Improvisational Games as a Benchmark for Social Intelligence of AI Agents: The Case of Connections

arXiv 2604.00284 ↗First posted 31 Mar 2026cs.AI cs.MAcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

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LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

We formally introduce a improvisational wordplay game called Connections to explore reasoning capabilities of AI agents. Playing Connections combines skills in knowledge retrieval, summarization and awareness of cognitive states of other agents. We show how the game serves as a good benchmark for social intelligence abilities of language model based agents that go beyond the agents' own memory and deductive reasoning and also involve gauging the understanding capabilities of other agents. Finally, we show how through communication with other agents in a constrained environment, AI agents must demonstrate social awareness and intelligence in games involving collaboration.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 5 of its 28 references that are in the map. First posted on arXiv 31 Mar 2026.

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References · 5

2023Benchmarking Large Language Models for News Summarizationcited by 444
2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 439
2022Can language models learn from explanations in context?cited by 248
2023Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmarkcited by 62
2023ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfercited by 49

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