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2,343 notes. Notes 2,051 to 2,100, page 42 of 47.
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
The researchers present a framework called OmniAct that aims to build persistent embodied agents for operation in unstructured environments. The framework integrates a multimodal semantic planner, an adaptive…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Researchers introduce OmniAct, a framework designed for creating persistent embodied agents in unstructured environments. The framework is built on a hierarchical asynchronous architecture that separates planning…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
The paper presents OmniAct, a framework for building persistent embodied agents that can operate in unstructured environments. The framework aims to integrate various tools from both cyber and physical domains, handle…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Researchers present OmniAct, a framework that aims to advance the development of persistent embodied agents in unstructured environments. The framework integrates a multimodal semantic planner for skill routing, an…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
Researchers analyzed over 15,000 user reviews from 59 AI healthcare chatbot apps to understand the performance of these systems in everyday use. They identified three main issues: access barriers and service…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
This study analyzes over 15,000 user reviews of AI healthcare chatbot apps to identify common issues reported by users. The research found three main breakdowns: access barriers and service unreliability, user…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
This research study examines over 15,000 user reviews of AI healthcare chatbot apps to identify common breakdowns in their performance. The study found three main issues: access barriers, service unreliability, user…
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
A study analyzing over 15,000 user reviews of AI healthcare chatbot apps found recurring issues with access barriers, service unreliability, user experience, interaction quality, billing, and customer support. Privacy…
Application of LLMs to Threat Assessment of Foreign Peacekeeping Missions
The authors propose a new method using Large Language Models (LLMs) to assist in the assessment of threats for foreign peacekeeping missions, using the EU Monitoring Mission in Georgia as a case study.
Application of LLMs to Threat Assessment of Foreign Peacekeeping Missions
The authors propose using Large Language Models (LLMs) to help assess threats during foreign peacekeeping missions, specifically referencing their work with the EU Monitoring Mission in Georgia. They combine an…
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Researchers have developed a new system called BINEVAL for evaluating large language models (LLMs). This system breaks down the evaluation criteria into binary questions, making it easier to understand the results and…
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Researchers have developed a new method called BINEVAL to evaluate the output of large language models (LLMs). Instead of relying on human judgment or lexical metrics, which can be expensive and unreliable, BINEVAL…
Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
Researchers investigated how different contexts or framings can affect the responses of large language models (LLMs) used in mental health support tools. They found that framing does systematically alter interpretive…
Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
This research investigates how different contexts (framings) can affect the responses of large language models used in mental health support tools. The study finds that framing does systematically alter interpretive…
Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
The study investigates how different contextual framings can affect the responses of large language models (LLMs) used in mental health support tools. The researchers found that framing systematically alters…
Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
Researchers investigated how different contexts or framings can affect the responses of large language models (LLMs) used in mental health support tools and psychologically sensitive conversations. They found that…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
In this research, the authors propose a new method called VLM-PBRS for automating potential-based reward shaping using vision language model (VLM) feedback. The goal is to help reinforcement learning agents learn from…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers introduce a new framework called VLM-PBRS for automating potential-based reward shaping in reinforcement learning agents. The framework learns the potential function directly from vision language model…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers introduce a new method called VLM-PBRS for automating potential-based reward shaping using vision language model (VLM) feedback. The aim is to improve reinforcement learning agents' ability to learn from…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers have developed a new method called VLM-PBRS for automating potential-based reward shaping in reinforcement learning agents. This method uses vision language models (VLMs) to learn the potential function…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
In this research, the authors present a new framework called VLM-guided PBRS, which learns potential functions for reward shaping directly from vision language model (VLM) feedback. The goal is to address the…
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Researchers have developed a new method for reinforcement learning agents using vision language models (VLMs). The approach called VLM-guided PBRS learns a potential function from VLM feedback to address the challenge…
Autoregressive Boltzmann Generators
A new method called Autoregressive Boltzmann Generators (ArBG) is proposed to efficiently sample molecular systems at thermodynamic equilibrium. This method overcomes limitations of previous methods by using an…
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
The authors present a framework for analyzing dialogue during collaborative problem-solving involving humans, AI, or multiple agents. The framework is designed to address limitations in current analytical approaches…
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
The authors present a framework for analyzing dialogue in collaborative problem-solving contexts, particularly focusing on human-AI and multi-agent collaboration. The framework addresses limitations in current…
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
The authors present a framework for analyzing dialogue in collaborative problem-solving contexts, especially in human-AI and multi-agent collaboration. The framework aims to understand the emerging dynamics of these…
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
Researchers present a framework for analyzing dialogues during collaborative problem-solving tasks involving human-AI and multi-agent interactions. The framework aims to optimize and evaluate these partnerships by…
CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention
The paper introduces CARVE (Content-Aware Recurrent with Value Efficiency), a recurrent model that addresses three issues in the leading delta-rule architecture (GDN-2). The primary problem is memory-blind gating…
Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities
Researchers have developed Chai, an AI-based system that discovers and validates cryptographic misuse vulnerabilities through naturally occurring signals. Instead of auditing one codebase for many flaws, Chai catalogs…
Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities
The paper introduces Chai, an AI-based system that discovers and validates cryptographic misuse vulnerabilities using naturally occurring signals. Instead of auditing one codebase for many flaws, Chai catalogs flaws at…
Confidence-Aware Tool Orchestration for Robust Video Understanding
The researchers propose a framework called Robust-TO for video understanding that addresses the 'Blind Trust Problem' in video reasoning language models. The proposed method incorporates per-frame trustworthiness into…
Confidence-Aware Tool Orchestration for Robust Video Understanding
The paper presents a new framework called Robust-TO for video understanding that addresses the issue of 'Blind Trust Problem' in current language models. These models assume every input frame is equally reliable, but…
Diagnosing Task Insensitivity in Language Agents
The research discusses a problem with large language models where they struggle to adapt to new tasks outside of their original training data (OOD generalization). The authors call this issue 'task insensitivity'. They…
Diagnosing Task Insensitivity in Language Agents
Researchers identify an issue with large language models where they are unable to adapt to new tasks effectively due to their task insensitivity. The authors propose a lightweight solution called Task-Perturbed NLL…
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
The researchers introduced a new framework called E-TTS for robotic manipulation that combines reasoning and action scaling using historical context. Unlike traditional open-loop TTS methods, E-TTS includes feedback…
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
Researchers have developed a new framework called E-TTS for robotic manipulation tasks. This framework combines reasoning and action scaling to address two challenges: the lack of study on the scaling mechanism for…
Efficient foundation decoders for fault-tolerant quantum computing
The paper introduces Neural Transfer Unification (NTU), a framework for creating efficient foundation decoders that can handle various code distances in fault-tolerant quantum computing. The NTU-Transformer, an…
Einstein World Models
The text discusses a proposed system called Einstein World Models (EWMs) which aim to enhance the reasoning abilities of Language Model (LLM) by incorporating visual-temporal rollouts within the reasoning process…
Einstein World Models
The paper proposes Einstein World Models (EWMs), a system for large language models (LLMs) to reason about counterfactual events using visual-temporal rollouts, enhancing their capability beyond language alone. The…
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
The research introduces a method called Planning Experience Exploration and Utilization (PEEU) to improve the task planning capabilities of smaller open-source Multi-Language Model Learning Machines (MLLMs). These…
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
Researchers introduce a new method called PEEU to improve the task planning capabilities of small, open-source multimodal web agents that assist humans in operating repetitive graphical user interface (GUI) tasks…
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
Researchers have developed a method called Planning Experience Exploration and Utilization (PEEU) to improve the task planning capabilities of small open-source multi-modal web agents. These agents assist humans in…
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
Researchers introduce a new method called PEEU (Planning Experience Exploration and Utilization) to improve the task planning capabilities of small, open-source Multimodal Language Learning Models (MLLMs). These models…
From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
This research study explores the use of AI to create non-consensual sexually explicit content (SNEACI) targeting both celebrities and non-celebrity individuals on the platform 4chan. The study finds that there has been…
From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
Researchers studied AI-generated explicit content on 4chan and found a shift from targeting celebrities to non-celebrity individuals (55.8% vs. 4.7% in prior studies). Open-source models are widely used, with Stable…
From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
This research study investigates AI-generated explicit content known as Synthetic Non-consensual Sexually Explicit Imagery (SNEACI) on the online forum 4chan. The study finds a shift in target demographics, with…
From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
This research study explores the Wild West of AI-generated explicit content known as AI nudification on the anonymous online community 4chan. The authors identify over 24,000 such items and find that non-celebrity…
Heavy-Ball Q-Learning with Residual Weighting Correction
The paper presents a corrected version of Heavy-Ball Q-Learning for reinforcement learning, which is guaranteed to converge faster than standard Q-Learning under certain conditions. The analysis uses a switched linear…
How to evaluate clustering with ground truth?
The text discusses the use of external validation indexes for evaluating clustering algorithms when ground truth is available. It focuses on set-matching-based measures and recommends the Centroid Index (CI) as a…
In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics
The paper proposes a new method called In-Context Model Predictive Generation (ICMPG) for generating human motions based on textual descriptions in digital applications. The method combines language model planning with…
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