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iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework

arXiv:2409.13007v3 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the

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Local Ai

Image-based Geo-localization for Robotics: Are Black-box Vision-Language Models there yet?

DGX agent

arXiv:2501.16947v2 Announce Type: replace Abstract: The advances in Vision-Language models (VLMs) offer exciting opportunities for robotic applications involving image geo-localization - the problem o

local-aiarxiv-cs-cv

29 Jun 2026

Improving Adversarial Robustness via Activation Amplification and Attenuation

arXiv:2606.27784v1 Announce Type: cross Abstract: The existence of adversarial attacks is often attributed to the presence of non-robust features in neural networks. While prior defenses reduce their

tutorialsarxiv-cs-ai

Instant Expressive Gaussian Head Avatars at Over 100 FPS

arXiv:2512.16893v2 Announce Type: replace Abstract: Portrait animation has witnessed tremendous quality improvements thanks to recent advances in video diffusion models. However, these 2D methods ofte

applicationsarxiv-cs-cv

Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning

arXiv:2606.27483v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-hor

IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

arXiv:2604.00757v2 Announce Type: replace-cross Abstract: Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidl

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications

arXiv:2606.28070v1 Announce Type: new Abstract: JD.com, one of the world's largest e-commerce platforms, serves over 700 million active users and millions of merchants, with a catalog of tens of billi

applicationsarxiv-cs-ai

Joint Transcription and Decryption of Images of Encrypted Handwritten Documents: A Comparison with the Traditional Pipeline

arXiv:2606.27700v1 Announce Type: cross Abstract: Historical encrypted manuscripts present a challenging problem at the intersection of cryptology, linguistics, paleography, and computer vision. Curre

applicationsarxiv-cs-cl

Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs

arXiv:2601.21233v2 Announce Type: replace Abstract: Autonomous code agents built on large language models are reshaping software and AI development through tool use, long-horizon reasoning, and self-d

KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systems

arXiv:2606.27742v1 Announce Type: cross Abstract: Enterprise Knowledge Graphs (KGs) are increasingly used for internal search, analytics, and question answering, but building natural-language interfac

applicationsarxiv-cs-ai

KISS-IMU: Self-supervised Inertial Odometry with Motion-balanced Learning and Uncertainty-aware Inference

arXiv:2603.06205v2 Announce Type: replace Abstract: Inertial measurement units (IMUs), which provide high-frequency linear acceleration and angular velocity measurements, serve as fundamental sensing

applicationsarxiv-cs-ro

Ko-WideSearch: A Korean Breadth-Search Benchmark for Exhaustive Set Enumeration by Web Agents

model-releasesarxiv-cs-cl

arXiv:2606.27595v1 Announce Type: new Abstract: Web-agent benchmarks overwhelmingly measure depth -- pinning one obscure answer behind a chain of constraints -- while breadth, exhaustively enumerating

Large Language Model Teaches Visual Students: Cross-Modality Transfer of Fine-Grained Conceptual Knowledge

arXiv:2606.27527v1 Announce Type: cross Abstract: Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pretraining, yet their potential to supervise models

Latent Visual Diffusion Reasoning with Monte Carlo Tree Search

arXiv:2606.27988v1 Announce Type: new Abstract: Analyzing fine-grained skill activities (e.g., sports, surgery) requires not only recognizing visual patterns but also performing step-by-step visual re

tutorialsarxiv-cs-cv

Layerwise Progressive Freezing: A Training Scaffold for Depth-Scalable Binary Networks

arXiv:2606.27759v1 Announce Type: new Abstract: Training binary neural networks (BNNs) from scratch is dominated by the straight-through estimator (STE), whose forward/backward mismatch produces sever

researcharxiv-cs-lg

Agents

Learn Structure, Adapt on the Fly: Multi-Scale Residual Learning and Online Adaptation for Aerial Manipulators

arXiv:2603.11638v2 Announce Type: replace Abstract: Autonomous Aerial Manipulators (AAMs) are inherently coupled, nonlinear systems that exhibit nonstationary and multiscale residual dynamics, particu

agentsarxiv-cs-ro

Agents

Learning 1-Bit LiDAR-based Localization with Auxiliary Objective

arXiv:2606.27729v1 Announce Type: new Abstract: 6-DoF LiDAR-based localization is a fundamental capability for autonomous systems operating in large-scale outdoor environments. Many deep-learning-base

agentsarxiv-cs-cv

Learning Complementary Action Modeling from Automotive Maintenance Instructions

arXiv:2606.27808v1 Announce Type: new Abstract: A minute lexical variation can reverse the procedural meaning of an instruction even when the rest of the sentence remains unchanged. In automotive main

researcharxiv-cs-cl

Learning from Annotation Uncertainty: Entropy-Aware Curriculum for Speech Emotion Recognition

arXiv:2606.27536v1 Announce Type: cross Abstract: Speech emotion recognition (SER) often relies on hard consensus labels that collapse annotator disagreement. We study distribution-based supervision f

safetyarxiv-cs-lg

Learning in Markovian bandits with non-observable states and constrained decision epochs

model-releasesarxiv-cs-lg

arXiv:2606.27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with non-observable states and possibly constrained decision epochs. The focu

Learning Peer Influence Probabilities with Linear Contextual Bandits

model-releasesarxiv-cs-lg

arXiv:2510.19119v2 Announce Type: replace Abstract: In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action. Whethe

Learning Stable In-Grasp Manipulation in a Non-Dropping Action Space

arXiv:2606.28196v1 Announce Type: new Abstract: Traditionally, dexterous manipulation controllers are designed using analytic models constrained by strong assumptions about the hand and the objects be

researcharxiv-cs-ro

Learning Stochastic Bridges for Video Object Removal via Video-to-Video Translation

arXiv:2601.12066v4 Announce Type: replace Abstract: Existing video object removal methods predominantly rely on diffusion models following a noise-to-data paradigm, where generation starts from uninfo

researcharxiv-cs-cv

Learning to Evict from Key-Value Cache

model-releasesarxiv-cs-cl

arXiv:2602.10238v2 Announce Type: replace Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Ke

Learning to Reason with Curriculum II: Compositional Generalization

arXiv:2606.27721v1 Announce Type: new Abstract: Compositional generalization, the ability to solve complex problems by combining solutions to simpler sub-problems, is a fundamental capability of both

researcharxiv-cs-lg

Learning to Refine Hidden States for Reliable LLM Reasoning

arXiv:2606.17524v2 Announce Type: replace Abstract: Large language models show strong reasoning ability, but their internal reasoning process can remain unstable in complex multi-step settings, where

safetyarxiv-cs-lg

Learning to Throw: Agile and Accurate Cable-Suspended Payload Delivery with a Quadrotor

arXiv:2606.27603v1 Announce Type: new Abstract: Quadrotors offer the agility needed to rapidly transport suspended payloads during time-critical applications, including search-and-rescue and medical d

safetyarxiv-cs-ro

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation

arXiv:2606.28268v1 Announce Type: cross Abstract: Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. However, existing TTA approaches for

tutorialsarxiv-cs-ai

Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation

arXiv:2511.14271v2 Announce Type: replace Abstract: Text-to-3D generation has advanced rapidly, yet state-of-the-art models, encompassing both optimization-based and feed-forward architectures, still

safetyarxiv-cs-cv

LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries

arXiv:2510.25731v2 Announce Type: replace-cross Abstract: Initial-boundary value problems (IBVPs) provide the essential framework for modelling a wide range of phenomena in physics and engineering. We

Lifted Causal Inference

arXiv:2606.28024v1 Announce Type: new Abstract: Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby spee

LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior

arXiv:2606.28182v1 Announce Type: cross Abstract: Embodied agents operating in decentralized and partially observable environments have attracted growing attention in recent years. However, existing l

LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation

model-releasesarxiv-cs-ro

arXiv:2606.27871v1 Announce Type: new Abstract: Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context, serving

Local Ai

Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery

arXiv:2606.27900v1 Announce Type: new Abstract: Human motion describes the three-dimensional full-body movement of a person. Anticipating such motion holds significant relevance across a wide range of

local-aiarxiv-cs-cv

Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering

arXiv:2606.16494v2 Announce Type: replace-cross Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by con

Low-Agreeableness Persona Conditioning for Safe LLM Fine-Tuning

arXiv:2606.27709v1 Announce Type: cross Abstract: Recent work has shown that fine-tuning large language models (LLMs) for social warmth degrades factual reliability and increases sycophancy. We invest

Agents

LXD-SLAM: LiDAR+X Dense SLAM with sum_{i=0}^{5}C_5^i Configurable Sensor Combinations

arXiv:2606.27811v1 Announce Type: new Abstract: Simultaneous Localization and Mapping (SLAM) is essential for autonomous systems, yet achieving reliable, globally consistent pose estimation and dense

agentsarxiv-cs-ro

Masked Language Flow Models

arXiv:2606.27617v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) promise fast, parallel language generation, but their reverse transition factorises across token positions -- an approxim

researcharxiv-cs-cl

MASS: Motion-Aligned Selective Scan for Refinement in Flow-Based Video Frame Interpolation

arXiv:2606.27718v1 Announce Type: new Abstract: Video frame interpolation (VFI) remains a challenging task, particularly when dealing with large, non-linear motions and complex occlusions. While flow-

tutorialsarxiv-cs-cv

Measuring the Redundancy of Decoder Layers in SpeechLLMs

arXiv:2603.05121v2 Announce Type: replace-cross Abstract: Speech Large Language Models route speech encoder representations into an LLM decoder that typically accounts for over 90% of total parameters

Mechanism-Driven Monitors for Preemptive Detection of LLM Training Instability

arXiv:2606.28116v1 Announce Type: new Abstract: Frontier large language model training consumes massive accelerator fleets and long wall-clock computation, making stability failures costly when they o

researcharxiv-cs-cl

MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis

arXiv:2602.17901v3 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) and diffusion models have respectively advanced representation learning and generative modeling for high-dimens

tutorialsarxiv-cs-cv

MemoBench: Benchmarking World Modeling in Dynamically Changing Environments

model-releasesarxiv-cs-cv

arXiv:2606.27537v1 Announce Type: new Abstract: Video generation models aspire to simulate dynamic environments, and several benchmarks now evaluate memory consistency across frames. However, most ass

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy

arXiv:2606.27652v1 Announce Type: new Abstract: We find that explicit reasoning does not necessarily translate into better multimodal emotion recognition (MER) accuracy, even though it makes predictio

MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation

arXiv:2510.10271v2 Announce Type: replace-cross Abstract: Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during

Mind the Gap: Quantifying the Domain Gap in Cross-Sensor Diffusion Super-Resolution

arXiv:2606.28039v1 Announce Type: cross Abstract: Demand for high-resolution satellite imagery has increased interest in super-resolution (SR) to bridge the spatial resolution gap between freely avail

MindFlow: Harmonizing Cognitive Semantics and Acoustic Dynamics for Facial Animation Generation in Dyadic Conversations

arXiv:2606.27779v1 Announce Type: new Abstract: Generating lifelike facial animation for dyadic conversations requires reconciling high-level cognitive intent with precise low-level motor reflexes, ye

researcharxiv-cs-cv

Mitigating LLM-based p-Hacking by Preregistering for the Next LLM

arXiv:2606.27687v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, classify, and annotate data whose outputs feed downstream hypothesis tests. However, L