VISTA: A Versatile Interactive User Simulation Toolkit for Agent Evaluation
Evaluation remains a critical bottleneck for interactive agent development. Existing evaluation methods often rely on static benchmarks, which fail to capture the dynamic, multi-step nature of agentic behavior and struggle to expose meaningful failure modes. While user-simulation-based evaluation offers a promising alternative, existing simulation frameworks suffer from two major limitations. First, they provide limited mechanisms for evaluating the quality and comprehensiveness of simulated interactions, making it difficult to assess whether a simulator sufficiently explores an agent's capabilities and failure modes. Second, most frameworks are restricted to either UI-only actions or API-only actions, limiting their ability to model the full range of realistic user behaviors. To address these limitations, we propose VISTA, a Versatile Interactive user Simulation Toolkit for Agent evaluation. Our toolkit includes a suite of six metrics for measuring the realism, capability coverage, and interaction effectiveness of simulated interactions. In addition, we develop a hybrid user simulator that integrates both UI-based interactions and API-based interactions, enabling more realistic and comprehensive evaluation across diverse interactive environments. We evaluate VISTA in e-commerce shopping and education customer service settings and demonstrate that it produces more realistic and comprehensive evaluations than existing methods.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography
Medical image segmentation is a core component of precision medicine, and 3D computed tomography (CT) is one of the most important imaging techniques. A highly accurate and clinically applicable segmentation foundation m…
Computed Tomography (CT)Image SegmentationInteractive SegmentationMedical Image Segmentation+2InFL-UX: A Toolkit for Web-Based Interactive Federated Learning
This paper presents InFL-UX, an interactive, proof-of-concept browser-based Federated Learning (FL) toolkit designed to integrate user contributions seamlessly into the machine learning (ML) workflow. InFL-UX enables use…
Federated LearningVISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging
Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging. However, the domain gaps and clinical use cases f…
Interactive SegmentationSegmentationVISTA-OCR: Towards generative and interactive end to end OCR models
We introduce \textbf{VISTA-OCR} (Vision and Spatially-aware Text Analysis OCR), a lightweight architecture that unifies text detection and recognition within a single generative model. Unlike conventional methods that re…
DecoderOptical Character Recognition (OCR)Text DetectionBeyond the Black Box: Demystifying Multi-Turn LLM Reasoning with VISTA
Recent research has increasingly focused on the reasoning capabilities of Large Language Models (LLMs) in multi-turn interactions, as these scenarios more closely mirror real-world problem-solving. However, analyzing the…