Pith. sign in

REVIEW 10 cited by

Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.08811 v1 pith:5HFE2FAU submitted 2024-09-13 cs.HC cs.AIcs.MA

Shao Zhang , Xihuai Wang , Wenhao Zhang , Yongshan Chen , Landi Gao , Dakuo Wang , Weinan Zhang , Xinbing Wang
show 1 more author
Ying Wen
This is my paper · ORCID
classification cs.HCcs.AIcs.MA
keywords communicationagentagentscapabilitycollaborationhumanmindmtom
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. When AI agents with ToM capability collaborate with humans, Mutual Theory of Mind (MToM) arises in such human-AI teams (HATs). The MToM process, which involves interactive communication and ToM-based strategy adjustment, affects the team's performance and collaboration process. To explore the MToM process, we conducted a mixed-design experiment using a large language model-driven AI agent with ToM and communication modules in a real-time shared-workspace task. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent and the feeling of being understood. Most participants in our study believe verbal communication increases human burden, and the results show that bidirectional communication leads to lower HAT performance. We discuss the results' implications for designing AI agents that collaborate with humans in real-time shared workspace tasks.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A dual-process LLM agent with a three-stage theory-of-mind module outperforms baselines in real-time Overcooked human-AI collaboration.

  2. Who's the Leader? Analyzing Novice Workflows in LLM-Assisted Debugging of Machine Learning Code

    cs.HC 2025-05 conditional novelty 6.0 of 10

    In an eight-person formative study, novice ML engineers who actively led the ChatGPT debugging conversation outperformed those who followed it, with patterns of over- and under-reliance.

  3. AI in Software Engineering: Perceived Roles and Their Impact on Adoption

    cs.SE 2025-04 conditional novelty 6.0 of 10

    Developers who attribute a greater number of roles to AI coding assistants, from tool to expert, report higher perceived usefulness and ease of use, but the evidence is correlational.

  4. Position: Theory of Mind Benchmarks are Broken for Large Language Models

    cs.AI 2024-12 conditional novelty 6.0 of 10

    The paper proposes that LLM theory-of-mind evaluation should measure functional adaptation to partners, not just literal prediction of their behavior, and shows the two can diverge sharply in simple games.

  5. Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details

    cs.AI 2026-08 conditional novelty 5.0 of 10

    For Other-Play in Yokai, agents trained with different implementation details coordinate across implementations about as well as across seeds, supporting inter-seed cross-play as a proxy for cross-implementation evaluation.

  6. Effect of Adaptive Communication Support on LLM-powered Human-Robot Collaboration

    cs.HC 2024-11 conditional novelty 5.0 of 10

    A human-robot teaming framework with adjustable LLM feedback improves collaboration in easy and medium tasks, but overly frequent feedback from a less capable LLM hurts performance in hard tasks.

  7. AI Agent Behavioral Science

    q-bio.NC 2025-06 conditional novelty 4.0 of 10

    AI agents should be studied as behavioral entities shaped by context and interaction, not only as trained models.

  8. Theory of Mind in Large Language Models: Assessment and Enhancement

    cs.CL 2025-04 conditional novelty 4.0 of 10

    A survey of recent story-based Theory of Mind benchmarks and enhancement strategies for large language models, organized by mental state coverage and method type.

  9. Defending Against Network Attacks for Secure AI Agent Migration in Vehicular Metaverses

    cs.NI 2024-12 conditional novelty 4.0 of 10

    A MAPPO-based pre-migration strategy with a trust-score filter is claimed to defend AI agent migration in vehicular metaverses against DDoS and malicious RSU attacks and to cut latency by roughly 43% in simulation.

  10. When Researchers Say Mental Model/Theory of Mind of AI, What Are They Really Talking About?

    cs.HC 2025-10 conditional novelty 3.0 of 10

    Researchers' claims of AI theory of mind are really about behavioral prediction, so AI evaluation should shift from isolated cognitive tests to human-AI interaction.

Pith tools