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CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
Researchers introduce CARD, a framework for creating realistic simulations of online credit card discussion threads. This system takes into account various factors such as reply structure, comment function, stance…
CEAA: A Cognitive Embodied Agents Architecture for Interactive Computing Systems
The authors propose a modular cognitive architecture to develop Intelligent Virtual Agents (IVAs) that can operate in real-time interactive virtual environments. This architecture aims to bridge the gap between…
Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localization
The study evaluates a multi-task DETR framework that simultaneously predicts the malignancy of mammogram images and locates lesions, using modern backbones such as ConvNeXtV2 and DINOv3. The results show that these…
Multimodal Model Diffing for Feature Discovery and Control
Researchers developed a framework called MMDiff to help understand, control, and audit the behavior of Multimodal Large Language Models (MLLMs). The framework identifies features that are altered by multimodal…
Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
Researchers present the Evaluation Agent framework, a system that efficiently evaluates visual generative models using human-like strategies. It decomposes evaluation requests into sub-aspects, generates prompts…
Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
Researchers propose a new method for fine-tuning large language models in a federated learning paradigm using Low-rank adaptation (LoRA). They investigate two strategies, Share-A/Local-B and Share-B/Local-A, and reveal…
Towards Expert-level Medical AI for Real-time Video Consultations
A new AI system called AMIE (Articulate Medical Intelligence Explorer) in a video configuration was tested for real-time clinical video consultations. The system performed on par or better than human primary care…
AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
Researchers present AV-AIVAT, a tool that uses AIVAT for variance reduction in imperfect-information games and combines it with Confidence Sequences (CS) for anytime-valid evaluation. The new method reduces the number…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
Researchers introduce a framework to evaluate the quality of benchmarks used for task-oriented conversational agents, which can lead to unreliable evaluations due to inconsistencies, simplistic scenarios, or limited…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
Researchers have introduced a new framework to evaluate the quality of benchmarks used for task-oriented conversational agents. The framework uses language models (LLMs) to assess the consistency, complexity, and…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
The authors propose a reference-free framework to evaluate the quality of task-oriented conversational agent benchmarks, which are often used to assess these agents. The framework uses Large Language Models (LLMs) as…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The paper proposes a new method for document search named Reliable Embedding-free Agentic Document-search (READ), which aims to improve the accuracy of retrieval-augmented generation on specific types of documents such…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The authors propose a new method for document search called Reliable Embedding-free Agentic Document-search (READ). In contrast to the common design of retrieval-augmented generation, READ uses an agent that reads the…
Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors
Researchers used Concept Activation Vectors (CAVs) to analyze the bias of two automatic L2 speaking assessment systems: a text-based BERT grader and a multimodal grader based on Whisper. The study shows that the…
Challenges in Evaluating Explanation Methods for Static and Evolving Data
The paper discusses the limitations in evaluating Explainable Artificial Intelligence (XAI) systems, specifically focusing on the DetoxAI image recognition system. It highlights the need for better evaluation methods…
Challenges in Evaluating Explanation Methods for Static and Evolving Data
This research paper highlights the issues in evaluating Explanation Methods for AI systems, using the DetoxAI image recognition system as an example. It discusses methods for adapting explanations to changing data…
Comparative Approaches to Agent Retrieval over Large Skill Libraries
The study compares two systems for deciding which skills to load from a large skill library by an agent. The first system is a hybrid ranker that combines lexical and dense-embedding retrieval, while the second system…
Comparative Approaches to Agent Retrieval over Large Skill Libraries
The study compares two systems for deciding which skills to load from large skill libraries in AI agents. The first system is a hybrid ranker that combines lexical and dense-embedding retrieval for sparse, on-demand…
Comparative Approaches to Agent Retrieval over Large Skill Libraries
The research compares two systems for deciding which skills to load from a large skill library when creating agents. One system combines lexical and dense-embedding retrieval, while the other uses a typed knowledge…
Comparative Approaches to Agent Retrieval over Large Skill Libraries
Researchers compared two methods for deciding which skills to load from large skill libraries in AI agents: a hybrid ranker combining lexical and dense-embedding retrieval, and a typed knowledge graph encoding workflow…
Contextual Information Policy Optimization for Search Agents
The paper proposes a new method called Contextual Information Policy Optimization (CIPO) for search agents. This method aims to improve the efficiency of evidence use in complex or evolving information tasks by…
Contextual Information Policy Optimization for Search Agents
The text introduces a new method called Contextual Information Policy Optimization (CIPO), which aims to improve the reliability of search agents by ensuring they use retrieved evidence effectively during multi-step…
Contextual Information Policy Optimization for Search Agents
The researchers propose a new reinforcement learning framework called Contextual Information Policy Optimization (CIPO) to improve the reliability of search agents for knowledge-intensive tasks. CIPO encourages the use…
ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment
The paper introduces ECHO, a locally-deployable health assistant designed for long-term chronic care management. It consists of three integrated modules: an agentic chatbot, a safety layer, and a speech assessment…
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