MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings

• URL: http://arxiv.org/abs/2603.09643v2
• Date: Wed, 11 Mar 2026 09:38:04 GMT
• Title: MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings
• Abstract summary: In customer experience management domain, the agent's behaviour evolves as the agent learns about user personality.<n>We propose the MM-tau-p$2$ benchmark with metrics for evaluating the robustness of multi-modal agents.<n>Overall, MM-tau-p$2$ builds on our prior work FOCAL and provides a holistic way of evaluating multi-modal agents.
• Score: 0.42970700836450487
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Current evaluation frameworks and benchmarks for LLM powered agents focus on text chat driven agents, these frameworks do not expose the persona of user to the agent, thus operating in a user agnostic environment. Importantly, in customer experience management domain, the agent's behaviour evolves as the agent learns about user personality. With proliferation of real time TTS and multi-modal language models, LLM based agents are gradually going to become multi-modal. Towards this, we propose the MM-tau-p$^2$ benchmark with metrics for evaluating the robustness of multi-modal agents in dual control setting with and without persona adaption of user, while also taking user inputs in the planning process to resolve a user query. In particular, our work shows that even with state of-the-art frontier LLMs like GPT-5, GPT 4.1, there are additional considerations measured using metrics viz. multi-modal robustness, turn overhead while introducing multi-modality into LLM based agents. Overall, MM-tau-p$^2$ builds on our prior work FOCAL and provides a holistic way of evaluating multi-modal agents in an automated way by introducing 12 novel metrics. We also provide estimates of these metrics on the telecom and retail domains by using the LLM-as-judge approach using carefully crafted prompts with well defined rubrics for evaluating each conversation.

Related papers

• PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence [22.89938884559524]
  Large language model (LLM) on edge device enables personalized LLM agents for various users.<n>Growing availability of diverse personalized agents presents a unique opportunity for peer-to-peer (P2P) collaboration.<n>This paper introduces PPAI, the first personalized LLM agent interoperability system.
  arXiv  Detail & Related papers  (2026-05-18T08:49:33Z)
• Beyond IVR: Benchmarking Customer Support LLM Agents for Business-Adherence [1.8357468337756873]
  We introduce JourneyBench, a benchmark designed to assess policy-aware agents in customer support.<n>We evaluate multiple state-of-the-art agent designs using two agent designs: a Static-Prompt Agent (SPA) and a Dynamic-Prompt Agent (DPA)<n>We show that DPA significantly boosts policy adherence, even allowing smaller models like GPT-4o-mini to outperform more capable ones like GPT-4o-mini.
  arXiv  Detail & Related papers  (2026-01-02T07:21:23Z)
• AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress [71.02263260394261]
  Large language models (LLMs) still encounter challenges in multi-turn decision-making tasks.<n>We build process reward models (PRMs) to evaluate each decision and guide the agent's decision-making process.<n>AgentPRM captures both the interdependence between sequential decisions and their contribution to the final goal.
  arXiv  Detail & Related papers  (2025-11-11T14:57:54Z)
• Multi-Agent Tool-Integrated Policy Optimization [67.12841355267678]
  Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks.<n>Existing implementations typically rely on a single agent, but they suffer from limited context length and noisy tool responses.<n>No existing methods support effective reinforcement learning post-training of tool-integrated multi-agent frameworks.
  arXiv  Detail & Related papers  (2025-10-06T10:44:04Z)
• PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time [87.99027488664282]
  PersonaAgent is a framework designed to address versatile personalization tasks.<n>It integrates a personalized memory module and a personalized action module.<n>Test-time user-preference alignment strategy ensures real-time user preference alignment.
  arXiv  Detail & Related papers  (2025-06-06T17:29:49Z)
• Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale [53.059480071818136]
  Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks.<n> PERSONAMEM features curated user profiles with over 180 simulated user-LLM interaction histories.<n>We evaluate LLM chatbots' ability to identify the most suitable response according to the current state of the user's profile.
  arXiv  Detail & Related papers  (2025-04-19T08:16:10Z)
• Large Language Models as User-Agents for Evaluating Task-Oriented-Dialogue Systems [6.8738526619759535]
  offline datasets have been used to evaluate task-oriented dialogue (TOD) models. User-agents, which are context-aware, can simulate the variability and unpredictability of human conversations.
  arXiv  Detail & Related papers  (2024-11-15T06:05:45Z)
• Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation [21.281471662696372]
  We propose the Multimodal Large Language Model-enhanced Multimodaln Sequential Recommendation (MLLM-MSR) model.<n>To capture the dynamic user preference, we design a two-stage user preference summarization method.<n>We then employ a recurrent user preference summarization generation paradigm to capture the dynamic changes in user preferences.
  arXiv  Detail & Related papers  (2024-08-19T04:44:32Z)
• RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents [27.807695570974644]
  We propose a novel method, textscRePrompt, which does agradient descent"-like approach to optimize the step-by-step instructions in the prompts given to LLM agents.<n>By leveraging intermediate feedback, textscRePrompt can optimize the prompt without the need for a final solution checker.
  arXiv  Detail & Related papers  (2024-06-17T01:23:11Z)
• A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration [55.35849138235116]
  We propose automatically selecting a team of agents from candidates to collaborate in a dynamic communication structure toward different tasks and domains. Specifically, we build a framework named Dynamic LLM-Powered Agent Network ($textDyLAN$) for LLM-powered agent collaboration. We demonstrate that DyLAN outperforms strong baselines in code generation, decision-making, general reasoning, and arithmetic reasoning tasks with moderate computational cost.
  arXiv  Detail & Related papers  (2023-10-03T16:05:48Z)
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations [53.76682562935373]
  We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools. InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
  arXiv  Detail & Related papers  (2023-08-31T07:36:44Z)

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.