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Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environments

arXiv:2604.06481v1 Announce Type: cross Abstract: This study introduces a hybrid deep learning model for intrusion detection in Industrial IoT (IIoT) systems, combining ResNet-1D, BiGRU, and Multi-Hea

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Model Releases

HyperMem: Hypergraph Memory for Long-Term Conversations

DGX agent

arXiv:2604.08256v1 Announce Type: new Abstract: Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across exte

model-releasesarxiv-cs-cl

10 Apr 2026

IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

arXiv:2604.07709v1 Announce Type: cross Abstract: Ask a frontier model how to taper six milligrams of alprazolam (psychiatrist retired, ten days of pills left, abrupt cessation causes seizures) and it

Illocutionary Explanation Planning for Source-Faithful Explanations in Retrieval-Augmented Language Models

arXiv:2604.06211v1 Announce Type: cross Abstract: Natural language explanations produced by large language models (LLMs) are often persuasive, but not necessarily scrutable: users cannot easily verify

Image-Guided Geometric Stylization of 3D Meshes

arXiv:2604.07795v1 Announce Type: new Abstract: Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control

Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation

arXiv:2405.03420v2 Announce Type: cross Abstract: This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using gradient-based Ne

Improved Evidence Extraction and Metrics for Document Inconsistency Detection with LLMs

arXiv:2601.02627v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss

arXiv:2402.08267v3 Announce Type: replace Abstract: Image coding for machines (ICM) aims to compress images for machine analysis using recognition models rather than human vision. Hence, in ICM, it is

Improving Robustness In Sparse Autoencoders via Masked Regularization

arXiv:2604.06495v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are widely used in mechanistic interpretability to project LLM activations onto sparse latent spaces. However, sparsity alo

Improving Semantic Uncertainty Quantification in Language Model Question-Answering via Token-Level Temperature Scaling

arXiv:2604.07172v1 Announce Type: new Abstract: Calibration is central to reliable semantic uncertainty quantification, yet prior work has largely focused on discrimination, neglecting calibration. As

safetyarxiv-cs-lg

ImVideoEdit: Image-learning Video Editing via 2D Spatial Difference Attention Blocks

arXiv:2604.07958v1 Announce Type: new Abstract: Current video editing models often rely on expensive paired video data, which limits their practical scalability. In essence, most video editing tasks c

In-Context Decision Making for Optimizing Complex AutoML Pipelines

arXiv:2508.13657v2 Announce Type: replace-cross Abstract: Combined Algorithm Selection and Hyperparameter Optimization (CASH) has been fundamental to traditional AutoML systems. However, with the adva

In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads

arXiv:2604.06356v1 Announce Type: cross Abstract: In-Context Learning (ICL) has been extensively studied in text-only Language Models, but remains largely unexplored in the speech domain. Here, we inv

Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models

arXiv:2604.06263v1 Announce Type: cross Abstract: Generative advertising in large language model (LLM) responses requires optimizing sponsorship configurations under two strict constraints: the strate

Incorporating Social Awareness into Control of Unknown Multi-Agent Systems: A Real-Time Spatiotemporal Tubes Approach

arXiv:2510.25597v2 Announce Type: replace-cross Abstract: This paper presents a decentralized control framework that incorporates social awareness into multi-agent systems with unknown dynamics to ach

safetyarxiv-cs-ro

Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation

arXiv:2604.07945v1 Announce Type: new Abstract: As the demand for mobile robots continues to increase, social navigation has emerged as a critical task, driving active research into deep reinforcement

safetyarxiv-cs-ro

Inference-Time Code Selection via Symbolic Equivalence Partitioning

arXiv:2604.06485v1 Announce Type: cross Abstract: 'Best-of-N' selection is a popular inference-time scaling method for code generation using Large Language Models (LLMs). However, to reliably identify

Tutorials

Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement

arXiv:2507.08390v4 Announce Type: replace Abstract: Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-s

tutorialsarxiv-cs-lg

Information as Structural Alignment: A Dynamical Theory of Continual Learning

arXiv:2604.07108v1 Announce Type: cross Abstract: Catastrophic forgetting is not an engineering failure. It is a mathematical consequence of storing knowledge as global parameter superposition. Existi

Informed Hybrid Zonotope-based Motion Planning Algorithm

arXiv:2507.09309v4 Announce Type: replace Abstract: Optimal path planning in nonconvex free spaces poses substantial computational challenges. A common approach formulates such problems as mixed-integ

researcharxiv-cs-ro

Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions

arXiv:2602.09987v5 Announce Type: replace-cross Abstract: Influence functions are commonly used to attribute model behavior to training documents. We explore the reverse: crafting training data that i

Initialisation Determines the Basin: Efficient Codebook Optimisation for Extreme LLM Quantization

arXiv:2604.08118v1 Announce Type: new Abstract: Additive quantization enables extreme LLM compression with O(1) lookup-table dequantization, making it attractive for edge deployment. Yet at 2-bit prec

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings

arXiv:2604.08192v1 Announce Type: cross Abstract: Reliable generalization metrics are fundamental to the evaluation of machine learning models. Especially in high-stakes applications where labeled tar

safetyarxiv-cs-cv

Instance-Adaptive Parametrization for Amortized Variational Inference

arXiv:2604.06796v1 Announce Type: cross Abstract: Latent variable models, including variational autoencoders (VAE), remain a central tool in modern deep generative modeling due to their scalability an

InstAP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding

model-releasesarxiv-cs-cv

arXiv:2604.08337v1 Announce Type: new Abstract: Current vision-language pre-training (VLP) paradigms excel at global scene understanding but struggle with instance-level reasoning due to global-only s

Interpretable Tau-PET Synthesis from Multimodal T1-Weighted and FLAIR MRI Using Partial Information Decomposition Guided Disentangled Quantized Half-UNet

arXiv:2602.22545v2 Announce Type: replace Abstract: Tau positron emission tomography (tau-PET) is an important in vivo biomarker of Alzheimer's disease, but its cost, limited availability, and acquisi

Interventional Time Series Priors for Causal Foundation Models

arXiv:2603.11090v2 Announce Type: replace Abstract: Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remain

researcharxiv-cs-lg

Invisible Influences: Investigating Implicit Intersectional Biases through Persona Engineering in Large Language Models

arXiv:2604.06213v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at human-like language generation but often embed and amplify implicit, intersectional biases, especially under per

Invisible to Humans, Triggered by Agents: Stealthy Jailbreak Attacks on Mobile Vision-Language Agents

arXiv:2510.07809v4 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) empower autonomous mobile agents, yet their security under realistic mobile deployment constraints remain

safetyarxiv-cs-ai

Iterative Formalization and Planning in Partially Observable Environments

arXiv:2505.13126v3 Announce Type: replace-cross Abstract: Using LLMs not to predict plans but to formalize an environment into the Planning Domain Definition Language (PDDL) has been shown to improve

researcharxiv-cs-cl

Iteratively Learning Muscle Memory for Legged Robots to Master Adaptive and High Precision Locomotion

arXiv:2507.13662v2 Announce Type: replace Abstract: This paper presents a scalable and adaptive control framework for legged robots that integrates Iterative Learning Control (ILC) with a biologically

researcharxiv-cs-ro

JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency

arXiv:2604.03044v2 Announce Type: replace-cross Abstract: We introduce JoyAI-LLM Flash, an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performan

k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS

arXiv:2604.03815v2 Announce Type: replace-cross Abstract: Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties i

k-server-bench: Automating Potential Discovery for the k-Server Conjecture

arXiv:2604.07240v1 Announce Type: cross Abstract: We introduce a code-based challenge for automated, open-ended mathematical discovery based on the k-server conjecture, a central open problem in com

Karma Mechanisms for Decentralised, Cooperative Multi Agent Path Finding

arXiv:2604.07970v1 Announce Type: cross Abstract: Multi-Agent Path Finding (MAPF) is a fundamental coordination problem in large-scale robotic and cyber-physical systems, where multiple agents must co

safetyarxiv-cs-ro

Kathleen: Oscillator-Based Byte-Level Text Classification Without Tokenization or Attention

arXiv:2604.07969v1 Announce Type: new Abstract: We present Kathleen, a text classification architecture that operates directly on raw UTF-8 bytes using frequency-domain processing -- requiring no toke

KD-MARL: Resource-Aware Knowledge Distillation in Multi-Agent Reinforcement Learning

arXiv:2604.06691v1 Announce Type: new Abstract: Real world deployment of multi agent reinforcement learning MARL systems is fundamentally constrained by limited compute memory and inference time. Whil

safetyarxiv-cs-ai

KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenance

arXiv:2510.05524v2 Announce Type: replace Abstract: We present Knowledge Extraction on OMIn (KEO), a domain-specific knowledge extraction and reasoning framework with large language models (LLMs) in s

KITE: Keyframe-Indexed Tokenized Evidence for VLM-Based Robot Failure Analysis

arXiv:2604.07034v1 Announce Type: cross Abstract: We present KITE, a training-free, keyframe-anchored, layout-grounded front-end that converts long robot-execution videos into compact, interpretable t

Knowledge Graphs Generation from Cultural Heritage Texts: Combining LLMs and Ontological Engineering for Scholarly Debates

arXiv:2511.10354v1 Announce Type: cross Abstract: Cultural Heritage texts contain rich knowledge that is difficult to query systematically due to the challenges of converting unstructured discourse in

Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency

model-releasesarxiv-cs-cv

arXiv:2604.07904v1 Announce Type: cross Abstract: Spatiotemporal neural dynamics and oscillatory synchronization are widely implicated in biological information processing and have been hypothesized t

KV Cache Offloading for Context-Intensive Tasks

arXiv:2604.08426v1 Announce Type: cross Abstract: With the growing demand for long-context LLMs across a wide range of applications, the key-value (KV) cache has become a critical bottleneck for both

LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation

arXiv:2604.08475v1 Announce Type: new Abstract: Human-like generalization in open-world remains a fundamental challenge for robotic manipulation. Existing learning-based methods, including reinforceme

Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains

arXiv:2602.13235v2 Announce Type: replace-cross Abstract: Visual Retrieval-Augmented Generation (VRAG) enhances Vision-Language Models (VLMs) by incorporating external visual documents to address a gi

LangDriveCTRL: Natural Language Controllable Driving Scene Editing with Multi-modal Agents

arXiv:2512.17445v2 Announce Type: replace Abstract: LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represe

safetyarxiv-cs-cv

Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning

arXiv:2604.07941v1 Announce Type: new Abstract: Post-training has become central to turning pretrained large language models (LLMs) into aligned and deployable systems. Recent progress spans supervise

safetyarxiv-cs-cl

Applications

Large Language Models for Outpatient Referral: Problem Definition, Benchmarking and Challenges

arXiv:2503.08292v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly applied to outpatient referral tasks across healthcare systems. However, there is a lack of stan

applicationsarxiv-cs-ai

Hardware

LAsset: An LLM-assisted Security Asset Identification Framework for System-on-Chip (SoC) Verification

arXiv:2601.02624v2 Announce Type: replace-cross Abstract: The growing complexity of modern system-on-chip (SoC) and IP designs is making security assurance difficult day by day. One of the fundamental

hardwarearxiv-cs-ai