integrated Simulator for LPDDR5X-PIM

arXiv:2606.00636v1 Announce Type: cross Abstract: This tech note describes the architecture and execution results of the LPDDR5X-PIM simulator, developed by Samsung Electronics. Based on the latest re

researcharxiv-cs-ai

LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

arXiv:2602.01053v2 Announce Type: replace Abstract: Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightwe

agentsarxiv-cs-lg

M^3 Scaling Law: Optimizing Multi-Epoch, Multi-Lingual, and Multi-Stage Training for Low-Resource Language Models

arXiv:2410.12325v2 Announce Type: replace Abstract: In this paper, we study a fundamental design problem in pretraining Large Language Models (LLMs) for low-resource language regimes. Existing works a

researcharxiv-cs-cl

MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment

arXiv:2312.03644v3 Announce Type: replace Abstract: Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky. While independent learn

agentsarxiv-cs-lg

Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting

arXiv:2606.00060v1 Announce Type: cross Abstract: This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performanc

researcharxiv-cs-lg

Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

arXiv:2606.02004v1 Announce Type: new Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data. A recurring obstacle is

researcharxiv-cs-cl

Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures

model-releasesarxiv-cs-lg

arXiv:2606.00938v1 Announce Type: cross Abstract: Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials. In this contribution, a supervised learning ap

Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps

arXiv:2510.00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via rea

Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing

arXiv:2606.00033v1 Announce Type: cross Abstract: While mechanistic interpretability (MI) has produced important insights into neural network internals, the field has yet to establish a standardized s

Make Your VLA More Robust Without More Data By Interleaving Motion Planning

model-releasesarxiv-cs-ro

arXiv:2606.00985v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable progress for mobile manipulation, but their performance on long-horizon tasks remains poor. Th

Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction

arXiv:2402.14521v2 Announce Type: replace Abstract: Standard English and Malaysian English exhibit notable differences, posing challenges for natural language processing (NLP) tasks on Malaysian Engli

researcharxiv-cs-cl

Manifold Diffusion for Structure Generation of Transition Metal Complexes

arXiv:2606.00666v1 Announce Type: cross Abstract: Transition metal complexes are central to catalysis, drug design, and materials science, with relevant properties strongly sensitive to their three-di

researcharxiv-cs-lg

Mapping the evolution of small reservoirs in Brazil from 1984 to 2025 using deep learning

arXiv:2606.00675v1 Announce Type: new Abstract: Water research in Brazil largely overlooks the widespread damming of small streams for agricultural uses such as watering cattle, farm-scale hydropower,

researcharxiv-cs-lg

MARFT: Multi-Agent Reinforcement Fine-Tuning

arXiv:2504.16129v5 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based Multi-Agent Systems (LaMAS) have demonstrated strong capabilities on complex agentic tasks requiring multifac

Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization

arXiv:2510.05342v2 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) has emerged as a simple and effective method for aligning large language models. However, its reliance on

Markerless Augmented Reality Registration for Surgical Guidance: A Multi-Anatomy Clinical Accuracy Study

arXiv:2511.02086v2 Announce Type: replace Abstract: Purpose: In this paper, we develop and clinically evaluate a depth-only, markerless augmented reality (AR) registration pipeline on a head-mounted d

safetyarxiv-cs-cv

Market-Based Replanning for Safety-Critical UAV Swarms in Search and Rescue Missions

arXiv:2606.01970v1 Announce Type: new Abstract: Reliable autonomous UAV swarms in Search and Rescue (SAR) missions require fault-tolerant coordination capable of sustaining operations despite agent de

safetyarxiv-cs-ro

MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems

arXiv:2601.14230v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are emerging as promising socio-collaborative companions for emotional and cognitive support. However, existing syst

MASER: Modality-Adaptive Specialist Routing for Embodied 3D Spatial Intelligence

arXiv:2606.02463v1 Announce Type: cross Abstract: In 3D environments, Embodied Agents answer spatially relevant questions through reasoning from a mixture of modalities including natural language, RGB

Masking Stale Observations Helps Search Agents -- Until It Doesn't: A Regime Map and Its Mechanism

arXiv:2606.00408v1 Announce Type: cross Abstract: Long-horizon search agents accumulate large amounts of retrieved content across many tool calls, making context-budget efficiency increasingly importa

Massive Spikes in LLMs are Bias Vectors: Mechanistic Uncovering and Spike-Free Quantization

arXiv:2606.02288v1 Announce Type: new Abstract: Massive activation spikes in Large Language Models (LLMs) severely degrade quantization by stretching dynamic ranges. While prior hypotheses characteriz

safetyarxiv-cs-lg

MAVL: A Multilingual Audio-Video Lyrics Dataset for Animated Song Translation

model-releasesarxiv-cs-cl

arXiv:2505.18614v5 Announce Type: replace Abstract: Lyrics translation requires both accurate semantic transfer and preservation of musical rhythm, syllabic structure, and poetic style. In animated mu

MBench: A Comprehensive Benchmark on Memory Capability for Video World Models

model-releasesarxiv-cs-cv

arXiv:2606.00793v1 Announce Type: new Abstract: Recent advancements in video-based world models have demonstrated an unprecedented ability to synthesize high-fidelity visual sequences. However, a fund

MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation

arXiv:2606.02470v1 Announce Type: new Abstract: The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and too

Applications

MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas

arXiv:2408.01653v4 Announce Type: replace Abstract: Omnidirectional depth estimation presents a significant challenge due to the inherent distortions in panoramic images. Despite notable advancements,

applicationsarxiv-cs-cv

Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks

model-releasesarxiv-cs-lg

arXiv:2511.08851v5 Announce Type: replace-cross Abstract: This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks.

Measurement Geometry and Design for Trustworthy Generative Inverse Problems

arXiv:2606.02309v1 Announce Type: cross Abstract: Generative models are increasingly used as priors for inverse problems, but their ability to produce realistic images creates a basic trust problem: a

safetyarxiv-cs-cv

Measuring and Mitigating Bias in Code Generated by Large Language Models

arXiv:2606.00049v1 Announce Type: cross Abstract: Large language models (LLMs) are widely recognised for their applications in natural language generation and are increasingly used for code generation

Measuring the Symmetry--Data Exchange Rate

arXiv:2606.01090v1 Announce Type: cross Abstract: Equivariance theory predicts that an architectural symmetry prior reduces sample complexity by a factor of |G|; this is widely cited but rarely measur

safetyarxiv-cs-lg

Mechanistic Diagnostics of Spatial Lexical Bias in Multimodal Large Language Model Spatial Reasoning

arXiv:2606.01914v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) remain unreliable on spatial multiple-choice questions, and their failures are often attributed to poorly atten

safetyarxiv-cs-cl

Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning

model-releasesarxiv-cs-cl

arXiv:2606.01301v1 Announce Type: new Abstract: Hallucinations in medical large language models (LLMs) pose serious risks for clinical decision support, particularly when models must reason over compl

Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training

arXiv:2601.23220v2 Announce Type: replace-cross Abstract: Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer f

Med-URWKV{ag}: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation

model-releasesarxiv-cs-cv

arXiv:2506.10858v2 Announce Type: replace-cross Abstract: Medical image segmentation is a fundamental task in computer-aided diagnosis and treatment. Existing approaches based on CNNs, ViTs, Mamba, an

MedGym:A Unified Continuous-Time Benchmark for Dynamic Medical Treatment Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2606.01028v1 Announce Type: new Abstract: Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements an

Medication-Aware Financial Exploitation Detection for Alzheimer's Patients Using Edge-Aware Interaction Risk Modeling

arXiv:2606.00672v1 Announce Type: new Abstract: Financial exploitation is a growing concern for people with Alzheimer's disease, especially during periods of reduced cognitive stability. Conventional

researcharxiv-cs-ai

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

arXiv:2606.00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging extern

MemoNoveltyAgent: A Historical Research Memory-Aware Agent Workflow for Paper Novelty Assessment

model-releasesarxiv-cs-cl

arXiv:2603.20884v2 Announce Type: replace Abstract: To alleviate the heavy burden of paper screening, researchers increasingly rely on existing AI agents, such as AI reviewers or DeepResearch, for pap

Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling

arXiv:2606.00888v1 Announce Type: cross Abstract: Dynamic Sparse Training (DST) offers a promising paradigm for improving the training and inference efficiency of deep neural networks; however, we fin

researcharxiv-cs-ai

MemPro: Agentic Memory Systems as Evolvable Programs

arXiv:2606.00619v1 Announce Type: cross Abstract: Long-horizon autonomous agents require memory systems to retain historical information, track evolving states, and reuse relevant knowledge beyond fin

Local Ai

MENTIS: What Belief Changes Under Alignment? Measuring Multi-Scale Latent Torsion in Language Models

arXiv:2606.01060v1 Announce Type: cross Abstract: Preference alignment has substantially improved the observable behavior of large language models, yet it remains unclear what alignment changes intern

local-aiarxiv-cs-ai

MESA: Improving MoE Safety Alignment via Decentralized Expertise

arXiv:2606.00651v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale Large Language Models (LLMs) efficiently, enabling greater capacity with reduced computational cost by dy

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA

arXiv:2606.00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems. However, their reliance on rig

safetyarxiv-cs-lg

MGRegBench: A Novel Benchmark Dataset with Anatomical Landmarks for Mammography Image Registration

arXiv:2512.17605v2 Announce Type: replace-cross Abstract: Robust mammography registration is essential for clinically relevant applications like tracking disease progression in breast tissue. However,

Applications

MiCU: End-to-End Smart Home Command Understanding with Large Language Model

arXiv:2606.01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perf

applicationsarxiv-cs-ai

MidSteer: Optimal Affine Framework for Steering Generative Models

arXiv:2605.05220v2 Announce Type: replace-cross Abstract: Steering intermediate representations has emerged as a powerful strategy for controlling generative models, particularly in post-deployment al