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Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries

model-releasesarxiv-cs-cl

arXiv:2509.22202v3 Announce Type: replace-cross Abstract: Large language models (LLMs) now play a central role in code generation, yet they continue to hallucinate, frequently inventing non-existent l

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LIFT and PLACE: A Simple, Stable, and Effective Knowledge Distillation Framework for Lightweight Diffusion Models

DGX agent

arXiv:2605.19729v1 Announce Type: cross Abstract: We demonstrate that in knowledge distillation for diffusion models, the teacher network's highly complex denoising process - stemming from its substan

model-releasesarxiv-cs-ai

20 May 2026

LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators

arXiv:2605.19060v1 Announce Type: cross Abstract: High-resolution 3D medical image generation remains challenging because fully volumetric models are computationally expensive, while efficient 2D slic

Lightweight and Fast Backdoor Model Detection

arXiv:2605.18907v1 Announce Type: cross Abstract: Deep neural networks (DNN), despite their remarkable performance, are highly vulnerable to backdoor attacks. Existing defenses mainly rely on activati

LionMuon: Alternating Spectral and Sign Descent for Efficient Training

arXiv:2605.19811v1 Announce Type: new Abstract: In large-scale optimization, the cheapness and effectiveness of update steps are the most crucial factors for a successful optimizer. Sign-based optimiz

LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets

arXiv:2605.19714v1 Announce Type: new Abstract: Investor sentiment shapes financial markets, yet modeling sentiment in Arabic financial contexts remains challenging due to linguistic complexity and li

researcharxiv-cs-cl

LLM Benchmark Datasets Should Be Contamination-Resistant

arXiv:2605.19999v1 Announce Type: cross Abstract: Benchmark datasets are critical for reproducible, reliable, and discriminative evaluation of LLMs. However, recent studies reveal that many benchmark

LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight

arXiv:2601.03645v2 Announce Type: replace Abstract: Emotional coordination is a core property of human interaction that shapes how relational meaning is constructed in real time. While text-based affe

applicationsarxiv-cs-cl

LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening

model-releasesarxiv-cs-cl

arXiv:2605.19597v1 Announce Type: new Abstract: Evaluating large language models (LLMs) on natural-language logical reasoning is essential because rule-governed tasks require conclusions to follow str

LMM-Track4D: Eliciting 4D Dynamic Reasoning in LMMs via Trajectory-Grounded Dialogue

arXiv:2605.19390v1 Announce Type: new Abstract: Recent large multimodal models (LMMs) have become increasingly capable on image and video understanding, yet still struggle to sustain 4D continuous spa

Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation

arXiv:2603.16284v2 Announce Type: replace Abstract: Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and r

Long-term Power Grid Planning via Answer Set Programming

arXiv:2605.20172v1 Announce Type: cross Abstract: The Power grid is a critical infrastructure underpinning all aspects of modern society and its services. Maintaining its effectiveness requires contin

applicationsarxiv-cs-ai

LoRA vs. Full Fine-Tuning: A Theoretical Perspective

arXiv:2605.19018v1 Announce Type: new Abstract: Fine-tuning adapts a pre-trained model to downstream tasks using a small amount of labeled data. Low-Rank Adaptation (LoRA) is an efficient fine-tuning

Lossless Anti-Distillation Sampling

arXiv:2605.18829v1 Announce Type: new Abstract: Frontier commercial generative models face a growing threat from distillation, whereby a distiller harvests generated responses and trains a competing m

Lost and Found in Translation: Variational Diagnostics for Neural Codebook Channels

arXiv:2605.18846v1 Announce Type: cross Abstract: Classical communication systems fail not only through random noise but also when transmitter and receiver use incompatible operational codebooks. Vari

Lost in Interpretation: The Plausibility-Faithfulness Trade-off in Cross-Lingual Explanations

arXiv:2605.19274v1 Announce Type: new Abstract: LLMs deployed multilingually are often audited via English explanations for non-English inputs. We evaluate extractive explanations ''where the model id

researcharxiv-cs-cl

Low-Compute Watermark Removal via Dual-Domain Natural Projection

arXiv:2510.07538v2 Announce Type: replace Abstract: Effective removal of semantic watermarks requires balancing three competing objectives: high removal success, low perceptual distortion, and low com

safetyarxiv-cs-cv

LP-Eval: Rubric and Dataset for Measuring the Quality of Legal Proposition Generation

arXiv:2605.19815v1 Announce Type: cross Abstract: Legal proposition generation is central to legal reasoning and doctrinal scholarship, yet remain under-examined in Legal NLP. This paper investigates

applicationsarxiv-cs-ai

Lying Is Just a Phase: The Hidden Alignment Transition in Language Model Scaling

arXiv:2605.18838v1 Announce Type: cross Abstract: Scaling laws predict loss from compute but not how capabilities interact. We measure the coupling between reasoning and truthfulness across 63 base mo

Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection

model-releasesarxiv-cs-lg

arXiv:2411.08982v3 Announce Type: replace Abstract: Selective parameter activation provided by Mixture-of-Expert (MoE) models have made them a popular choice in modern foundational models. However, Mo

m3BERT: A Modern, Multi-lingual, Matryoshka Bidirectional Encoder

model-releasesarxiv-cs-cl

arXiv:2605.19568v1 Announce Type: new Abstract: Embedding models are pivotal in industrial information retrieval systems like search and advertising. However, existing pretrained models often exhibit

M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Models

arXiv:2605.18774v1 Announce Type: cross Abstract: In long, multi-page industrial documents, retrieval-augmented generation (RAG) depends heavily on whether chunk boundaries follow the document's true

MAM-CLIP: Vision-Language Pretraining on Mammography Atlases for BI-RADS Classification

arXiv:2605.19359v1 Announce Type: new Abstract: Deep learning methods have demonstrated promising results in predicting BI-RADS scores from mammography images. However, the interpretation of these ima

MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning

arXiv:2605.19080v1 Announce Type: cross Abstract: In Online Continual Learning (OCL), a neural network sequentially learns from a non-stationary data stream in a single-pass with access only to a limi

MapAnything: Evaluating Monocular Metric Depth Models for 3D Urban Asset Localization

arXiv:2509.14839v2 Announce Type: replace Abstract: City administrations increasingly rely on comprehensive databases and urban digital twins of city assets, such as traffic signs and trees, as well a

Mapping Uncharted Symmetries: Machine Discovery in Combinatorics

arXiv:2605.19063v1 Announce Type: new Abstract: Inspired by long-standing open problems in algebraic combinatorics, we show that modern machine learning can meaningfully contribute to verifiable mathe

Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models

arXiv:2605.18931v1 Announce Type: cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling. This behavior poses a fundamental challenge fo

Mask-to-Correct^+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction

arXiv:2605.18776v1 Announce Type: cross Abstract: The rapid spread of misinformation on social media highlights the need for robust, automated fact correction frameworks. However, existing works rely

Matern Noise for Triangulation-Agnostic Flow Matching on Meshes

arXiv:2605.19305v1 Announce Type: cross Abstract: This paper tackles the task of learning to generate signals over triangle meshes in a triangulation-agnostic manner, meaning the trained model can be

Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges

arXiv:2605.19723v1 Announce Type: cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial

MatPhys: Learning Material-Aware Physics Parameters for Deformable Object Simulation from Videos

arXiv:2605.19386v1 Announce Type: new Abstract: Reconstructing simulation-ready deformable objects is important for vision, graphics, and robotics. Existing physics-driven methods can recover physical

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

arXiv:2512.05958v2 Announce Type: replace-cross Abstract: Generative search engines based on large language models (LLMs) are replacing traditional search, fundamentally changing how information provi

MCNav: Memory-Aware Dynamic Cognitive Map for Zero-shot Goal-oriented Navigation

arXiv:2605.19594v1 Announce Type: new Abstract: Navigating to instance-level targets in complex environments is a challenging problem. Many existing zero-shot methods achieve strong performance by mod

researcharxiv-cs-ro

Measuring Safety Alignment Effects in Autonomous Security Agents

arXiv:2605.19722v1 Announce Type: cross Abstract: Do stock safety-aligned language models and their uncensored or abliterated derivatives behave differently when run as autonomous security agents? Sin

Measuring Stereotype and Deviation Biases in Large Language Models

arXiv:2508.06649v3 Announce Type: replace Abstract: Large language models (LLMs) are widely applied across diverse domains, raising concerns about their limitations and potential risks. In this study,

safetyarxiv-cs-cl

Local Ai

Mechanisms of Object Localization in Vision-Language Models

arXiv:2605.19792v1 Announce Type: new Abstract: Visually-grounded language models (VLMs) are highly effective in linking visual and textual information, yet they often struggle with basic classificati

local-aiarxiv-cs-cv

Mechanistic Interpretability Needs Philosophy

arXiv:2506.18852v2 Announce Type: replace-cross Abstract: Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying mechanisms. As the field grows in in

MedFM-Robust: Benchmarking Robustness of Medical Foundation Models

arXiv:2605.19027v1 Announce Type: new Abstract: Medical foundation models (MedFMs) have emerged as transformative tools in healthcare, demonstrating capabilities across diverse clinical applications.

Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation

arXiv:2605.19833v1 Announce Type: cross Abstract: Despite rapid advances in automatic speech recognition (ASR) and large audio-language models, robust recognition in real-world environments remains li

safetyarxiv-cs-ai

Memory-Augmented Reinforcement Learning Agent for CAD Generation

arXiv:2605.19748v1 Announce Type: new Abstract: Automatic generation of computer-aided design (CAD) models is a core technology for enabling intelligence in advanced manufacturing. Existing generation

safetyarxiv-cs-ai

MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling

arXiv:2605.20090v1 Announce Type: new Abstract: Multi-modal remote sensing images are vital for Earth observation, yet complete paired observations are often scarce in practice. Existing generative me

MetaRA: Metamorphic Robustness Assessment for Multimodal Large Language Model-based Visual Question Answering Systems

arXiv:2605.19307v1 Announce Type: new Abstract: Visual Question Answering (VQA), as the representative multimodal task, serves as a key benchmark for evaluating the reasoning capabilities of Multimoda

Metric-Gradient Projection for Stable Multi-Agent Policy Learning

arXiv:2605.18809v1 Announce Type: cross Abstract: General-sum multi-agent learning is often governed by a stacked update field in which each agent's policy update changes the optimization landscape fa

safetyarxiv-cs-ai

MiMuon: Mixed Muon Optimizer with Improved Generalization for Large Models

arXiv:2605.19619v1 Announce Type: cross Abstract: Matrix-structured parameters frequently appear in many artificial intelligence models such as large language models. More recently, an efficient Muon

Mind Your Moras: Orthography-Aware Error Analysis of Neural Japanese Morphological Generation

arXiv:2605.20043v1 Announce Type: new Abstract: We present an orthography-aware error analysis of Japanese past-tense morphological inflection, treating hiragana not merely as a transcriptional medium

researcharxiv-cs-cl

Minimalist Visual Inertial Odometry

arXiv:2605.19990v1 Announce Type: cross Abstract: Visual-Inertial Odometry(VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing came

applicationsarxiv-cs-cv

Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs

arXiv:2605.19768v1 Announce Type: new Abstract: We study reinforcement learning for episodic Markov Decision Processes (MDPs) whose transitions are modelled by a multinomial logistic (MNL) model. Exis

MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency

arXiv:2510.25897v2 Announce Type: replace Abstract: The default paradigm of post-training text-to-image generators includes post-hoc selection of generated images, and subsequent training with one rew