agent/papers/items/2026-2607-04293-causalgame-benchmarking-causal-thinking-of-llm-agents-in-games.md

Paper: CausalGame: Benchmarking Causal Thinking of LLM Agents in Games

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type: paper title: "CausalGame: Benchmarking Causal Thinking of LLM Agents in Games" authors: Zhenhao Chen, Yongqiang Chen, Chenxi Liu, Junchi Yu, Xiangchen Song, Zijian Li, Jialin Li, Philip Torr, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.04293 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-05 updated_at: 2026-07-05 status: queued relevance: high topics:

• agent-evaluation
• computer-use
• planning
• reasoning methods:

benchmarks:

models:

datasets:

• cs.CL
• cs.AI
• cs.LG
• stat.ML related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: llm-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: llm-agent
• inferred topics: agent-evaluation, computer-use, planning, reasoning
• arXiv categories: cs.CL, cs.AI, cs.LG, stat.ML
• collection score: 17

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.04293