CRAFT: Grounded Multi-Agent Coordination Under Partial Information
📰 ArXiv cs.AI
CRAFT is a multi-agent benchmark for evaluating pragmatic communication in large language models under partial information
Action Steps
- Formalize the problem as a multi-sender pragmatic reasoning task
- Decompose the task into smaller sub-problems using a diagnostic framework
- Evaluate the performance of large language models in constructing a shared 3D structure under partial information
- Analyze the results to identify areas for improvement in the models' communication and coordination capabilities
Who Needs to Know This
AI researchers and engineers working on multi-agent systems and natural language processing can benefit from CRAFT to evaluate and improve their models' ability to coordinate and communicate effectively
Key Insight
💡 CRAFT provides a framework for evaluating the ability of large language models to coordinate and communicate effectively in multi-agent settings with incomplete information
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🤖 Introducing CRAFT: a benchmark for evaluating pragmatic communication in large language models under partial information 📚
Key Takeaways
CRAFT is a multi-agent benchmark for evaluating pragmatic communication in large language models under partial information
Full Article
Title: CRAFT: Grounded Multi-Agent Coordination Under Partial Information
Abstract:
arXiv:2603.25268v1 Announce Type: cross Abstract: We introduce CRAFT, a multi-agent benchmark for evaluating pragmatic communication in large language models under strict partial information. In this setting, multiple agents with complementary but incomplete views must coordinate through natural language to construct a shared 3D structure that no single agent can fully observe. We formalize this problem as a multi-sender pragmatic reasoning task and provide a diagnostic framework that decomposes
Abstract:
arXiv:2603.25268v1 Announce Type: cross Abstract: We introduce CRAFT, a multi-agent benchmark for evaluating pragmatic communication in large language models under strict partial information. In this setting, multiple agents with complementary but incomplete views must coordinate through natural language to construct a shared 3D structure that no single agent can fully observe. We formalize this problem as a multi-sender pragmatic reasoning task and provide a diagnostic framework that decomposes
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