Evaluating Multimodal Interactive Agents

• URL: http://arxiv.org/abs/2205.13274v1
• Date: Thu, 26 May 2022 11:18:09 GMT
• Title: Evaluating Multimodal Interactive Agents
• Abstract summary: We present a novel approach to evaluation called the Standardised Test Suite (STS) The STS uses behavioural scenarios mined from real human interaction data. It is fast, controlled, interpretable, and representative of naturalistic interactions.
• Score: 14.936474798718653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research. However, evaluating these interactions is challenging: collecting online human-agent interactions is slow and expensive, yet faster proxy metrics often do not correlate well with interactive evaluation. In this paper, we assess the merits of these existing evaluation metrics and present a novel approach to evaluation called the Standardised Test Suite (STS). The STS uses behavioural scenarios mined from real human interaction data. Agents see replayed scenario context, receive an instruction, and are then given control to complete the interaction offline. These agent continuations are recorded and sent to human annotators to mark as success or failure, and agents are ranked according to the proportion of continuations in which they succeed. The resulting STS is fast, controlled, interpretable, and representative of naturalistic interactions. Altogether, the STS consolidates much of what is desirable across many of our standard evaluation metrics, allowing us to accelerate research progress towards producing agents that can interact naturally with humans. https://youtu.be/YR1TngGORGQ

Related papers

• Benchmarking AI Agents for Addressing Scientific Challenges Across Scales [118.2204632627895]
  SciAgentArena is a systematic benchmark for evaluating AI agents in real-world scientific research scenarios.<n>It comprises approximately 200 tasks with stepwise verification and an interactive, agent-agnostic environment for assessing diverse AI agents.<n>We find that current agents can contribute effectively to well-specified data-analysis, particularly when the task structure and evaluation criteria are clear.
  arXiv  Detail & Related papers  (2026-06-10T22:55:30Z)
• The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development? [80.24951682268332]
  We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development.<n>To ensure evaluation integrity, this framework is secured by multi-layer defenses against reward hacking.<n>We demonstrate that meta-agents rarely match human-engineered baseline policies, and the few that do are dominated by proprietary frontier models.
  arXiv  Detail & Related papers  (2026-06-03T04:58:17Z)
• AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents [50.667928258781934]
  We introduce AgentOdyssey, a novel evaluation framework that procedurally generates open-ended text games with rich entities, world dynamics, and long-horizon tasks.<n>We propose a multifaceted evaluation methodology that measures not only game progress but also offers diagnostic tests on world knowledge acquisition, episodic memory, object and action exploration, action diversity, and model cost.<n>Our experimental results reveal critical limits in agents' key abilities, as well as factors that influence their meaningful horizon.
  arXiv  Detail & Related papers  (2026-05-29T22:40:51Z)
• Position: AI Agents Are Not (Yet) a Panacea for Social Simulation [62.891898598784415]
  Recent advances in large language models (LLMs) have spurred growing interest in using LLM-integrated agents for social simulation.<n>This position paper argues that LLM-based agents are not (yet) a panacea for social simulation.
  arXiv  Detail & Related papers  (2026-02-19T06:35:07Z)
• AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation [0.7031557790463293]
  We introduce Align, a framework that learns world-model-driven agents from human interactions.<n>We generate counterfactual trajectories around demonstrations and prompt the LLM to compare its decisions with human choices, identify suboptimal actions, and extract lessons.
  arXiv  Detail & Related papers  (2026-01-02T03:01:33Z)
• InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs [72.5651722107621]
  InterAgent is an end-to-end framework for text-driven physics-based multi-agent humanoid control.<n>We introduce an autoregressive diffusion transformer equipped with multi-stream blocks, which decouples proprioception, exteroception, and action to cross-modal interference.<n>We also propose a novel interaction graph exteroception representation that explicitly captures fine-grained joint-to-joint spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-08T10:46:01Z)
• Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents [58.00130492861884]
  TraitBasis is a lightweight, model-agnostic method for systematically stress testing AI agents.<n>TraitBasis learns directions in activation space corresponding to steerable user traits.<n>We observe on average a 2%-30% performance degradation on $tau$-Trait across frontier models.
  arXiv  Detail & Related papers  (2025-10-06T05:03:57Z)
• The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation [0.16921396880325779]
  We introduce a novel evaluation framework that uses multi-agent debate as a controlled "social laboratory"<n>We show that assigned personas induce stable, measurable psychometric profiles, particularly in cognitive effort.<n>This work provides a blueprint for a new class of dynamic, psychometrically grounded evaluation protocols.
  arXiv  Detail & Related papers  (2025-10-01T07:10:28Z)
• Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction [46.286440953594266]
  We propose to scale test-time interaction, an untapped dimension of test-time scaling.<n>We first show that even prompting-based interaction scaling can improve task success on web benchmarks non-trivially.<n>We introduce TTI (Test-Time Interaction), a curriculum-based online reinforcement learning approach that trains agents by adaptively adjusting their rollout lengths.
  arXiv  Detail & Related papers  (2025-06-09T17:50:02Z)
• AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents [28.20409050985182]
  A/B testing remains constrained by its dependence on the large-scale and live traffic of human participants. We present AgentA/B, a novel system that automatically simulate user interaction behaviors with real webpages. Our findings suggest AgentA/B can emulate human-like behavior patterns.
  arXiv  Detail & Related papers  (2025-04-13T21:10:56Z)
• Interact with me: Joint Egocentric Forecasting of Intent to Interact, Attitude and Social Actions [25.464036307823974]
  SocialEgoNet is a graph-based framework that exploits task dependencies through a hierarchical learning approach. SocialEgoNet uses body skeletons (keypoints from face, hands and body) extracted from only 1 second of video input for high inference speed. For evaluation, we augment an existing egocentric human-agent interaction with new class labels and bounding box annotations.
  arXiv  Detail & Related papers  (2024-12-21T16:54:28Z)
• Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues [54.81155589931697]
  Collaborative Instance object Navigation (CoIN) is a new task setting where the agent actively resolve uncertainties about the target instance. We propose a novel training-free method, Agent-user Interaction with UncerTainty Awareness (AIUTA) First, upon object detection, a Self-Questioner model initiates a self-dialogue within the agent to obtain a complete and accurate observation description. An Interaction Trigger module determines whether to ask a question to the human, continue or halt navigation.
  arXiv  Detail & Related papers  (2024-12-02T08:16:38Z)
• X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents [56.64615470513102]
  The Turing test examines whether AIs exhibit human-like behaviour in natural language conversations.<n>Traditional setting limits each participant to one message at a time and requires constant human participation.<n>This paper proposes textbftextscX-Turing, which enhances the original test with a textitburst dialogue pattern.
  arXiv  Detail & Related papers  (2024-08-19T09:57:28Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems [112.76941157194544]
  We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
  arXiv  Detail & Related papers  (2023-10-13T16:37:14Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate [57.71597869337909]
  We build a multi-agent referee team called ChatEval to autonomously discuss and evaluate the quality of generated responses from different models. Our analysis shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments.
  arXiv  Detail & Related papers  (2023-08-14T15:13:04Z)
• Conveying Autonomous Robot Capabilities through Contrasting Behaviour Summaries [8.413049356622201]
  We present an adaptive search method for efficiently generating contrasting behaviour summaries. Our results indicate that adaptive search can efficiently identify informative contrasting scenarios that enable humans to accurately select the better performing agent.
  arXiv  Detail & Related papers  (2023-04-01T18:20:59Z)
• Evaluating Human-Language Model Interaction [79.33022878034627]
  We develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems. We design five tasks to cover different forms of interaction: social dialogue, question answering, crossword puzzles, summarization, and metaphor generation. We find that better non-interactive performance does not always translate to better human-LM interaction.
  arXiv  Detail & Related papers  (2022-12-19T18:59:45Z)
• Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback [16.268581985382433]
  An important goal in artificial intelligence is to create agents that can both interact naturally with humans and learn from their feedback. Here we demonstrate how to use reinforcement learning from human feedback to improve upon simulated, embodied agents.
  arXiv  Detail & Related papers  (2022-11-21T16:00:31Z)
• MUG: Interactive Multimodal Grounding on User Interfaces [12.035123646959669]
  We present MUG, a novel interactive task for multimodal grounding where a user and an agent work collaboratively on an interface screen. Prior works modeled multimodal UI grounding in one round: the user gives a command and the agent responds to the command. MUG allows multiple rounds of interactions such that upon seeing the agent responses, the user can give further commands for the agent to refine or even correct its actions.
  arXiv  Detail & Related papers  (2022-09-29T21:08:18Z)
• INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions [47.90088587508672]
  InSCIt is a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering.
  arXiv  Detail & Related papers  (2022-07-02T06:18:12Z)
• Imitating Interactive Intelligence [24.95842455898523]
  We study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. To build agents that can robustly interact with humans, we would ideally train them while they interact with humans. We use ideas from inverse reinforcement learning to reduce the disparities between human-human and agent-agent interactive behaviour.
  arXiv  Detail & Related papers  (2020-12-10T13:55:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.