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Logit-Gap Steering: A Forward-Pass Diagnostic for Alignment Robustness

arXiv:2506.24056v2 Announce Type: replace-cross Abstract: RLHF-style alignment trains language models to refuse unsafe requests, but how much operational margin does this refusal rest on? We introduce

safetyarxiv-cs-cl

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Human DGX agent

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Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference

DGX agent

arXiv:2411.16821v5 Announce Type: replace Abstract: Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive de

researcharxiv-cs-cl

5 May 2026

Lost in the Tower of Babel: The Adverse Effects of Incidental Multilingualism in LLMs

arXiv:2605.01224v1 Announce Type: new Abstract: This paper argues that contemporary multilingual NLP has converged on a fragile and misleading paradigm of incidental multilingualism. Today's LLMs appe

agentsarxiv-cs-cl

Low-Latency Embedded Driver Monitoring System with a Multi-Task Neural Network

arXiv:2605.02563v1 Announce Type: new Abstract: Road traffic accidents remain a significant global concern, with the majority attributed to human factors such as driver distraction and fatigue. This s

researcharxiv-cs-cv

Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance

arXiv:2410.18717v2 Announce Type: replace Abstract: Recent advancements in artificial intelligence hold ample potential for monitoring applications using surveillance cameras. However, concerns about

safetyarxiv-cs-cv

Local Ai

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers

arXiv:2509.15113v2 Announce Type: replace Abstract: The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photon

local-aiarxiv-cs-lg

LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning

model-releasesarxiv-cs-lg

arXiv:2605.02133v1 Announce Type: new Abstract: AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale

LVLM-Aided Alignment of Task-Specific Vision Models

arXiv:2512.21985v2 Announce Type: replace Abstract: In high-stakes domains, small task-specific vision models are crucial due to their low computational requirements and the availability of numerous m

safetyarxiv-cs-cv

Applications

Machine Learning as Iterated Belief Change a la Darwiche and Pearl

arXiv:2506.13157v3 Announce Type: replace-cross Abstract: Artificial Neural Networks (ANNs) are powerful machine-learning models capable of capturing intricate non-linear relationships. They are widel

applicationsarxiv-cs-lg

Applications

Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

arXiv:2605.01122v1 Announce Type: new Abstract: Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic expe

applicationsarxiv-cs-lg

Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments

arXiv:2605.01306v1 Announce Type: cross Abstract: Laser absorption spectroscopy (LAS) is a well-established technique for non-intrusive measurement of gas species in combustion and atmospheric environ

MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate

arXiv:2605.01347v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own trajectories under token-level teacher supervision, but existing methods are capped by a single

safetyarxiv-cs-cl

MAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement Learning

arXiv:2605.01805v1 Announce Type: cross Abstract: A key challenge in multi-agent reinforcement learning (MARL) lies in designing learning signals that effectively promote coordination among agents. De

agentsarxiv-cs-lg

Hardware

Maistros: A Greek Large Language Model Adapted Through Knowledge Distillation From Large Reasoning Models

arXiv:2605.01870v1 Announce Type: new Abstract: Large Language Models (LLMs) have substantially advanced the field of Natural Language Processing (NLP), achieving state-of-the-art performance across a

hardwarearxiv-cs-cl

Mamoda2.5: Enhancing Unified Multimodal Model with DiT-MoE

model-releasesarxiv-cs-cv

arXiv:2605.02641v1 Announce Type: new Abstract: We present Mamoda2.5, a unified AR-Diffusion framework that seamlessly integrates multimodal understanding and generation within a single architecture.

Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution

arXiv:2605.02167v1 Announce Type: cross Abstract: Feature attribution is central to diagnosing and trusting deep neural networks, and Integrated Gradients (IG) is widely used due to its axiomatic prop

researcharxiv-cs-cv

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment

arXiv:2605.02183v1 Announce Type: new Abstract: Adversarial training is effective on balanced datasets, but its robustness degrades under longtailed class distributions, where tail classes suffer high

Mapping Discourse Reframing: A Multi-Layer Network Approach to Italian HPV Vaccine Discourse on X (2010-2024)

arXiv:2605.02629v1 Announce Type: new Abstract: Understanding how online narratives travel through coalitions is critical for identifying information disorder, yet computational analyses often rely on

MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training

arXiv:2511.19527v2 Announce Type: replace Abstract: Autonomous driving systems benefit from high-definition (HD) maps that provide critical information about road infrastructure. The online constructi

agentsarxiv-cs-cv

Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners

arXiv:2502.08597v3 Announce Type: replace-cross Abstract: We analyze the performance of heterogeneous learning agents in asset markets with stochastic payoffs. Our main focus is on comparing Bayesian

agentsarxiv-cs-ai

mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection

arXiv:2605.02712v1 Announce Type: new Abstract: SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our

mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection

model-releasesarxiv-cs-cl

arXiv:2605.02695v1 Announce Type: new Abstract: SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and mul

Mean-Field Path-Integral Diffusion: From Samples to Interacting Agents

model-releasesarxiv-cs-ai

arXiv:2605.00007v1 Announce Type: cross Abstract: Independent sample generation is the prevailing paradigm in modern diffusion-based generative models of AI. We ask a different question: can samples c

Mean Testing under Truncation beyond Gaussian

arXiv:2605.01335v1 Announce Type: cross Abstract: We characterize the fundamental limits of high-dimensional mean testing under arbitrary truncation, where samples are drawn from the conditional distr

Tutorials

Measuring AI Reasoning: A Guide for Researchers

arXiv:2605.02442v1 Announce Type: cross Abstract: In this paper, we offer a guide for researchers on evaluating reasoning in language models, building the case that reasoning should be assessed throug

tutorialsarxiv-cs-cl

Measuring Differences between Conditional Distributions using Kernel Embeddings

arXiv:2605.02260v1 Announce Type: cross Abstract: Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. W

Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks

model-releasesarxiv-cs-cl

arXiv:2605.01417v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insuff

MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio

model-releasesarxiv-cs-cl

arXiv:2605.00969v1 Announce Type: cross Abstract: We present MedMosaic, a medical audio question-answering dataset designed to benchmark language and audio reasoning models under realistic clinical co

MedScribe: Clinically Grounded CT Reporting through Agentic Workflows

arXiv:2605.01779v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown potential for automated radiology report generation, yet existing approaches rely on global embedding compressi

agentsarxiv-cs-cv

MemeLens: Multilingual Multitask VLMs for Memes

arXiv:2601.12539v3 Announce Type: replace-cross Abstract: Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery

MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents

arXiv:2605.01386v1 Announce Type: new Abstract: Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from informatio

Applications

MER-DG: Modality-Entropy Regularization for Multimodal Domain Generalization

arXiv:2605.01967v1 Announce Type: cross Abstract: Deploying multimodal models in real-world scenarios requires generalization to new environments where recording conditions differ from training, a cha

applicationsarxiv-cs-cv

Mesh Based Simulations with Spatial and Temporal awareness

arXiv:2605.01542v1 Announce Type: new Abstract: Machine Learning surrogates for Computational Fluid Dynamics (CFD), particularly Graph Neural Networks (GNNs) and Transformers, have become a new import

Meta-learning Structure-Preserving Dynamics

model-releasesarxiv-cs-lg

arXiv:2508.11205v2 Announce Type: replace Abstract: Structure-preserving approaches to dynamics discovery have demonstrated great potential for modeling physical systems due to their use of strong ind

Methods, Data, and Conceptual Change: Reflections from Two Quantitative Diachronic Case Studies

arXiv:2605.02052v1 Announce Type: new Abstract: This discussion paper reflects on how quantitative approaches to historical linguistics interact with dataset properties. Drawing on two worked examples

Local Ai

Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumption

arXiv:2605.01137v1 Announce Type: new Abstract: Metric differential privacy (mDP) strengthens local differential privacy (LDP) by scaling noise to semantic distance, but many machine learning (ML) sys

local-aiarxiv-cs-lg

Metric Unreliability in Multimodal Machine Unlearning: A Systematic Analysis and Principled Unified Score

model-releasesarxiv-cs-cv

arXiv:2605.02206v1 Announce Type: new Abstract: Machine unlearning in Vision-Language Models (VLMs) is required for compliance with the General Data Protection Regulation (GDPR), yet current evaluatio

Mextsuperscript{4}Fuse: Lightweight State-Space MoE with a Cross-Scale Gating Bridge for Brain Tumor Segmentation

model-releasesarxiv-cs-cv

arXiv:2605.02444v1 Announce Type: new Abstract: Encoder-decoder imbalance and the reliance on large input volumes make many 3D brain tumor segmentation models both compute-heavy and brittle. We presen

Middle-mile logistics through the lens of goal-conditioned reinforcement learning

arXiv:2605.02461v1 Announce Type: cross Abstract: Middle-mile logistics describes the problem of routing parcels through a network of hubs linked by trucks with finite capacity. We rephrase this as a

Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2605.00123v1 Announce Type: new Abstract: Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts. Because we lack a robust understa

Minimizing Collateral Damage in Activation Steering

arXiv:2605.01167v1 Announce Type: new Abstract: Activation steering is a method for controlling Large Language Model (LLM) behavior by intervening in its internal representations to increase the align

Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference

model-releasesarxiv-cs-lg

arXiv:2605.01579v1 Announce Type: cross Abstract: Empirical causal claims depend on many analyst decisions, from selecting covariates to choosing estimators. Existing robustness tools summarize how re

MIRA: A Score for Conditional Distribution Accuracy and Model Comparison

arXiv:2605.02014v1 Announce Type: cross Abstract: We introduce Mira, a sample-based score for assessing the accuracy of a candidate conditional distribution using only joint samples from the true data

MIRL: Mutual Information-Guided Reinforcement Learning for Vision-Language Models

arXiv:2605.01520v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) frequently suffer from visual perception errors and hallucinations that compromise answer accuracy in complex reasoning

Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability

arXiv:2605.01987v1 Announce Type: new Abstract: We study differential privacy (DP) in Graph Convolutional Networks (GCNs) through the framework of extit{subsampling stability}. We derive upper bounds

Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling

arXiv:2605.01676v1 Announce Type: cross Abstract: Missing data imputation remains a fundamental challenge in modern data science, especially when uncertainty quantification is essential. In this work,

Mitigating Misalignment Contagion by Steering with Implicit Traits

arXiv:2605.02751v1 Announce Type: cross Abstract: Language models (LMs) are increasingly used in high-stakes, multi-agent settings, where following instructions and maintaining value alignment are cri

safetyarxiv-cs-cl

Mitigating Multimodal LLMs Hallucinations via Relevance Propagation at Inference Time

arXiv:2605.01766v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have revolutionized the landscape of AI, demonstrating impressive capabilities in tackling complex vision and

researcharxiv-cs-cv