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Inside Real Estate Launches Streams AI Mobile App to Boost Agent Productivity and Response
Ai Startups

Inside Real Estate Launches Streams AI Mobile App to Boost Agent Productivity and Response

Inside Real Estate launched Streams, an AI-powered mobile app that delivers real-time lead insights, follow-ups and productivity tools to...

AI Tools & Products · 5 min ·
[2603.05659] When Rubrics Fail: Error Enumeration as Reward in Reference-Free RL Post-Training for Virtual Try-On
Machine Learning

[2603.05659] When Rubrics Fail: Error Enumeration as Reward in Reference-Free RL Post-Training for Virtual Try-On

Abstract page for arXiv paper 2603.05659: When Rubrics Fail: Error Enumeration as Reward in Reference-Free RL Post-Training for Virtual T...

arXiv - AI · 4 min ·
[2512.16081] Evaluation of Generative Models for Emotional 3D Animation Generation in VR
Machine Learning

[2512.16081] Evaluation of Generative Models for Emotional 3D Animation Generation in VR

Abstract page for arXiv paper 2512.16081: Evaluation of Generative Models for Emotional 3D Animation Generation in VR

arXiv - AI · 4 min ·

All Content

[2507.20174] LRR-Bench: Left, Right or Rotate? Vision-Language models Still Struggle With Spatial Understanding Tasks
Llms

[2507.20174] LRR-Bench: Left, Right or Rotate? Vision-Language models Still Struggle With Spatial Understanding Tasks

The paper introduces LRR-Bench, a benchmark for evaluating Vision-Language Models (VLMs) on spatial understanding tasks, revealing signif...

arXiv - AI · 4 min ·
[2412.17596] Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context
Llms

[2412.17596] Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context

This article evaluates the divergent thinking capabilities of Large Language Models (LLMs) for scientific idea generation using minimal c...

arXiv - AI · 4 min ·
[2511.04934] Leak@$k$: Unlearning Does Not Make LLMs Forget Under Probabilistic Decoding
Llms

[2511.04934] Leak@$k$: Unlearning Does Not Make LLMs Forget Under Probabilistic Decoding

The paper discusses the limitations of current unlearning methods in large language models (LLMs), revealing that they fail to effectivel...

arXiv - Machine Learning · 4 min ·
[2509.24228] Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
Machine Learning

[2509.24228] Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms

This paper presents a benchmark for evaluating positive-unlabeled (PU) learning algorithms, addressing inconsistencies in experimental se...

arXiv - Machine Learning · 4 min ·
[2509.22295] Aurora: Towards Universal Generative Multimodal Time Series Forecasting
Llms

[2509.22295] Aurora: Towards Universal Generative Multimodal Time Series Forecasting

Aurora introduces a Multimodal Time Series Foundation Model that enhances cross-domain generalization in time series forecasting by integ...

arXiv - Machine Learning · 4 min ·
[2512.06393] Conflict-Aware Fusion: Resolving Logic Inertia in Large Language Models via Structured Cognitive Priors
Llms

[2512.06393] Conflict-Aware Fusion: Resolving Logic Inertia in Large Language Models via Structured Cognitive Priors

This article introduces Conflict-Aware Fusion, a framework designed to address Logic Inertia in large language models (LLMs) by integrati...

arXiv - Machine Learning · 4 min ·
[2510.05761] Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis
Data Science

[2510.05761] Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis

This article presents a novel approach to predicting the virality of memes on Reddit using a multimodal dataset and advanced machine lear...

arXiv - AI · 4 min ·
[2506.22740] Explanations are a Means to an End: Decision Theoretic Explanation Evaluation
Machine Learning

[2506.22740] Explanations are a Means to an End: Decision Theoretic Explanation Evaluation

The paper presents a decision-theoretic framework for evaluating explanations in AI, emphasizing their role as information signals that i...

arXiv - AI · 3 min ·
[2504.12764] GraphOmni: A Comprehensive and Extensible Benchmark Framework for Large Language Models on Graph-theoretic Tasks
Llms

[2504.12764] GraphOmni: A Comprehensive and Extensible Benchmark Framework for Large Language Models on Graph-theoretic Tasks

GraphOmni introduces a benchmark framework for evaluating large language models on graph-theoretic tasks, highlighting performance variab...

arXiv - Machine Learning · 4 min ·
[2501.00773] Revisiting Graph Neural Networks for Graph-level Tasks: Taxonomy, Empirical Study, and Future Directions
Machine Learning

[2501.00773] Revisiting Graph Neural Networks for Graph-level Tasks: Taxonomy, Empirical Study, and Future Directions

This article presents a comprehensive study on Graph Neural Networks (GNNs) for graph-level tasks, categorizing them into five types and ...

arXiv - AI · 4 min ·
[2411.01685] Reducing Biases in Record Matching Through Scores Calibration
Machine Learning

[2411.01685] Reducing Biases in Record Matching Through Scores Calibration

This paper explores methods to reduce biases in record matching through score calibration, proposing two model-agnostic post-processing t...

arXiv - Machine Learning · 4 min ·
[2602.19948] Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming
Llms

[2602.19948] Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming

This article presents a framework for assessing the risks associated with using large language models (LLMs) in mental health support, hi...

arXiv - AI · 4 min ·
[2602.19984] Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model
Machine Learning

[2602.19984] Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model

This article presents a Normal Behavior Model (NBM) for forecasting monitoring data from the ASTRI-Horn telescope, demonstrating effectiv...

arXiv - Machine Learning · 4 min ·
[2602.19843] MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systems
Llms

[2602.19843] MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systems

The paper presents MAS-FIRE, a framework for evaluating the reliability of LLM-based Multi-Agent Systems through fault injection, address...

arXiv - AI · 4 min ·
[2602.19339] SplitLight: An Exploratory Toolkit for Recommender Systems Datasets and Splits
Machine Learning

[2602.19339] SplitLight: An Exploratory Toolkit for Recommender Systems Datasets and Splits

SplitLight is an open-source toolkit designed to enhance the evaluation of recommender systems by providing measurable and comparable dat...

arXiv - Machine Learning · 3 min ·
[2602.19329] Dynamic Elasticity Between Forest Loss and Carbon Emissions: A Subnational Panel Analysis of the United States
Data Science

[2602.19329] Dynamic Elasticity Between Forest Loss and Carbon Emissions: A Subnational Panel Analysis of the United States

This article analyzes the dynamic relationship between forest loss and carbon emissions in the U.S. using a comprehensive dataset from 20...

arXiv - Machine Learning · 4 min ·
[2602.19320] Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations
Llms

[2602.19320] Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

This article presents a comprehensive analysis of agentic memory systems in large language models, highlighting their architectural frame...

arXiv - AI · 3 min ·
[2602.18813] Habilis-$β$: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model
Machine Learning

[2602.18813] Habilis-$β$: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model

Habilis-$β$ is a new on-device vision-language-action model that excels in fast-motion tasks, demonstrating superior performance in real-...

arXiv - Machine Learning · 4 min ·
[2602.18525] Do Generative Metrics Predict YOLO Performance? An Evaluation Across Models, Augmentation Ratios, and Dataset Complexity
Machine Learning

[2602.18525] Do Generative Metrics Predict YOLO Performance? An Evaluation Across Models, Augmentation Ratios, and Dataset Complexity

This paper evaluates the effectiveness of generative metrics in predicting the performance of YOLO object detection models across various...

arXiv - Machine Learning · 4 min ·
[2602.18922] Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning
Llms

[2602.18922] Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

The paper discusses the limitations of current agent caching methods in AI, proposing a new framework, W5H2, that improves efficiency and...

arXiv - Machine Learning · 3 min ·
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