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mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties

arXiv:2605.31296v1 Announce Type: cross Abstract: Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regio

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Multi-Agent Teams Hold Experts Back

DGX agent

arXiv:2602.01011v4 Announce Type: replace-cross Abstract: Multi-agent LLM systems are increasingly deployed as autonomous collaborators, where agents interact freely rather than execute fixed, pre-spe

safetyarxiv-cs-ai

1 Jun 2026

Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs

arXiv:2605.31027v1 Announce Type: new Abstract: We propose a novel neural network architecture, termed Multi-Scale Separable Fourier Neural Networks (MS-SFNN), for the accurate and efficient solution

Agents

Multi-Turn Multi-Agent Dialogue for Collaborative Reconstruction Improves VLM Performance on Spatial Reasoning, But Only Barely

arXiv:2605.31387v1 Announce Type: new Abstract: Robots operating in diverse environments rely on visual input to interpret objects and spatial layouts. In human-collaborative tasks, they are expected

agentsarxiv-cs-cl

MultiAct: Text-to-Motion Generation from Composite Text via Tailored Attention Guidance

arXiv:2605.30925v1 Announce Type: new Abstract: Text-to-motion generation has progressed rapidly in recent years, offering an expressive interface for animation and human-computer interaction. However

researcharxiv-cs-cv

Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages

arXiv:2605.31136v1 Announce Type: new Abstract: In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, t

researcharxiv-cs-cl

Multimodal Fusion via Self-Consistent Task-Gradient Fields

arXiv:2410.15475v2 Announce Type: replace Abstract: Multimodal learning aims to preserve as much task-related information as possible from different inputs. However, current fusion designs often disto

researcharxiv-cs-cv

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models

arXiv:2511.16940v3 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) pose significant individual-level privacy risks by linking fragmented multimodal data to identifiable individua

Multivariate Distributional Reinforcement Learning Using Sliced Divergences

arXiv:2605.31222v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) models the full return distribution rather than expectations, but extending it to multivariate settings rema

Native Hierarchical and Compositional Representations with Subspace Embeddings

arXiv:2508.16687v2 Announce Type: replace Abstract: Traditional embeddings represent datapoints as vectors, which makes similarity easy to compute but limits how well they capture hierarchies and comp

Neither Replacement nor Panacea: Comparing LLM-Based Conversational and Graphical Decision Support in Industrial Tasks

arXiv:2605.31287v1 Announce Type: cross Abstract: Managers in manufacturing settings rely on digital interfaces to interpret operational data for decision-making, but growing data volume and complexit

Agents

NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents

arXiv:2601.21372v2 Announce Type: replace Abstract: We present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implem

agentsarxiv-cs-ai

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs

model-releasesarxiv-cs-cl

arXiv:2505.17595v4 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve impressive performance across domains but face significant challenges when deployed on consumer-grade GPU

Local Ai

Neuro-Symbolic Predictive Process Monitoring

arXiv:2509.00834v2 Announce Type: replace Abstract: This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitori

local-aiarxiv-cs-ai

Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm

arXiv:2605.31421v1 Announce Type: cross Abstract: In this paper, we show the possibility of a direct injection of algorithms into neural network architecture. We focus on a complex algorithm, that is,

Neuron-Level Interventions for Gendered and Gender-Neutral Generation in Language Models

arXiv:2605.30717v1 Announce Type: new Abstract: Language models (LMs) can produce gendered language and stereotypes even when given neutral prompts. Most prior work on gender bias in LMs primarily exa

safetyarxiv-cs-cl

Hardware

Neurosim: A Fast Simulator for Neuromorphic Robot Perception

arXiv:2602.15018v2 Announce Type: replace-cross Abstract: Neurosim is a fast, real-time, high-performance library for simulating sensors such as dynamic vision sensors, RGB cameras, depth sensors, and

hardwarearxiv-cs-cv

NGDBench: Towards Neural Graph Data Management

arXiv:2603.05529v2 Announce Type: replace-cross Abstract: Data critical to real-world decision-making is increasingly found within organizations. Such data is heterogeneous, constantly evolving, and o

Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework

arXiv:2605.31309v1 Announce Type: new Abstract: We survey Lyapunov-based techniques for the finite-time analysis of stochastic iterative algorithms, also known as stochastic approximation (SA) algorit

Non-destructive Identification of Oyster Species is possible from Hyperspectral Images with Machine Learning

arXiv:2605.30811v1 Announce Type: new Abstract: Differentiating between oyster species is important for developing new commercial oyster species suited to production systems and is critical for tracea

applicationsarxiv-cs-lg

Non-Parametric Probabilistic Robustness: A Conservative Risk Estimator under Unknown Perturbation Distributions

arXiv:2511.17380v2 Announce Type: replace Abstract: Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motiva

researcharxiv-cs-cv

Not All Synthetic Data Is Yours to Learn From

arXiv:2605.31126v1 Announce Type: cross Abstract: Can a language model improve from plain text sampled from itself, with no prompts, no teacher, no verifier, and no reward model? Yes, but only when th

Notes-to-Self: Scratchpad Augmented VLAs for Memory Dependent Manipulation Tasks

arXiv:2602.21013v2 Announce Type: replace Abstract: Many dexterous manipulation tasks are non-markovian in nature, yet little attention has been paid to this fact in the recent upsurge of the vision-l

applicationsarxiv-cs-ro

Agents

NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving

arXiv:2605.31116v1 Announce Type: new Abstract: Recent perception-free end-to-end (E2E) autonomous driving methods bypass explicit perception outputs by compressing dense image patch tokens into compa

agentsarxiv-cs-cv

NumLeak: Public Numeric Benchmarks as Latent Labels in Foundation Models

arXiv:2605.30393v1 Announce Type: cross Abstract: Public numeric benchmarks appear in pretraining, so an evaluation that conditions on a date may be measuring memorized recall rather than out-of-sampl

nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving

arXiv:2605.31572v1 Announce Type: new Abstract: Reasoning is essential for autonomous driving (AD) in long-tail scenarios, where vehicles must apply commonsense knowledge, understand spatial relations

OBCache: Optimal Brain KV Cache Pruning for Efficient Long-Context LLM Inference

arXiv:2510.07651v2 Announce Type: replace-cross Abstract: Large language models (LLMs) with extended context windows enable powerful applications but impose significant memory overhead, as caching all

Object-Informed Model Predictive Path Integral Control for Non-Prehensile Robot Manipulation

arXiv:2605.30778v1 Announce Type: new Abstract: Long-horizon planning for non-prehensile robot manipulation is challenging due to underactuated and discontinuous interactions. We propose a hierarchica

researcharxiv-cs-ro

OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries

arXiv:2605.06235v2 Announce Type: replace-cross Abstract: Retrieval benchmarks are increasingly saturating, but we argue that efficient search is far from a solved problem. We identify a class of quer

OLG++: A Semantic Extension of Obligation Logic Graph

arXiv:2507.05488v2 Announce Type: replace Abstract: We present OLG++, a semantic extension of the Obligation Logic Graph (OLG) for modeling regulatory and legal rules in municipal and interjurisdictio

Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning

arXiv:2605.30969v1 Announce Type: new Abstract: Text-based human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the consistency of

OmniMem: Scalable and Adaptive Memory Retrieval for Long Video Generation

arXiv:2605.30519v1 Announce Type: new Abstract: Autoregressive (AR) video generation extends videos by producing latent chunks sequentially, but scaling to long videos requires repeated access to a gr

safetyarxiv-cs-cv

On-Device Generative AI for GDPR-Compliant Visual Monitoring: Natural Language Alerts from Local Object Detection

arXiv:2605.30544v1 Announce Type: new Abstract: Visual monitoring systems that rely on cloud-based AI inference expose raw image data to external services, creating fundamental tensions with the data-

Local Ai

On-Device Robotic Planning: Eliminating Inference Redundancy for Efficient Decision-Making

arXiv:2605.31460v1 Announce Type: new Abstract: Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a

local-aiarxiv-cs-ro

Hardware

On Efficient Scaling of GNNs via IO-Aware Layers Implementations

arXiv:2605.31500v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are bottlenecked by sparse, irregular memory access. Popular frameworks such as DGL and PyTorch Geometric support general

hardwarearxiv-cs-ai

On Revisiting Entropy for Identifying Mislabeled Images

arXiv:2605.31090v1 Announce Type: cross Abstract: Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labe

On the Illusion of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes

arXiv:2501.12020v2 Announce Type: replace Abstract: Face recognition systems (FRS) exhibit significant accuracy differences based on the user's gender. Since such a gender gap reduces the trustworthin

safetyarxiv-cs-cv

On the impact of retrieved content representations in RAG Pipelines

arXiv:2605.30790v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) supplements a language model's input with retrieved documents, yet most RAG pipelines inherit retrieval component

On the 'Induction Bias' in Sequence Models

arXiv:2602.18333v2 Announce Type: replace-cross Abstract: Despite the remarkable practical success of transformer-based language models, recent work has raised concerns about their ability to perform

safetyarxiv-cs-cl

On the regularization of Wasserstein GANs

arXiv:1709.08894v3 Announce Type: replace-cross Abstract: Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unl

On the Relationship Between Activation Outliers and Feature Death in Sparse Autoencoders

arXiv:2605.31518v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose neural network activations into interpretable features, but many learned features never activate, a problem called

safetyarxiv-cs-lg

On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets

arXiv:2605.31142v1 Announce Type: cross Abstract: Large-scale multilingual text embedding models play crucial role in both research and industry, yet their behavior in language-specific, multi-task se

OpenSTBench: Beyond Semantic Evaluation for Speech Translation

arXiv:2605.30792v1 Announce Type: cross Abstract: Speech translation systems increasingly span speech-to-text translation (S2TT), speech-to-speech translation (S2ST), offline translation, and streamin

Optimizing Rank for High-Fidelity Implicit Neural Representations

arXiv:2512.14366v2 Announce Type: replace Abstract: Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-freq

safetyarxiv-cs-cv

OrcaRouter: A Production-Oriented LLM Router with Hybrid Offline-Online Learning

arXiv:2605.30736v1 Announce Type: cross Abstract: The rapid development of large language models, each with distinct capabilities and inference costs, raises a practical deployment question: given an

Organizational Adaptation to Generative AI in Cybersecurity

arXiv:2506.12060v2 Announce Type: replace-cross Abstract: Cybersecurity organizations are adapting to GenAI integration through modified frameworks and hybrid operational processes, with success influ

safetyarxiv-cs-ai

Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression

arXiv:2512.00919v2 Announce Type: replace-cross Abstract: We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regres

PAC-Bayesian Reinforcement Learning Trains Generalizable Policies

arXiv:2510.10544v3 Announce Type: replace-cross Abstract: We derive a novel PAC-Bayesian generalization bound for reinforcement learning that explicitly accounts for Markov dependencies in the data, t

safetyarxiv-cs-ai