All entries

Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning

arXiv:2608.16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification. In ensemble learning, Hoeffding Trees are widely adopted as b

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tutorials

HOIMask: Towards Generative Masked Modeling for Human Object Interaction Generation

DGX agent

arXiv:2608.15141v1 Announce Type: new Abstract: Diffusion-based methods have dominated the HOI generation, as they enable critical contact fusions or signals to guide the diffusion process. However, t

tutorialsarxiv-cs-cv

18 Aug 2026

Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers

arXiv:2606.21037v2 Announce Type: replace-cross Abstract: The empirical foundation of cyber deception relies on human-centered hypotheses, but the rapid emergence of autonomous, AI-enabled attackers c

Agents

How Do Agents Fail on AutoResearch: End-to-End Diagnostic Evaluation on 100 Real-World Frontier Research Tasks

arXiv:2608.14905v1 Announce Type: new Abstract: AI has long assisted scientific research, but the rapid advance of LLMs and agentic scaffolds is reshaping the landscape; a single system can now carry

agentsarxiv-cs-cl

How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAM

arXiv:2608.15840v1 Announce Type: cross Abstract: We study how many observations are needed to determine the causal direction between two linearly related variables. Classical LiNGAM theory shows that

How Sampling Strategy Affects Imbalance Mitigation in LiDAR Segmentation: A Study of Structured vs. Random Point-Based Architectures

model-releasesarxiv-cs-cv

arXiv:2608.16673v1 Announce Type: new Abstract: Class imbalance in LiDAR point clouds poses challenges for semantic segmentation in autonomous navigation and urban mapping. While 2D vision has numerou

HP2-SLAM: Adaptive Hybrid ICP for Robust and Efficient LiDAR SLAM

model-releasesarxiv-cs-ro

arXiv:2608.14996v1 Announce Type: new Abstract: Achieving robustness, accuracy, and efficiency simultaneously remains a central challenge in light detection and ranging (LiDAR) simultaneous localizati

Local Ai

HTTM: Head-wise Temporal Token Merging for Faster VGGT

arXiv:2511.21317v2 Announce Type: replace Abstract: The Visual Geometry Grounded Transformer (VGGT) marks a significant leap forward in 3D scene reconstruction, as it is the first model that directly

local-aiarxiv-cs-cv

Tutorials

Human Pose Estimation in Trampoline Gymnastics: How to Improve Performance on Extreme Poses

arXiv:2604.01322v2 Announce Type: replace Abstract: Trampoline gymnastics involves extreme human poses and uncommon viewpoints, on which state-of-the art pose estimation models tend to under-perform.

tutorialsarxiv-cs-cv

Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines

arXiv:2504.20823v3 Announce Type: replace Abstract: Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems. We propose

Agents

Hybrid System Planning using a Mixed-Integer ADMM Heuristic and Hybrid Zonotopes

arXiv:2602.17574v2 Announce Type: replace Abstract: Embedded optimization-based planning for hybrid systems is challenging due to the use of mixed-integer programming, which is computationally intensi

agentsarxiv-cs-ro

HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation

arXiv:2608.15703v1 Announce Type: new Abstract: Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time. As

Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement

arXiv:2608.16628v1 Announce Type: new Abstract: Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple gra

Agents

HyperSkill: Self-Evolving LLM Agents via Hypergraph-Structured Skill Memory

arXiv:2608.16114v1 Announce Type: new Abstract: As agentic tasks grow in complexity, LLM agents increasingly rely on experiential memory to reuse procedural knowledge across tasks. Effective memory de

agentsarxiv-cs-cl

I-CALM: Incentivizing Confidence-Aware Abstention for LLM Selective Answering

arXiv:2604.03904v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident but incorrect answers, in part because standard evaluation incentives reward guessing ove

I-Perceive: A Foundation Model for Active Perception with Language Instructions

arXiv:2603.00600v2 Announce Type: replace Abstract: Active perception - the ability of a robot to proactively select viewpoints to acquire task-relevant information - is essential for robust operation

safetyarxiv-cs-ro

Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification

arXiv:2608.15731v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandabl

safetyarxiv-cs-ai

Tutorials

Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level

arXiv:2608.14692v1 Announce Type: cross Abstract: Personalized, generative AI systems increasingly adapt their behavior to individual users over time, fundamentally changing model behavior. While exis

tutorialsarxiv-cs-ai

Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network

arXiv:2608.15198v1 Announce Type: cross Abstract: Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PEN

iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration

arXiv:2608.14646v1 Announce Type: cross Abstract: Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform

Image Denoising via the Adaptive Rank-Cluster Filter

arXiv:2608.15298v1 Announce Type: new Abstract: A spatial-local image-denoising filter is proposed, and its performance metrics are evaluated in comparison with baseline filtering algorithms, includin

researcharxiv-cs-cv

Imagining Recovery: Inference-Time Counterfactual Realignment for Vision-Language-Action Models

arXiv:2608.14822v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have improved the flexibility and generality of robotic manipulation, yet they remain fragile to online disruption

safetyarxiv-cs-cv

Implementation of a Metacognition Framework for Self-Awareness and Self-Regulation in Ensembles of LLMs

arXiv:2608.15400v1 Announce Type: new Abstract: Large Language Models (LLMs) are notorious for struggling with assessing their own uncertainty, detecting knowledge conflicts, or recognizing when probl

Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization

arXiv:2608.16492v1 Announce Type: cross Abstract: This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP ba

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities

arXiv:2501.12147v2 Announce Type: replace-cross Abstract: Selecting appropriate training data is crucial for instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong ca

Improving the matrix multiplication exponent with modern optimization and AlphaEvolve

arXiv:2608.16884v1 Announce Type: cross Abstract: The current best bounds on the matrix multiplication exponent omega are obtained through a refinement of the laser method called combination loss anal

In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults

arXiv:2608.14663v1 Announce Type: new Abstract: As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers

In-Context Source and Channel Coding

arXiv:2601.10267v2 Announce Type: replace Abstract: Separate Source-Channel Coding (SSCC) remains attractive for text transmission due to its modularity and compatibility with mature entropy coders an

In Defense of OCTA: The Reconstruction-Utility Gap in OCT-to-OCTA Synthesis

arXiv:2608.15626v1 Announce Type: new Abstract: Optical coherence tomography angiography (OCTA) images retinal blood flow, giving capillary-perfusion and foveal-avascular-zone biomarkers that grade di

Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method

arXiv:2608.14749v1 Announce Type: cross Abstract: Background and Objective: Image-guided surgical navigation has been actively studied because of its advantage of identifying subsurface targets and cr

researcharxiv-cs-cv

Incoherent by Design? On the Moral Self-Consistency of LLMs

arXiv:2608.15354v1 Announce Type: new Abstract: LLMs are increasingly used in morally sensitive contexts, yet it is unclear whether they apply ethical principles consistently across situations. A mode

IndicQE-APE: A Benchmark for Quality Estimation and Automatic Post-Editing for Indic Languages

arXiv:2608.16344v1 Announce Type: new Abstract: Indic quality estimation (QE) and automatic post-editing (APE) data is spread across separate releases, so no single resource supports training and eval

Individual Disempowerment through an Advice Channel: Control Loss when Influence is Endogenous

arXiv:2608.14795v1 Announce Type: new Abstract: An AI that can only give advice seems safe: the human is always free to ignore it. That is the premise of the boxing tradition in AI safety, and its lon

safetyarxiv-cs-ai

Inference-Time Mitigation of Adversarial Political Bias in Large Language Models

arXiv:2608.14629v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become the mainstay for information retrieval and summarization tasks, ensuring that they are always non-partisan and

safetyarxiv-cs-ai

Inferential Evaluation of Surrogate-Derived Models under Covariate Shift

arXiv:2608.15783v1 Announce Type: cross Abstract: In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are

safetyarxiv-cs-lg

Information Geometry of Message Passing

arXiv:2608.15922v1 Announce Type: cross Abstract: We show that the natural-gradient stationary condition of variational inference has an edge-local form on a Forney-style factor graph. We start from t

Information-Theoretic Causal Modelling of Semiconductor Process Dynamics

arXiv:2608.14678v1 Announce Type: cross Abstract: With the progress of the semiconductor industry toward increasingly complex compute devices and tighter process tolerances, advanced process control h

Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations

arXiv:2603.14894v3 Announce Type: replace-cross Abstract: Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model expl

INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval

arXiv:2608.16203v1 Announce Type: cross Abstract: Existing speech retrieval systems rely on fixed similarity matching and cannot adapt to diverse user intents. We introduce INSPIRE, the first benchmar

Integrating Persuasion Theory into the Epidemiological Modelling of Health Misinformation Spread on Social Media

arXiv:2608.15689v1 Announce Type: cross Abstract: This study presents a hybrid epidemiological and behavioural framework to simulate the spread of health misinformation on social media. We extend the

Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach

arXiv:2608.14599v1 Announce Type: cross Abstract: The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks. Yet large-scale BS deployment opt

Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents

arXiv:2608.15755v1 Announce Type: new Abstract: User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing informat

Local Ai

Interactive Whole Slide Images for RL-based Tumour Segmentation

arXiv:2608.16607v1 Announce Type: new Abstract: Whole-slide image (WSI) analysis remains computationally challenging due to the extremely large spatial resolution of slides and the sparse distribution

local-aiarxiv-cs-cv

Interpretable Cross-Lingual Alignment in Small Language Models: Probing Cultural and Pragmatic Reasoning in Japanese-English Bilingual LLMs

arXiv:2608.14896v1 Announce Type: new Abstract: Large language models work well on English and behave in poorly understood ways on languages typologically far from it. Japanese is a clean example, whe

Invariant Pretraining for Robust Code Representations

arXiv:2608.15412v1 Announce Type: cross Abstract: Encoder-based code representation models remain widely deployed for discriminative tasks such as clone detection and code classification, where their

IP Protection in the Era of Visual Generative AI: A Survey

arXiv:2608.14730v1 Announce Type: new Abstract: The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction

researcharxiv-cs-cv

Is Grokking a Loss of Normal Hyperbolicity of the Interpolation Manifold?

arXiv:2608.14803v1 Announce Type: new Abstract: A recent line of work recasts the post-memorization phase of grokking as constrained optimization: once a network interpolates the training set, weight

Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems

arXiv:2608.14744v1 Announce Type: new Abstract: Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation.