All entries

Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks

arXiv:2605.14239v1 Announce Type: new Abstract: Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling betw

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Improving Multi-turn Dialogue Consistency with Self-Recall Thinking

DGX agent

arXiv:2605.15102v1 Announce Type: cross Abstract: Large language model (LLM) based multi-turn dialogue systems often struggle to track dependencies across non-adjacent turns, undermining both consiste

researcharxiv-cs-ai

15 May 2026

In-Context Learning for Data-Driven Censored Inventory Control

arXiv:2605.14840v1 Announce Type: new Abstract: We study inventory control with decision-dependent censoring, focusing on the censored or repeated newsvendor (R-NV), where each order quantity determin

In-IDE Toolkit for Developers of AI-Based Features

arXiv:2605.14612v1 Announce Type: cross Abstract: AI-enabled features built on LLMs and agentic workflows are difficult to test, debug, and reproduce, especially for product-focused software engineers

agentsarxiv-cs-ai

Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis

arXiv:2605.13871v1 Announce Type: cross Abstract: This paper presents a novel population-based metaheuristic, Indian Wedding System Optimization (IWSO), inspired by the socio-cultural dynamics of trad

InfoSFT: Learn More and Forget Less with Information-Aware Token Weighting

arXiv:2605.14967v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) provides the standard approach for teaching LLMs new behaviors from offline expert demonstrations. However, standard SFT un

safetyarxiv-cs-lg

InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation

arXiv:2605.14333v1 Announce Type: new Abstract: Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregr

Intelligence Impact Quotient (IIQ): A Framework for Measuring Organizational AI Impact

arXiv:2605.14455v1 Announce Type: new Abstract: The Intelligence Impact Quotient (IIQ) is a composite metric intended to quantify the depth to which AI systems are integrated into organizational work

agentsarxiv-cs-ai

IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation

arXiv:2605.14712v1 Announce Type: cross Abstract: Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators

Interestingness as an Inductive Heuristic for Future Compression Progress

arXiv:2605.14831v1 Announce Type: new Abstract: One of the bottlenecks on the way towards recursively self-improving systems is the challenge of interestingness: the ability to prospectively identify

researcharxiv-cs-ai

Invisible Orchestrators Suppress Protective Behavior and Dissociate Power-Holders: Safety Risks in Multi-Agent LLM Systems

arXiv:2605.13851v1 Announce Type: new Abstract: Multi-agent orchestration -- in which a hidden coordinator manages specialized worker agents -- is becoming the default architecture for enterprise AI d

IPR-1: Interactive Physical Reasoner

arXiv:2511.15407v3 Announce Type: replace Abstract: Humans learn by observing, interacting with environments, and internalizing physics and causality. Here, we aim to ask whether an agent can similarl

Is Grep All You Need? How Agent Harnesses Reshape Agentic Search

model-releasesarxiv-cs-cl

arXiv:2605.15184v1 Announce Type: new Abstract: Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools,

Iskra: A System for Inverse Geometry Processing

arXiv:2602.12105v2 Announce Type: replace-cross Abstract: We propose a system for differentiating through solutions to geometry processing problems. Our system differentiates a broad class of geometri

IsoNet: Spatially-aware audio-visual target speech extraction in complex acoustic environments

arXiv:2605.14736v1 Announce Type: cross Abstract: Target speech extraction remains difficult for compact devices because monaural neural models lack spatial evidence and classical beamformers lose res

researcharxiv-cs-lg

It Takes Two: Your GRPO Is Secretly DPO

arXiv:2510.00977v3 Announce Type: replace-cross Abstract: GRPO has emerged as a prominent reinforcement learning algorithm for post-training LLMs. Unlike critic-based methods, GRPO computes advantages

researcharxiv-cs-cl

JointAVBench: A Benchmark for Joint Audio-Visual Reasoning Evaluation

model-releasesarxiv-cs-cv

arXiv:2512.12772v2 Announce Type: replace-cross Abstract: Understanding videos inherently requires reasoning over both visual and auditory information. To properly evaluate Omni-Large Language Models

K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles

arXiv:2605.14828v1 Announce Type: cross Abstract: Existing clustering methods for functional data often prioritize partitioning accuracy over interpretability, making it challenging to extract meaning

Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models

arXiv:2509.25826v3 Announce Type: replace Abstract: Inherent temporal heterogeneity, such as varying sampling densities and periodic structures, has posed substantial challenges in zero-shot generaliz

Keyed Nonlinear Transform: Lightweight Privacy-Enhancing Feature Sharing for Medical Image Analysis

arXiv:2605.14123v1 Announce Type: cross Abstract: Feature sharing via split inference offers a lightweight alternative to federated learning for resource-constrained hospitals, but transmitted feature

KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning

arXiv:2605.14907v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models aim to generalize across graphs with unseen entities and relations by learning transferable relational structure.

local-aiarxiv-cs-ai

Know When To Fold 'Em: Token-Efficient LLM Synthetic Data Generation via Multi-Stage In-Flight Rejection

arXiv:2605.14062v1 Announce Type: new Abstract: While synthetic data generation with large language models (LLMs) is widely used in post-training pipelines, existing approaches typically generate full

safetyarxiv-cs-ai

Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation

arXiv:2605.14404v1 Announce Type: new Abstract: While LLMs are increasingly used in commercial services, they pose privacy risks such as leakage of sensitive personally identifiable information (PII).

researcharxiv-cs-cl

Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces

arXiv:2605.14786v1 Announce Type: cross Abstract: As LLM-based agents increasingly browse the web on users' behalf, a natural question arises: can websites passively identify which underlying model po

agentsarxiv-cs-ai

Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws

arXiv:2511.07686v2 Announce Type: replace-cross Abstract: Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics direct

Krause Synchronization Transformers

arXiv:2602.11534v3 Announce Type: replace-cross Abstract: Self-attention in Transformers relies on globally normalized softmax weights, causing all tokens to compete for influence at every layer. When

KVPO: ODE-Native GRPO for Autoregressive Video Alignment via KV Semantic Exploration

arXiv:2605.14278v1 Announce Type: new Abstract: Aligning streaming autoregressive (AR) video generators with human preferences is challenging. Existing reinforcement learning methods predominantly rel

safetyarxiv-cs-cv

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts

arXiv:2601.21349v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small subset of experts, where the router plays a central

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows

arXiv:2605.14527v1 Announce Type: new Abstract: Developing machine learning interatomic potentials (MLIPs) for complex materials systems remains challenging because it requires expertise in atomistic

agentsarxiv-cs-lg

Language Generation as Optimal Control: Closed-Loop Diffusion in Latent Control Space

arXiv:2605.14531v1 Announce Type: new Abstract: This work reformulates language generation as a stochastic optimal control problem, providing a unified theoretical perspective to analyze autoregressiv

safetyarxiv-cs-cl

Tutorials

Language-Induced Priors for Domain Adaptation

arXiv:2605.14301v1 Announce Type: new Abstract: Domain adaptation faces a fundamental paradox in the cold-start regime. When target data is scarce, statistical methods fail to distinguish relevant sou

tutorialsarxiv-cs-lg

Large Dimensional Kernel Ridge Regression: Extending to Product Kernels

arXiv:2605.14524v1 Announce Type: cross Abstract: Recent studies have reported extit{saturation effects} and extit{multiple descent behavior} in large dimensional kernel ridge regression (KRR). Howeve

researcharxiv-cs-lg

Tutorials

Large Language Models for Web Accessibility: A Systematic Literature Review

arXiv:2605.13873v1 Announce Type: cross Abstract: Web accessibility aims to ensure that web content and services are usable by people with diverse abilities. In recent years, Large Language Models (LL

tutorialsarxiv-cs-ai

Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents

arXiv:2605.14241v1 Announce Type: new Abstract: Tool-augmented LLM agents increasingly access the same tool type through multiple functionally equivalent providers, such as web-search APIs, retrievers

LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection

arXiv:2605.15054v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently emerged as a promising paradigm for video anomaly detection (VAD) due to their strong visual reasoning abili

safetyarxiv-cs-cv

Applications

LEAP: Local ECT-Based Learnable Positional Encodings for Graphs

arXiv:2510.00757v3 Announce Type: replace Abstract: Graph neural networks (GNNs) largely rely on the message-passing paradigm, where nodes iteratively aggregate information from their neighbors. Yet,

applicationsarxiv-cs-lg

Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation

model-releasesarxiv-cs-ro

arXiv:2605.14805v1 Announce Type: new Abstract: Accurate dynamics models are critical for aerial manipulators operating under complex tasks such as payload transport. However, modeling these systems r

Learning Developmental Scaffoldings to Guide Self-Organisation

arXiv:2605.14998v1 Announce Type: new Abstract: From subcellular structures to entire organisms, many natural systems generate complex organisation through self-organisation: local interactions that c

local-aiarxiv-cs-ai

Learning Direct Control Policies with Flow Matching for Autonomous Driving

arXiv:2605.14832v1 Announce Type: cross Abstract: We present a flow-matching planner for autonomous driving that directly outputs actionable control trajectories defined by acceleration and curvature

agentsarxiv-cs-cv

Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control

arXiv:2602.21302v2 Announce Type: replace Abstract: We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of ropes. We demonstrate this method on a non-planar rope manip

researcharxiv-cs-ro

Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards

arXiv:2605.14539v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective paradigm for improving the reasoning capabilities of large language mo

safetyarxiv-cs-cl

Learning from Language Feedback via Variational Policy Distillation

arXiv:2605.15113v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) suffers from sparse outcome signals, creating severe exploration bottlenecks on complex reasoning

safetyarxiv-cs-lg

Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation

arXiv:2512.02920v3 Announce Type: replace-cross Abstract: We consider analyzing traffic accident patterns using both road network data and satellite images aligned to road graph nodes. Previous work f

Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty

arXiv:2605.14494v1 Announce Type: new Abstract: Two-Stage Robust Optimization (2RO) with discrete uncertainty is challenging, often rendering exact solutions prohibitive. Scenario reduction alleviates

researcharxiv-cs-ai

Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

arXiv:2601.21151v2 Announce Type: replace Abstract: Recent machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms-advection (lo

local-aiarxiv-cs-lg

Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis

arXiv:2605.14392v1 Announce Type: new Abstract: We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the envi

safetyarxiv-cs-ai

Learning with Semantic Priors: Stabilizing Point-Supervised Infrared Small Target Detection via Hierarchical Knowledge Distillation

arXiv:2605.14346v1 Announce Type: new Abstract: Single-frame Infrared Small Target Detection (ISTD) aims to localize weak targets under heavy background clutter, yet dense pixel-wise annotations are e

local-aiarxiv-cs-cv

Applications

Learning with Shallow Neural Networks on Cluster-Structured Features

arXiv:2605.14927v1 Announce Type: new Abstract: The success of deep learning in high-dimensional settings is often attributed to the presence of low-dimensional structure in real-world data. While sta

applicationsarxiv-cs-lg