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Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting

arXiv:2604.24788v1 Announce Type: new Abstract: Natural gas is undoubtedly an essential component of the global energy system. Accurate short-term forecasting of natural gas price is challenging due t

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LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation

DGX agent

arXiv:2604.25665v1 Announce Type: new Abstract: Reliable evaluation of large language model (LLM)-generated summaries remains an open challenge, particularly across heterogeneous domains and document

model-releasesarxiv-cs-cl

29 Apr 2026

Logic of Fuzzy Paths

arXiv:2604.24907v1 Announce Type: cross Abstract: We introduce a new family of temporal logics intended for specifications in motion planning (MP). It builds upon the signal temporal logic (STL), whic

researcharxiv-cs-ro

LongSumEval: Question-Answering Based Evaluation and Feedback-Driven Refinement for Long Document Summarization

arXiv:2604.25130v1 Announce Type: new Abstract: Evaluating long document summaries remains the primary bottleneck in summarization research. Existing metrics correlate weakly with human judgments and

Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling

arXiv:2604.25860v1 Announce Type: new Abstract: Machine-generated text (MGT) detection requires identifying structurally invariant signals across generation models, rather than relying on model-specif

local-aiarxiv-cs-cl

M^3-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering

arXiv:2604.25122v1 Announce Type: new Abstract: We present M^3-VQA, a novel knowledge-based Visual Question Answering (VQA) benchmark, to enhance the evaluation of multimodal large language models (ML

Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures

arXiv:2604.25817v1 Announce Type: new Abstract: Magnification shift is a major obstacle to robust histopathology classification, because models trained on one imaging scale often generalize poorly to

MAIC-UI: Making Interactive Courseware with Generative UI

arXiv:2604.25806v1 Announce Type: new Abstract: Creating interactive STEM courseware traditionally requires HTML/CSS/JavaScript expertise, leaving barriers for educators. While generative AI can produ

safetyarxiv-cs-cl

Making AI-Assisted Grant Evaluation Auditable without Exposing the Model

arXiv:2604.25200v1 Announce Type: cross Abstract: Public agencies are beginning to consider large language models (LLMs) as decision-support tools for grant evaluation. This creates a practical govern

Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling

arXiv:2604.25578v1 Announce Type: new Abstract: We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only

Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile

arXiv:2604.25765v1 Announce Type: new Abstract: The quality of training data is critical to the performance of machine learning models. In this paper, the Error Sensitivity Profile (ESP) is proposed.

Measuring the stability and plasticity of recommender systems

arXiv:2508.03941v3 Announce Type: replace-cross Abstract: The typical offline protocol to evaluate recommendation algorithms is to collect a dataset of user-item interactions and then use a part of th

Metric, inertially aligned monocular state estimation via kinetodynamic priors

arXiv:2511.20496v3 Announce Type: replace Abstract: Accurate state estimation for flexible robotic systems poses significant challenges, particularly for platforms with dynamically deforming structure

researcharxiv-cs-ro

MGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation

arXiv:2601.21225v2 Announce Type: replace Abstract: Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation has lagge

MGTEVAL: An Interactive Platform for Systemtic Evaluation of Machine-Generated Text Detectors

arXiv:2604.25152v1 Announce Type: cross Abstract: We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detectio

MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition

arXiv:2512.07348v2 Announce Type: replace Abstract: In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., Multi-Image Composition (MICo),

Agents

MiMo-Embodied: X-Embodied Foundation Model Technical Report

arXiv:2511.16518v2 Announce Type: replace-cross Abstract: We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in

agentsarxiv-cs-cl

minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation

arXiv:2604.24805v1 Announce Type: new Abstract: Modern machine learning optimizes for accuracy without explicitly accounting for internal computational cost, even though physical and biological system

Minimax Generalized Cross-Entropy

arXiv:2603.19874v3 Announce Type: replace-cross Abstract: Loss functions play a central role in supervised classification. Cross-entropy (CE) is widely used, whereas the mean absolute error (MAE) loss

Mitigating Coordinate Prediction Bias from Positional Encoding Failures

arXiv:2510.22102v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) excel at general vision-language tasks, precise coordinate prediction remains a significant cha

MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding

arXiv:2512.17492v2 Announce Type: replace Abstract: Geo-spatial analysis of our world benefits from a multimodal approach, as every single geographic location can be described in numerous ways (images

MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment

arXiv:2603.15954v2 Announce Type: replace Abstract: Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The

local-aiarxiv-cs-lg

Modeling Human-Like Color Naming Behavior in Context

arXiv:2604.25674v1 Announce Type: new Abstract: Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both le

MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts

arXiv:2411.14721v2 Announce Type: replace Abstract: Molecule discovery is a pivotal research field, impacting everything from medicine to materials. Recently, Large Language Models (LLMs) have been wi

safetyarxiv-cs-cl

Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing

arXiv:2604.24880v1 Announce Type: cross Abstract: This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic

Applications

MotionBricks: Scalable Real-Time Motions with Modular Latent Generative Model and Smart Primitives

arXiv:2604.24833v1 Announce Type: cross Abstract: Despite transformative advances in generative motion synthesis, real-time interactive motion control remains dominated by traditional techniques. In t

applicationsarxiv-cs-lg

MTPano: Multi-Task Panoramic Scene Understanding via Label-Free Integration of Dense Prediction Priors

arXiv:2602.05330v2 Announce Type: replace Abstract: Comprehensive panoramic scene understanding is critical for immersive applications, yet it remains challenging due to the scarcity of high-resolutio

Multi-layer Cross-Attention is Provably Optimal for Multi-modal In-context Learning

arXiv:2602.04872v2 Announce Type: replace-cross Abstract: Recent progress has rapidly advanced our understanding of the mechanisms underlying in-context learning in modern attention-based neural netwo

Multimodal Contextualized Support for Enhancing Video Retrieval System

arXiv:2412.07584v2 Announce Type: replace Abstract: Current video retrieval systems, especially those used in competitions, primarily focus on querying individual keyframes or images rather than encod

Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation

arXiv:2604.25819v1 Announce Type: new Abstract: In this work, we propose Mutual Forcing, a framework for fast autoregressive audio-video generation with long-horizon audio-video synchronization. Our a

Named Entity Recognition of Historical Texts via Large Language Model

arXiv:2508.18090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. On

Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models at the Nishimori Temperature

arXiv:2508.18717v3 Announce Type: replace-cross Abstract: Modern multi-class image classification uses high-dimensional CNN features that incur large memory and computational costs and obscure the dat

Nautile-370M: Spectral Memory Meets Attention in a Small Reasoning Model

arXiv:2604.24809v1 Announce Type: new Abstract: We present Nautile-370M, a 371-million-parameter small language model designed for efficient reasoning under strict parameter and inference budgets. Nau

Navigating Global AI Regulation: A Multi-Jurisdictional Retrieval-Augmented Generation System

arXiv:2604.25448v1 Announce Type: new Abstract: Navigating AI regulation across jurisdictions is increasingly difficult for policymakers, legal professionals, and researchers. To address this, we pres

safetyarxiv-cs-cl

Applications

Near-Optimal Sample Complexities of Divergence-based S-rectangular Distributionally Robust Reinforcement Learning

arXiv:2505.12202v3 Announce Type: replace Abstract: Distributionally robust reinforcement learning (DR-RL) has recently gained significant attention as a principled approach that addresses discrepanci

applicationsarxiv-cs-lg

Applications

Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision

arXiv:2604.24824v1 Announce Type: new Abstract: This article philosophically examines how shifts in assumptions regarding the existence and non-existence of the true target (TT) give rise to new persp

applicationsarxiv-cs-lg

Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence

arXiv:2604.24954v1 Announce Type: cross Abstract: We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, i

NimbleReg: A light-weight deep-learning framework for diffeomorphic image registration

arXiv:2503.07768v2 Announce Type: replace Abstract: This paper presents NimbleReg, a light-weight deep-learning (DL) framework for diffeomorphic image registration leveraging surface representation of

safetyarxiv-cs-cv

No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control

arXiv:2604.25887v1 Announce Type: new Abstract: Current pedestrian crossing signals operate on fixed timing without adjustment to pedestrian behavior, which can leave vulnerable road users (VRUs) such

safetyarxiv-cs-cv

Novel 3D Binary Indexed Tree for Volume Computation of 3D Reconstructed Models from Volumetric Data

arXiv:2412.10441v2 Announce Type: replace-cross Abstract: In the burgeoning field of medical imaging, precise computation of 3D volume holds a significant importance for subsequent qualitative analysi

NUBO: A Transparent Python Package for Bayesian Optimization

arXiv:2305.06709v4 Announce Type: replace Abstract: NUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimization framework for the optimization of expensive-to-evaluate black

Null Measurability at the Symmetrization Interface in VC Learning

arXiv:2604.25028v1 Announce Type: new Abstract: Recent work revisiting measurability in the fundamental theorem of statistical learning imposes Borel measurability of ghost-gap suprema. We show that,

Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks

arXiv:2604.24964v1 Announce Type: cross Abstract: Existing web agent benchmarks have largely converged on short, single-site tasks that frontier models are approaching saturation on. However, real wor

OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning

arXiv:2508.16198v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have increasingly supported omni-modal processing across text, vision, and speech. However, existing evalua

OmniAlpha: Aligning Transparency-Aware Generation via Multi-Task Unified Reinforcement Learning

arXiv:2511.20211v2 Announce Type: replace Abstract: Transparency-aware generation requires modeling not only RGB appearance but also alpha-based opacity and cross-layer composition, which are essentia

local-aiarxiv-cs-cv

OmniVTG: A Large-Scale Dataset and Training Paradigm for Open-World Video Temporal Grounding

arXiv:2604.25276v1 Announce Type: new Abstract: Video Temporal Grounding (VTG), the task of localizing video segments from text queries, struggles in open-world settings due to limited dataset scale a

local-aiarxiv-cs-cv

On Halting vs Converging in Recurrent Graph Neural Networks

arXiv:2604.25551v1 Announce Type: new Abstract: Recurrent Graph Neural Networks (RGNNs) extend standard GNNs by iterating message-passing until some stopping condition is met. Various RGNN models have

local-aiarxiv-cs-lg

On quantitative Laplace-type convergence results for some exponential probability measures, with two applications

arXiv:2110.12922v2 Announce Type: replace-cross Abstract: Laplace-type results characterize the limit of sequence of measures (pi_arepsilon)_{arepsilon >0} with density w.r.t the Lebesgue measure (d p