mlsanityData to 14 Oct 2026

How Should I Pick a Foundation Model for My Robot? In Favor of a Community Evaluation Framework for Social Robots

arXiv 2608.06898 ↗First posted 7 Aug 2026cs.RO cs.CL cs.HCcited by 0

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Abstract (arXiv)

Researchers who seek to build social robot applications on foundation models are faced with a difficult question: how should we pick a model? Public leaderboards offer little guidance: the demands of real-time, embodied social interaction lie largely outside their focus. And direct evaluation is impractical at scale: each embodied study requires scarce participant, robot, and experimenter time. In this paper, we identify five evaluation dimensions for foundation models in social robots: (i) conversational competence, (ii) user safety, (iii) embodied character, (iv) target scene effectiveness, and (v) audience appropriateness. To make model selection cheaper and better informed, we propose a three-tiered evaluation funnel paradigm that first filters with general metrics, then extends to simulated interactions, and terminates in more expensive, robot-specific evaluation. We map all five dimensions across all three tiers, chart where applicable evaluation methods exist and are missing, and close with a call to action: let's build the evaluation framework together as a community.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 35 references that are in the map. First posted on arXiv 7 Aug 2026.

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23 of 35 references.

References · 23

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  5. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  6. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  7. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,807
  8. 2024A Survey on LLM-as-a-Judgecited by 1,423
  9. 2023Instruction-Following Evaluation for Large Language Modelscited by 1,228
  10. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
  11. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
  12. 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 505
11 more
  1. 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agentscited by 367
  2. 2024LiveBench: A Challenging, Contamination-Limited LLM Benchmarkcited by 262
  3. 2024OR-Bench: An Over-Refusal Benchmark for Large Language Modelscited by 244
  4. 2024BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practicescited by 114
  5. 2024SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agentscited by 93
  6. 2025RoboArena: Distributed Real-World Evaluation of Generalist Robot Policiescited by 64
  7. 2024Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMscited by 59
  8. 2025EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safetycited by 21
  9. 2024Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Harucited by 5
  10. 2024Towards Pareto Optimal Throughput in Small Language Model Servingcited by 5
  11. 2025Robot-Led Vision Language Model Wellbeing Assessment of Childrencited by 4

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