arXiv:2604.20443v2 Announce Type: replace-cross Abstract: We introduce DialToM, an annotated Theory of Mind (ToM) benchmark built from naturalistic human-human dialogues using a multiple-choice evaluation framework. Concurrent with recent work showing a gap between explicit mental-state inference and applied ToM in synthetic settings~\cite{gu2024simpletom}, we establish a stricter \emph{State-Driven Diagnostic Probe} in which models must forecast state-consistent dialogue trajectories solely from isolated mental-state profiles without dialogue context. Our evaluation reveals a systematic reaso
The continuous advancements in AI research necessitate more sophisticated benchmarks to accurately assess model capabilities, especially in complex human-like reasoning tasks.
This benchmark addresses a critical gap in evaluating AI's understanding of mental states, which is fundamental for developing truly intelligent and context-aware autonomous agents.
The introduction of DialToM provides a more rigorous diagnostic tool for assessing 'Theory of Mind' in AI, potentially accelerating development in human-AI interaction and agentic systems.
- · AI researchers
- · AI ethics and safety organizations
- · Developers of AI agents
- · AI models with superficial ToM capabilities
- · Benchmarks that rely solely on explicit inference
Refinement of AI models specifically to address the challenges posed by the DialToM benchmark.
Accelerated development of more robust, state-aware AI agents capable of nuanced human interaction.
Enhanced trust and broader adoption of AI agents in complex decision-making and collaborative environments due to improved 'Theory of Mind'.
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