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2,343 notes. Notes 1,201 to 1,250, page 25 of 47.
RoboTTT: Context Scaling for Robot Policies
The research introduces RoboTTT, a robot model and training method that enables visuomotor context scaling to 8K timesteps, far beyond current state-of-the-art policies. This allows new robot capabilities like one-shot…
Scaling Behavior Foundation Model for Humanoid Robots
The research focuses on improving the performance of Behavior Foundation Models (BFMs) used for controlling humanoid robots. The authors propose a scaling recipe that involves coordinating three core components: 1)…
Scaling Behavior Foundation Model for Humanoid Robots
Researchers propose a scaling recipe for Behavior Foundation Models (BFMs) to improve the capabilities of humanoid robots. The recipe includes a learning paradigm, strategic synergy between data collection methods, and…
Scaling Behavior Foundation Model for Humanoid Robots
Researchers propose a scaling recipe for Behavior Foundation Models (BFMs) to improve the capabilities of humanoid robots, focusing on the learning paradigm, synergy between rollout quantity and reference motion…
Scaling Behavior Foundation Model for Humanoid Robots
Researchers have developed a new method for improving the performance of Behavior Foundation Models (BFMs) in controlling humanoid robots. The method involves coordinating three core components: motion tracking…
SceneBind: Binding What and Where Across Vision, Audio and Language
The researchers introduce SceneBind, a system that combines visual, auditory, and linguistic data to create a detailed representation of realistic scenes with both semantic (what is present) and spatial (where it is)…
SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
The text is about a new system called SciDiagramEdit that automates the editing of scientific diagrams in research papers based on natural language instructions. The system learns from revisions made to figures in…
SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
The paper presents SciDiagramEdit, a system that automates the time-consuming task of editing scientific diagrams in research papers based on authors' revisions. The system learns from natural language instructions and…
SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
The text introduces SciDiagramEdit, a framework for automating the editing of scientific diagrams in research papers based on natural language instructions. The framework learns from natural paper revisions and…
SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
The authors introduce SciDiagramEdit, a system designed to automate the time-consuming process of editing scientific diagrams in research papers based on natural language instructions. The system learns from paper…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces SearchOS, a system designed to improve the efficiency of open-domain information-seeking agents by making their search progress more explicit and persistent. It aims to prevent repetitive loops…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces SearchOS, a system that aims to improve the efficiency of open-domain information seeking for information-seeking agents. It struggles with tracking task progress as interaction histories grow and…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces a new system called SearchOS, a multi-agent framework designed to improve the efficiency and quality of open-domain information seeking for AI agents. The framework addresses the issue of tracking…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
Researchers introduce SearchOS, a system-level framework designed to improve the efficiency of multi-agent information-seeking systems. By making search progress explicit and persistent, it helps prevent repetitive…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces SearchOS, a system-level multi-agent framework designed to improve the quality and completeness of search results in open-domain information-seeking tasks. The framework addresses the issue of…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers propose a self-evolving annotation framework to create explainable and reliable datasets for Major Depressive Disorder (MDD) research. The framework uses a large language model (LLM) in combination with…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers have proposed a self-evolving framework to improve the quality of depression symptom annotations in AI systems for mental health research. The framework combines large language models with human expertise…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers propose a self-evolving annotation framework for Major Depressive Disorder (MDD) that combines large language model (LLM)-assisted labeling with expert verification. The goal is to create explainable…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers have proposed a new framework to improve the quality of annotations in depression-related datasets for artificial intelligence systems in mental health research. The framework uses a large language model…
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
Researchers propose a graph neural network approach to detect and analyze reasoning structures in large language models (LLMs) generated texts for improved robustness against paraphrasing and obfuscation techniques…
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
The paper proposes a new method for detecting text generated by large language models (LLMs) and attributing authorship to them. The method goes beyond just analyzing surface-level linguistic features and instead…
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
Researchers present StructureClaw, a workbench that allows Large Language Model (LLM) agents to operate through controlled engineering tasks with governed skills, typed tools, and shared artifact state. The team also…
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
Researchers introduce StructureClaw, a workbench designed to evaluate large language model (LLM) agents in structural engineering tasks. This workbench ensures the LLMs follow structured workflows and produces complete…
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
Researchers have developed StructureClaw, a workbench that allows large language model (LLM) agents to operate within a structured engineering environment through governed skills, typed tools, shared artifact state…
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
Researchers have developed StructureClaw, an artifact-centered workbench that uses LLM (Language Model) agents to operate through governed engineering skills and checks for completion, consistency, and execution of…
Symbal: Detecting Systematic Misalignments in Model-Generated Captions
Researchers have developed Symbal, a method for detecting systematic misalignments in image-text pairs generated by multimodal large language models (MLLMs). These misalignments occur when the model consistently…
Symbal: Detecting Systematic Misalignments in Model-Generated Captions
Researchers present Symbal, a system that identifies systematic misalignments in image-text pairs generated by multimodal large language models (MLLMs). The system uses a structured dual-stage setup and performs well…
teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
Researchers have developed teLLMe, a system to analyze urban driving data for understanding traffic safety and congestion by answering causal questions. The system uses dashcam annotations to build a structured event…
teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
Researchers developed teLLMe, a system that allows traffic agencies to analyze urban driving data using causal analysis methods. The system helps answer questions about the impact of various factors on traffic density…
teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
Researchers have developed teLLMe, a system that helps traffic agencies analyze large volumes of video-derived data for studying safety and congestion by answering causal questions about urban driving, such as 'How…
The Industrialization of Research ; On AI-Driven Science and Its Consequences
This paper discusses the industrialization of research through the use of artificial intelligence (AI), referring specifically to the US Department of Energy's Genesis Mission. The author raises seven concerns about…
The Industrialization of Research ; On AI-Driven Science and Its Consequences
The text discusses the industrialization of research through artificial intelligence (AI), outlining seven key questions raised by this transition, such as the erosion of intergenerational knowledge transmission, the…
The Industrialization of Research ; On AI-Driven Science and Its Consequences
The text discusses the transformation of scientific research due to artificial intelligence (AI), likening it to the industrialization of research. The author raises several concerns about this shift, including the…
The Industrialization of Research ; On AI-Driven Science and Its Consequences
The text discusses the industrialization of research due to artificial intelligence (AI), referring to a shift from human-led research to an automated pipeline model. The author raises several questions about the…
Towards Hierarchical Structure Understanding of Newspaper Images
The paper presents two approaches for understanding the complex and nested hierarchical structures in newspaper images. The first approach is a modular pipeline that combines existing open-source models to detect…
When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
This research investigates how people's understanding of their own authorship changes when using AI, particularly Generative AI. The study uses the CoAuthor dataset and finds that frequent users tend to overestimate…
When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
This research introduces the concept of 'authorship calibration' – users' awareness of their actual contributions when working with AI. The study, based on the CoAuthor dataset, found that frequent AI users tend to…
A Self-Evolving Agent for Longitudinal Personal Health Management
The authors developed an open-source agent architecture called HealthClaw for personal health management. It learns and adapts to changes in a person's routines, preferences, measurements, and risks over time. The…
A Self-Evolving Agent for Longitudinal Personal Health Management
A research paper introduces HealthClaw, a self-evolving agent architecture for personal health management. The system updates support as a person's routines, preferences, measurements, and risks change. It was…
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
The text is about AgentCompass, an open-source evaluation infrastructure designed to evaluate Large Language Models (LLMs) as they evolve into autonomous agents. The infrastructure aims to address the current…
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
The text discusses AgentCompass, an open-source infrastructure designed to evaluate Large Language Models (LLMs) as they transition into autonomous agents. The tool is lightweight, extensible, and organizes the…
AI-accelerated End-to-End Framework for Rapid Professional Upskilling
The paper presents an end-to-end AI-accelerated framework designed to improve professional upskilling programs by focusing on five stages: knowledge acquisition, content development, content review and verification…
AI-accelerated End-to-End Framework for Rapid Professional Upskilling
The paper presents an end-to-end AI-accelerated framework designed to improve professional upskilling programs' efficiency by focusing on five stages: knowledge acquisition, content development, content review and…
AI-accelerated End-to-End Framework for Rapid Professional Upskilling
A new end-to-end framework that uses AI to accelerate various stages of professional upskilling has been presented by the authors. The framework was validated through three external signals: approval by the US National…
AI-Augmented Human Resource Management? Insights from German companies
This study investigates how AI is being integrated into Human Resource Management (HRM) in German companies and whether it enhances or 'augments' HRM. The research findings, based on interviews, group discussions, and…
AIMO Interpretability Challenge
The text proposes the AIMO Interpretability Challenge, a competition aimed at distinguishing robust from spurious reasoning in advanced mathematical language models. The challenge will provide participants with new…
AIMO Interpretability Challenge
The AIMO Interpretability Challenge is a competition proposed by researchers aimed at distinguishing robust from spurious reasoning in advanced mathematical language models. The challenge focuses on evaluating the…
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
The MEDA system is an automated framework using Large Language Models (LLMs) and Symbolic Regression (SR) to discover Ordinary Differential Equations (ODEs) for biological systems. It retrieves background knowledge…
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
The MEDA system is a new framework that uses large language models and symbolic regression to automatically discover ordinary differential equation (ODE) models for biological systems. The system retrieves background…
CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems
The paper introduces Canonical Action Verification and Attestation (CAVA), a system designed to convert diverse agent activities into standardized action objects, addressing the challenge of governing heterogeneous AI…
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