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2,343 notes. Notes 1,401 to 1,450, page 29 of 47.
Technical Report on the CVPR 2026@AdvML Workshop Challenge
This text is about the CVPR 2026@AdvML Workshop Challenge which focuses on testing the robustness of vision-language agents (VLAs) used in autonomous driving. The challenge involved creating adversarial images and…
Technical Report on the CVPR 2026@AdvML Workshop Challenge
This text is about the CVPR 2026@AdvML Workshop Challenge, focusing on adversarial attacks against Vision-Language Agents (VLAs) used in autonomous driving. The challenge involves creating adversarial images and…
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
The text discusses a new proactive assistance system named Vinci2, which provides assistance in continuous egocentric videos by considering the user's history, current activity, and whether help would be welcome. The…
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
Researchers have developed a proactive assistance system called Vinci2 for continuous egocentric videos. The system, which advances on the existing reactive response system Vinci, aims to offer more context-dependent…
VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion
The paper introduces VoxENES 2026, a new benchmark for testing speech synthesis systems against modern text-to-speech (TTS) and voice conversion (VC) methods. The benchmark includes 53,628 audio samples in English and…
VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion
The paper introduces VoxENES 2026, a new benchmark for evaluating the performance of speech spoofing detectors against modern text-to-speech (TTS) and voice conversion (VC) systems. The benchmark contains 53,628 audio…
A Practical Investigation of Training-free Relaxed Speculative Decoding
The paper investigates training-free methods to speed up the sampling process of autoregressive Language Models (LLMs) by relaxing some strict guarantees in standard speculative decoding. The authors provide insights…
A Practical Investigation of Training-free Relaxed Speculative Decoding
The study investigates training-free methods of relaxed speculative decoding, which can speed up sampling from an autoregressive Language Model (LLM) by using a faster auxiliary model to draft tokens that are then…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
The paper proposes a Q-learning based adaptive retraining approach for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL). The goal is to balance forecasting accuracy and retraining cost, which can…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
Researchers propose a Q-learning-based adaptive retraining approach using Reinforcement Learning (RL) to handle drift in Open Radio Access Networks (O-RAN). This approach aims to balance forecasting accuracy and…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
The paper proposes an adaptive retraining method for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL). The goal is to balance forecasting accuracy and retraining cost in the presence of dynamic…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
Researchers propose a Q-learning based adaptive retraining approach for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL) to address the issue of drift that degrades the performance of AI/ML models…
AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism
Researchers have developed a method to create and select facial expressions that enhance differences in emotion perception between autistic and neurotypical adults. This is done by training artificial neural networks…
ArtMine: Discovering and Formalizing Artistic Processes
The paper introduces ArtMine, a framework for discovering and formalizing artistic processes from various historical sources. It uses an abductive agent to infer evidence-based production steps which are then optimized…
ArtMine: Discovering and Formalizing Artistic Processes
ArtMine is a framework designed to discover and formalize artistic processes from various historical sources. It uses an abductive agent to infer evidence-based production steps, which are then optimized to generate…
Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models
Researchers have developed Blind-Spots-Bench, a new benchmark to evaluate the limitations of AI systems by presenting tasks that are easy for humans but challenging for current AI models. The study reveals significant…
Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models
Researchers introduce 'blind-spots-bench', a new benchmark to evaluate and expose persistent blind spots in modern AI models, especially those from closed-source providers, which can outperform open-weight models by up…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
Researchers have proposed a new type of multimodal agent called the Cognitive-structured Multimodal Agent. This agent is designed to improve long-horizon multimodal dialogue by externalizing visual information into an…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
The paper proposes a new multimodal agent called the Cognitive-structured Multimodal Agent. This agent is designed to improve long-horizon multimodal dialogue by externalizing visual information into an Episodic Visual…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
Researchers propose a new type of multimodal agent called the Cognitive-structured Multimodal Agent (CMA) that can understand, generate, and edit visual and textual information. The agent externalizes visual…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
Researchers propose a new model called the Cognitive-structured Multimodal Agent. This agent is designed to understand and generate multimodal data (vision and language) in long-horizon dialogues more efficiently. It…
CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
Researchers have developed a machine learning model named CommuniWave. This model aims to help urban managers and designers by efficiently detecting and quantifying temporary informal behaviors in urban communities…
CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
Researchers have developed a machine learning model called CommuniWave to help urban managers measure the degree of informal behavior in urban communities. This can help make better decisions for community planning and…
Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
The authors explore the potential of using the internal k-nearest-neighbor (kNN) graph constructed by UMAP, a popular tool for exploring high-dimensional data. They demonstrate that standard graph algorithms like…
Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
The authors of the paper 'Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph', published in 2026, discuss a new approach to utilizing the internal k-nearest-neighbor (kNN) graph constructed…
DocMaster: A Hierarchical Structure-Aware System for Document Analysis
DocMaster is a system that allows for the analysis of complex documents while preserving their hierarchical structures such as sections, tables, figures, and equations. It offers an interactive web interface where…
DocMaster: A Hierarchical Structure-Aware System for Document Analysis
DocMaster is a system designed to analyze complex documents while preserving their hierarchical structures such as sections, tables, figures, and equations. It allows users to upload document collections, construct…
DrugGen 2: A disease-aware language model for enhancing drug discovery
DrugGen 2 is a new model that generates small molecules for drug design by considering both disease ontology and target protein sequences. It was developed by fine-tuning a pre-trained GPT-2 model and outperformed…
DrugGen 2: A disease-aware language model for enhancing drug discovery
A new generative model called DrugGen-2 has been developed to enhance drug discovery. It takes into account both disease ontology and target protein sequences, improving upon existing models in generating unique…
Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
This research paper explores mechanisms for maintaining market stability in societies of self-interested agents. The authors conduct a simulation study using DeepSeek-V3 agents and compare eight conditions with…
Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
The researchers investigated mechanisms that can maintain market stability in a society of self-interested agents who tend to defect in repeated social dilemmas. They conducted a multi-agent marketplace simulation with…
Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
The study investigates mechanisms to maintain market stability in societies of self-interested agents who tend to defect in repeated social dilemmas. The research uses a multi-agent marketplace simulation with 18 LLM…
Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
The research presented investigates mechanisms that allow societies of self-interested agents to maintain market stability, focusing on a multi-agent marketplace simulation with 18 AI agents. The study identifies…
From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure
The paper proposes an agent pipeline using Machine Comprehension Powered (MCP) technology to convert natural language system descriptions into audit-ready artifacts in OSCAL format for continuous automated compliance…
From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure
The paper proposes an agent pipeline that converts natural language system descriptions into OSCAL format for continuous automated compliance management in critical infrastructure. The pipeline uses MCP-grounded…
Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
Researchers present G-Frame, an adaptive multi-agent framework that reduces hallucinations in lightweight large language models used in rule-based scientific domains. The framework is based on Bayesian and team game…
Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
Researchers present G-Frame, a new framework that uses artificial intelligence (AI) to create a high-quality dataset and train a model called OmniChem. This model performs as well as another AI model (GPT 4o mini) on…
Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
Researchers have developed a new framework called G-Frame that helps lightweight Large Language Models perform better in rule-based scientific domains by reducing hallucinations. They created a new model called…
GitLake: Git-for-data for the agentic lakehouse
The paper introduces GitLake, a system designed for managing data in an agent-first lakehouse using Git principles. It allows agents to work on isolated branches while humans review changes, and ensures all outputs are…
GitLake: Git-for-data for the agentic lakehouse
The authors present GitLake, a Git-based data system for an agent-first lakehouse. It allows agents to work on isolated branches while humans review and publish changes. Pipelines run on temporary branches and are…
Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
The text introduces IdeaGene-Bench (IG-Bench), a benchmark for evaluating AI systems' ability to follow the inheritance structure of scientific ideas, similar to how biological genomes evolve. IG-Bench uses the…
Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
Researchers have developed IdeaGene-Bench (IG-Bench), a benchmark for assessing AI systems' ability to follow the inheritance structure of scientific ideas, similar to how biological genomes pass traits from generation…
Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
A new benchmark called IdeaGene-Bench (IG-Bench) has been proposed for evaluating AI systems' ability to follow the inheritance structure of scientific ideas, similar to how biological genomes pass traits from one…
Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
The text presents IdeaGene-Bench (IG-Bench), a new benchmark for evaluating AI systems' ability to follow the structure of scientific idea inheritance. IG-Bench is based on the IdeaGene framework, which represents each…
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
The researchers propose a method for machine learning that focuses on aligning autonomous agents' behavior with human intent in diverse operational contexts. They argue that existing methods for inverse reinforcement…
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
The research paper proposes a method for teaching machines to learn robust reward functions that can work across various operational contexts. The study focuses on scenarios with diverse feedback modalities and…
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
The researchers introduce a machine teaching algorithm that operates across multiple MDPs (multi-agent decision making problems) to learn robust reward functions from heterogeneous feedback modalities in diverse…
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
The paper proposes a method for machine learning that aims to align autonomous agents' behavior with human intent in diverse operational contexts by using a hierarchical machine teaching algorithm for reward learning…
OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
The paper introduces OmniFood-Bench, a new benchmark for evaluating Large Vision-Language Models (VLMs) in the context of personalized healthcare and dietary management. The benchmark is designed to assess the ability…
OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
The paper introduces OmniFood-Bench, a comprehensive benchmark for evaluating the performance of Vision-Language Models (VLMs) in personalized healthcare and dietary management. The benchmark focuses on three…
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