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2,348 notes, page 16 of 98
Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence
Researchers have developed a new framework called 'Agentic Self-Improvement' to improve the performance of Image-to-Video (I2V) models, which are commonly used for automated content creation. The new framework aims to…
Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN, Transformer, and Foundation-Model-Era Visual Explanations
This research paper provides a comprehensive review of various visual explanation methods used in explainable artificial intelligence, specifically focusing on Class Activation Mapping (CAM). The paper discusses the…
Co-constructing sociotechnical AI governance: participatory system mapping using algorithm registers
In this paper, researchers examine a municipal algorithm register used for decision-support tools in welfare benefits eligibility assessments. They find that engaging diverse stakeholders in participatory system…
Constructing Dynamic Master Logic Models as Knowledge Graphs for Complex System Diagnostics Using Retrieval-Augmented Large Language Models
The study introduces a framework to automate the construction of Dynamic Master Logic (DML) models for complex systems using Retrieval-Augmented Generation and Large Language Models. These models, represented as…
Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents
Researchers introduce Convergent Detour Hijacking (CDH), a text-based attack that manipulates third-party skills in LLM agents to make tasks unnecessarily expensive and time-consuming without affecting their final…
Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents
Researchers introduce Convergent Detour Hijacking (CDH), a technique that manipulates Language Learning Model (LLM) agents by using natural language descriptions and instruction bodies to steer the agents onto more…
Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents
Researchers introduce Convergent Detour Hijacking (CDH), an attack that manipulates language model (LLM) agents by exploiting their reliance on third-party skills for task selection and planning. The attack can lead to…
CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations
Researchers have developed a new benchmark called CTBench to evaluate the performance of AI agents in troubleshooting network issues in telecom environments. The benchmark focuses on root cause analysis and path…
CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations
The paper introduces CTBench, a public benchmark for evaluating AI agents' ability to perform troubleshooting tasks in telecom network operations. The focus is on root cause analysis and path restoration. The authors…
Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams
A new benchmark called Diagram-MMU has been created to test the ability of Multimodal Large Language Models (MLLMs) in parsing and understanding scientific diagrams. The benchmark includes 3,700 curated diagrams and…
Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams
The paper discusses Diagram-MMU, a new benchmark designed to evaluate the ability of Multimodal Large Language Models (MLLMs) in parsing and understanding scientific diagrams. The benchmark includes 3.7k diagrams and…
Do LLMs Take Care of Their Own? Similarity Signals Can Induce Cooperation
This research introduces a framework to evaluate language models (LLMs) when agents are provided with graded similarity signals. The study finds variations in how different LLMs handle these signals across various…
Do LLMs Take Care of Their Own? Similarity Signals Can Induce Cooperation
Researchers introduce a framework for evaluating how Language Model (LLM) agents cooperate when provided with similarity signals. The study finds that different LLM models exhibit varying behaviors, but some modern…
DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation
Researchers propose DreamFly, a framework for aerial vision-language navigation that addresses challenges like limited historical context, short planning horizons, and unreliable implicit termination. The framework…
DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation
The authors propose DreamFly, a framework for aerial vision-language navigation that uses a causally aligned historical memory to reason about the past without leaking future information. It also uses receding-horizon…
DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation
In this paper, the authors introduce DreamFly, a new framework for aerial vision-language navigation that addresses the challenges of limited historical context, short planning horizons, and unreliable implicit…
From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices
The paper proposes a framework for medical device safety using Large Language Models (LLMs). The goal is to streamline the process of managing risk-management evidence in medical devices that are becoming increasingly…
From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices
The paper proposes a framework that uses large language models (LLMs) to support medical device safety work, which is document-based and costly due to the need for safety and domain experts. The framework connects…
Graph-Structured Rubrics: Compiling Rubrics into Typed Evaluation Graphs for LLM Judges
The paper introduces Graph-Structured Rubrics (GSR), a method that converts rubrics into response-independent typed evaluation graphs before observing responses. This allows for more efficient and accurate scoring of…
GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
The paper introduces GUIDE, a framework designed to automate the creation of enterprise guideline documents. The system uses six specialized agents to parse, extract, check consistency, evaluate, escalate human…
GUIDE: Governed Unified Intelligence for Document-to-Artifact Generation in Enterprise Settings
The paper introduces GUIDE, a framework for generating enterprise guideline documents that are often complex and multimodal (combining text, tables, images). Existing systems struggle with maintaining the structure of…
How Organizations Use AI: Evidence from ChatGPT
The authors study the use of ChatGPT in organizations by analyzing data from ChatGPT Enterprise accounts linked with worker roles, task classifications, and financial data of public companies. They found rapid growth…
Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL
The paper proposes a method to integrate robot locomotion and manipulation using Reinforcement Learning (RL) and Sample-based Model Predictive Control (SMPC). The approach generates offline datasets from SMPC…
Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL
The authors propose a method to integrate locomotion and manipulation for robots using a combination of Sample-based Model Predictive Control (SMPC) and Reinforcement Learning (RL). By leveraging SMPC, they create an…
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