Distributed Partial Information Puzzles: Examining Common Ground Construction Under Epistemic Asymmetry

2026-03-05 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Multimodal AI · Depth: Advanced, quick

Summary

Researchers introduce the Distributed Partial Information Puzzle (DPIP), a novel collaborative construction task designed to investigate common ground establishment in AI systems under conditions of epistemic asymmetry. This task involves multiple parties with differing information, necessitating rich multimodal communication. A corresponding multimodal dataset has been created, featuring temporally aligned annotations across speech, gesture, and action, specifically designed to facilitate reasoning about propositional content and belief dynamics. The study evaluates two distinct approaches to modeling common ground: state-of-the-art large language models (LLMs) prompted for belief inference from multimodal inputs, and an axiomatic pipeline based on Dynamic Epistemic Logic (DEL). Initial findings from the DPIP dataset reveal that tracking both task progression and belief states presents a significant challenge for current LLMs.

Key takeaway

For research scientists developing collaborative AI, this work highlights that current LLMs struggle with tracking shared beliefs and task progression in scenarios with asymmetric information. You should consider integrating explicit common ground modeling techniques, such as those inspired by Dynamic Epistemic Logic, alongside LLMs to improve collaborative intelligence. Further research into multimodal belief state tracking is crucial for robust AI collaboration.

Key insights

Epistemic asymmetry in collaborative tasks challenges AI systems' common ground construction.

Principles

• Common ground is fundamental for collaboration.
• Multimodal communication enriches collaborative tasks.

Method

The Distributed Partial Information Puzzle (DPIP) elicits multimodal communication under epistemic asymmetry, with common ground modeled via LLMs or a Dynamic Epistemic Logic (DEL) pipeline.

In practice

• Use DPIP to test AI collaborative capabilities.
• Annotate multimodal data for belief dynamics.

Topics

• Distributed Partial Information Puzzle
• Common Ground
• Epistemic Asymmetry
• Multimodal Communication
• Large Language Models

Best for: Research Scientist, AI Researcher, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.