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arxiv: 2503.06047 · v2 · submitted 2025-03-08 · 💻 cs.AI · cs.CL

DSGBench: A Diverse Strategic Game Benchmark for Evaluating LLM-based Agents in Complex Decision-Making Environments

Wenjie Tang , Yuan Zhou , Erqiang Xu , Keyan Cheng , Minne Li , Liquan Xiao This is my paper

Pith reviewed 2026-05-23 01:10 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords LLM agentsstrategic decision-makingbenchmark evaluationmulti-agent interactionlong-horizon reasoninggame-based testingperformance analysisdecision tracking
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The pith

DSGBench evaluates LLM agents on six strategic games using a five-dimension scoring system and automated decision tracking.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces DSGBench to test LLM-based agents in environments that require extended planning, interactions among multiple agents, and choices amid uncertainty. Current benchmarks often isolate narrow skills or rely on single overall scores, so the new platform draws on six adjustable games that demand multi-step decisions. It applies five separate scoring dimensions for a more detailed view of capabilities and logs agent choices automatically to map out behavior patterns and strategy shifts. Evaluations of several open and closed models uncover consistent differences in performance across tasks and point to recurring weaknesses in how the models reason.

Core claim

DSGBench combines six complex strategic games as test environments, a five-dimension evaluation system that breaks down decision quality, and an automated tracking system that records trajectories and turning points. When applied to six popular LLM agents, the platform shows distinct performance profiles and identifies recurring limitations in handling long-horizon and uncertain choices.

What carries the argument

DSGBench, the benchmark that supplies six customizable strategic games, a five-dimension scoring rubric, and automated decision-trajectory logging to enable granular analysis of agent behavior.

If this is right

  • The five scoring dimensions allow separate measurement of distinct aspects of strategic performance rather than a single aggregate score.
  • Automated tracking of decisions reveals specific points where agent strategies change or break down.
  • Game customization supports testing the same models at multiple difficulty levels and with different objectives.
  • Results across open-source and closed-source models can inform choices for particular strategic applications.
  • Observed systemic limitations in the tested agents indicate areas for targeted improvement in future model training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Widespread use of the benchmark could shift development focus toward models that maintain coherent plans over dozens of steps.
  • The tracking mechanism might be adapted to non-game domains such as supply-chain or negotiation simulations to diagnose similar failures.
  • If the five dimensions prove stable across additional games, they could serve as a template for designing new evaluation suites in multi-agent settings.

Load-bearing premise

The six selected games together with the five evaluation dimensions adequately represent the range of demands in complex strategic decision-making.

What would settle it

Agents that score highly on DSGBench would need to be shown performing poorly on other strategic tasks that share the same demands for long-horizon reasoning and multi-agent interaction.

Figures

Figures reproduced from arXiv: 2503.06047 by Erqiang Xu, Keyan Cheng, Liquan Xiao, Minne Li, Wenjie Tang, Yuan Zhou.

Figure 1
Figure 1. Figure 1: The overall framework of DSGBench. The framework consists of (1) a multi-game environment supporting both asynchronous and synchronous interactions; (2) fine-grained capability metrics for strategic planning, real-time decision-making, and team collaboration; and (3) decision trajectory tracking tools that collaboratively analyze agents’ decision-making processes. Through observation-to-prompt and response… view at source ↗
Figure 2
Figure 2. Figure 2: Performance indicators for evaluating LLM capabilities in [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Code Architecture of DSGBench Framework. B StarCraft II StarCraft II is a real-time strategy (RTS) game developed by Blizzard Entertainment. It is the successor to StarCraft: Broodwar, one of the most successful RTS games. The objective of StarCraft II is to establish bases, manage resources, build armies, and destroy the enemy’s base. The most common game setup is 1v1, where each player chooses one of thr… view at source ↗
read the original abstract

Large language model (LLM)-based agents are increasingly applied to complex strategic environments that demand long-horizon reasoning, multi-agent interaction, and decision-making under uncertainty. However, common existing benchmarks either assess isolated skills, lack environmental diversity, or rely on broad overall metrics. To address these issues, we introduce DSGBench, a more rigorous evaluation platform for strategic decision-making tasks. Firstly, it incorporates six complex strategic games which serve as ideal testbeds due to their long-term and multi-dimensional decision-making demands and flexibility in customizing tasks with various difficulty levels and targets. Secondly, DSGBench employs a fine-grained evaluation scoring system which examines the decision-making capabilities by looking into the performance in five specific dimensions, offering a comprehensive assessment in a better-designed fashion. Furthermore, DSGBench also incorporates an automated decision-tracking mechanism which enables in-depth analysis of agent behaviour patterns and the turning points in their strategies. We evaluate six popular LLM agents, including open-source and closed-source models, and observe distinct strengths and limitations among various tasks. Through decision trajectory analysis, we further identify systemic limitations in different LLMs. These findings offer valuable insights for model selection and future LLM-based agent development.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces DSGBench as a benchmark for LLM-based agents in strategic decision-making, consisting of six complex games chosen for their long-term and multi-dimensional demands, a five-dimension fine-grained scoring system, and an automated decision-tracking mechanism. It evaluates six popular LLM agents (open- and closed-source), reports distinct strengths and limitations across tasks, and uses trajectory analysis to identify systemic LLM limitations, claiming this setup provides a more rigorous evaluation platform than existing benchmarks that lack diversity or use broad metrics.

Significance. If the six games and five dimensions are shown to systematically address gaps in diversity and metric granularity, and if the automated tracking enables reproducible insights, the benchmark could support more targeted development of LLM agents for multi-agent, uncertain, long-horizon settings; the explicit evaluation of multiple models and trajectory analysis are concrete strengths that could be leveraged by the community.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Benchmark Design): the central claim that DSGBench is 'more rigorous' because it incorporates six games plus a five-dimension scoring system plus automated tracking rests on the untested assumption that these choices adequately capture strategic diversity and address prior limitations; no systematic mapping of game features to strategic primitives, no comparison table to existing benchmarks, and no validation (e.g., inter-dimension correlation or coverage analysis) is supplied to demonstrate necessity and sufficiency of the five dimensions.
  2. [Evaluation section] Evaluation section: the reported score differences among the six agents demonstrate observable variation but, absent error analysis, inter-rater validation of the scoring rubric, or evidence that the five dimensions avoid post-hoc selection, the results cannot confirm that the platform itself is more rigorous than prior benchmarks.
minor comments (1)
  1. [Abstract] Abstract: the phrasing 'in a better-designed fashion' is vague; replace with a concrete statement of how the five-dimension system improves on broad overall metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below and outline revisions that will strengthen the justification for DSGBench's design and evaluation.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Benchmark Design): the central claim that DSGBench is 'more rigorous' because it incorporates six games plus a five-dimension scoring system plus automated tracking rests on the untested assumption that these choices adequately capture strategic diversity and address prior limitations; no systematic mapping of game features to strategic primitives, no comparison table to existing benchmarks, and no validation (e.g., inter-dimension correlation or coverage analysis) is supplied to demonstrate necessity and sufficiency of the five dimensions.

    Authors: We agree that the manuscript would benefit from explicit justification of the design choices. In the revision, we will add a systematic mapping in §3 linking each of the six games to strategic primitives (long-horizon planning, multi-agent interaction, uncertainty handling, etc.). We will also insert a comparison table contrasting DSGBench against prior benchmarks on diversity, metric granularity, and tracking features. Finally, we will report inter-dimension correlation and coverage analyses to demonstrate the necessity and sufficiency of the five dimensions. revision: yes

  2. Referee: [Evaluation section] Evaluation section: the reported score differences among the six agents demonstrate observable variation but, absent error analysis, inter-rater validation of the scoring rubric, or evidence that the five dimensions avoid post-hoc selection, the results cannot confirm that the platform itself is more rigorous than prior benchmarks.

    Authors: The distinct performance patterns across agents and dimensions already show the platform's ability to differentiate capabilities. We will add error analysis to the evaluation section and document that the five dimensions were derived from established strategic decision-making literature rather than selected post-hoc. Because scoring is fully automated via the decision-tracking mechanism, conventional inter-rater validation does not apply; we will note this limitation and outline future human-validation plans. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical benchmark design with no derivations or self-referential reductions

full rationale

The manuscript introduces DSGBench as an empirical evaluation platform by selecting six games and five scoring dimensions. No equations, fitted parameters, predictions, or first-principles derivations appear in the provided text. The claim of greater rigor rests on design assertions (long-term/multi-dimensional demands, flexibility) rather than any quantity defined in terms of itself or prior self-citations. No load-bearing self-citation chains, ansatzes, or renamings of known results are present. This is a standard benchmark paper whose central claims do not reduce by construction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the selected games and dimensions sufficiently represent strategic decision-making; no free parameters, invented entities, or additional axioms are introduced in the abstract.

axioms (1)
  • domain assumption Existing benchmarks assess isolated skills or lack environmental diversity, and the six games plus five dimensions address these gaps.
    Invoked in the abstract to motivate the new benchmark.

pith-pipeline@v0.9.0 · 5755 in / 1205 out tokens · 29079 ms · 2026-05-23T01:10:32.286731+00:00 · methodology

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Forward citations

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  3. From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review

    cs.AI 2025-04 accept novelty 4.0

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · cited by 3 Pith papers

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    Game Overview: At 10:07 game time, our current situation is characterized by a strong economy with substantial mineral reserves but relatively low gas resources. We have a modest army supply and are in the process of researching key technologies

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    Our Situation: 3.1 Units and Buildings: – Buildings : We have one Nexus, two Pylons, two gas buildings, four Warp Gates, one Cybernetics Core, one Twilight Council, and one Robotics Facility. – Units : We have 16 Probes, 2 Zealots, and 1 Stalker. – Planning : We are planning to build another Robotics Facility. 3.2 Economy: – Resources : We have 5245 miner...

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