mlsanityData to 14 Oct 2026

VISTA: A Versatile Interactive User Simulation Toolkit for Agent Evaluation

arXiv 2606.11079 ↗First posted 9 Jun 2026cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 9 Jun 2026.

Builds on

18 of 25 references.

References · 18

  1. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  2. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  3. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
  4. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  5. 2025Survey on Evaluation of LLM-based Agentscited by 204
  6. 2025Evaluation and Benchmarking of LLM Agents: A Surveycited by 152
  7. 2025Where LLM Agents Fail and How They can Learn From Failurescited by 133
  8. 2024CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environmentscited by 71
  9. 2026Mind the Sim2Real Gap in User Simulation for Agentic Taskscited by 54
  10. 2025SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants?cited by 50
  11. 2025UXAgent: An LLM Agent-Based Usability Testing Framework for Web Designcited by 40
  12. 2026Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluationscited by 40
6 more
  1. 2025LLM Agent Meets Agentic AI: Can LLM Agents Simulate Customers to Evaluate Agentic-AI-based Shopping Assistants?cited by 15
  2. 2026RealUserSim: Bridging the Reality Gap in Agent Benchmarking via Grounded User Simulationcited by 12
  3. 2026Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agentscited by 10
  4. 2025Human vs. Agent in Task-Oriented Conversationscited by 9
  5. 2025SAGE: A Top-Down Bottom-Up Knowledge-Grounded User Simulator for Multi-turn AGent Evaluationcited by 6
  6. 2026ShopGym: An Integrated Framework for Realistic Simulation and Scalable Benchmarking of E-Commerce Web Agentscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

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  1. 2026AppWorld-UL: Benchmarking Diverse Agent-User Interactions for Tool-Use
  2. 2025AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents
  3. 2026UserProxyBench: Evaluating LLM User Simulators for Agent Benchmarks and Training
  4. 2026AcCoRD: Evaluating User-Agent Collaboration Under Realistic User Preference Dynamics
  5. 2026Efficient Agent Evaluation via Diversity-Guided User Simulation
  6. 2026CRAB-Bench: Evaluating LLM Agents under Complex Task Dependencies and Human-aligned User Simulation
  7. 2026Iterating Toward Better Search: A Two-Agent Simulation Framework for Evaluating Agentic Search Architectures in E-Commerce
  8. 2026Interaction-as-a-Judge