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agent/papers/items/2026-2607-04293-causalgame-benchmarking-causal-thinking-of-llm-agents-in-games.md
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# Paper: CausalGame: Benchmarking Causal Thinking of LLM Agents in Games
---
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