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REVIEW 4 major objections 6 minor 21 references

Can LLMs Play \^O \u{A}n Quan Game? A Study of Multi-Step Planning and Decision Making

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that in the game Ô Ăn Quan, larger LLMs generate deeper planning traces and more long-term reasoning, while model size does not predict wins against smaller models.

desk verdict New game testbed with a plausible central observation, but the planning-depth metric is unvalidated and the scale-based conclusions go beyond the evidence. read the letter →

arxiv 2507.03711 v3 pith:CK5RHDAD submitted 2025-07-04 cs.CL

Sang Quang Nguyen , Kiet Van Nguyen , Vinh-Tiep Nguyen , Thanh Duc Ngo , Ngan Luu-Thuy Nguyen , Duy-Dinh Le This is my paper
classification cs.CL
keywords largelanguagemodelsÔĂnQuanmulti-stepplanningdecision-makingstrategicreasoningpersona-basedagentsgame-theoreticAI
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether large language models can plan over multiple steps in a fully observable, deterministic game, and uses the traditional Vietnamese board game Ô Ăn Quan as the testbed. The authors build LLM agents with personas such as balanced, defensive, aggressive, and risk-taking, then measure win rates, planning depth (steps generated per move), and the share of short-term vs. long-term reasoning. Their central claim is that larger models plan deeper and show more long-term strategic reasoning, yet this depth does not reliably convert into more wins. They also find that balanced and defensive strategies outperform aggressive ones over a full game and that planning depth declines for all models as the game progresses. The paper itself notes the evaluation is zero-shot, with a simplified environment, and that conclusions rest on reasoning labels rather than full gameplay outcomes.

What carries the argument

The central object is a closed-loop agent framework built on the Ô Ăn Quan game: each turn, the model receives the current board state, the history of previous moves, the rule set (including extra rules like the Immature Mandarin and forced capture chains), and a natural-language persona prompt, and returns a rationale plus an action of position and direction. The load-bearing mechanism is the planning-depth metric, defined as the number of steps the model generates per move, combined with a zero-shot classifier that assigns each rationale to short-term gain, long-term strategy, or ambiguous. Together these measurements support the paper's claims about how scale changes planning and strategy.

What would settle it

Inspect the reasoning traces and count how many distinct future board states the model considers before acting. If long traces mention few or no alternative board configurations, or if instructing a model to be more verbose raises its planning-depth score without improving the quality of its moves, the proxy is invalid and the scale-to-planning claim loses its support.

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Extended reading notes

Core claim

The paper reports that Llama-3.3-70B-Instruct obtains the highest planning depth, with some moves exceeding 250 generated steps, followed by Llama-3.1-8B-Instruct at around 150 steps, while Llama-3.2-3B-Instruct mostly stays below 100 steps. A Gemini-2.0-Flash classifier labels the models' rationales and shows the 70B model favoring long-term strategy (64.86%) and the 3B model favoring short-term gain (70.18%), with the 8B model in between. In head-to-head games, however, the 8B model beats the 70B model 38% to 34%, and the 70B does not dominate smaller opponents. The paper concludes that model scale tracks planning depth and long-horizon reasoning but not competitive success, and that the game's early rounds are where the models plan most deeply.

Load-bearing premise

The load-bearing premise is that the number of steps a model writes before choosing a move measures how deeply it plans, with no check that longer output corresponds to wider or deeper search rather than verbosity or repeated deliberation.

Editorial extensions

If this is right

  • If larger models genuinely plan deeper, then scale should matter most for long-horizon tasks even when short-run outcome metrics are flat.
  • The dissociation between planning depth and win rate implies that game outcomes alone are an incomplete benchmark for LLM planning ability.
  • The universal decline in planning depth as games progress suggests planning effort is state-dependent and may weaken as uncertainty or constraints grow.
  • The stronger full-game results of balanced and defensive personas argue for testing multiple persona strategies rather than a single style.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's planning-depth proxy is text-based; a natural extension would count the distinct board states a model actually evaluates, or test whether forcing longer reasoning through prompting alone raises the metric without improving moves.
  • Pitting the LLM agents against a known-state-space search baseline, such as minimax or Monte Carlo tree search, would reveal whether the depth-versus-outcome dissociation is specific to LLMs or inherent to the game's complexity.
  • If personas steer planning style, the same model should produce measurably different move distributions under aggressive versus defensive prompts; that is a direct test of the persona mechanism the paper assumes.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces the traditional Vietnamese board game Ô Ăn Quan as a testbed for evaluating the strategic planning and decision-making abilities of three Llama-based instruction-tuned models (3B, 8B, and 70B). The authors implement a closed-loop game environment with additional rules, define several agent personas, and conduct zero-shot experiments. They report persona-based score averages, win/draw rates against the 70B model, distributions of a 'planning depth' measure defined as the number of generated steps per move, and reasoning-type classifications produced by Gemini. They conclude that larger models exhibit deeper planning but not necessarily higher win rates.

Significance. The paper's main contribution is a new, fully observable, deterministic game environment for probing LLM planning, complete with source code. The descriptive results on win rates and reasoning-type distributions, if properly qualified, could be of interest to the community studying LLM strategic behavior. However, the central claim about planning depth is built on an unvalidated proxy (generated step count) that confounds verbosity with planning, and the quantitative comparisons lack statistical support. As a result, the paper currently reads as a promising pilot study rather than a definitive evaluation; with additional validation and statistical rigor, the underlying setup could support stronger claims.

major comments (4)
  1. [V.C, Figure 3] The central claim that "larger models possess not only better language understanding but also more advanced planning capabilities" (Section V.C) rests entirely on the measure of "planning depth" defined as the number of steps generated per move. This proxy is never validated against any external notion of planning, such as the depth of game-tree search, the number of legal moves considered, or the quality of the resulting moves, nor is output verbosity controlled for. Since larger instruction-tuned models are known to produce longer rationales, the observed step-count differences are equally consistent with over-explanation as with deeper planning; the claim as stated is therefore unsupported.
  2. [V.B, Table II] The cross-architecture comparison in Table II lacks statistical support: no error bars, confidence intervals, or significance tests are reported, and the number of games for these matches is not stated (the 50-game figure appears only for the persona experiments in Section V.A). The observed differences, such as 38% vs. 34% win rate, are small and could easily arise from sampling noise, so the conclusion that "model scale alone is not a sufficient predictor of success" (Section V.B) is not established. In addition, the table setup is ambiguous: the 70B model appears both as the reference opponent and as a tested agent (with † denoting first player), so it is unclear against whom the 70B row is measured and whether the 3B and 8B models always play second.
  3. [V.D, Table III] The reasoning-type distributions in Table III are produced by Gemini in a zero-shot setting with no human reliability check, inter-annotator agreement, or comparison against a gold standard. These labels are then used to support the claim that larger models show "reduced impulsiveness" and "more coherent and consistent strategic justifications" (Section V.D). Without classifier validation, the percentages may simply reflect a bias toward labeling longer or more fluent text as "LONG TERM STRATEGY," which would make this analysis circular with the unvalidated planning-depth metric.
  4. [V.A, Table I] The persona comparison in Table I reports average scores without error bars or significance tests, despite the differences between agents being small (e.g., 23.5 vs. 22.4 average points). The claim that "strategies with better long-term balance tend to perform more consistently across full matches" (Section V.A) is therefore not statistically supported, and the phase-wise breakdown (EGE/MGE/LGE) is not accompanied by any measure of variance.
minor comments (6)
  1. [Figure 3] The caption states that the data are aggregated across 50 games played against Llama-3.3-70B-Instruct, but panel (c) shows the 70B model itself; clarify who the opponent is for each panel and whether the 70B's own step counts are measured in games against a different opponent.
  2. [V.C] The term "number of steps" is never precisely defined; specify whether a step is a sentence, a line, a token, or a thought block in the generated reasoning trace.
  3. [Limitations] The Limitations section states that "the models often struggle with multi-step planning and complex capture chains, limiting their strategic depth," which appears to contradict the conclusion that larger models exhibit "superior planning depth" (Section V.C). Please reconcile these statements.
  4. [Appendix B] Generation details such as temperature, max tokens, decoding strategy, and number of random seeds are not reported, which limits reproducibility despite the provided source-code link.
  5. [Section II] Reference [18] ("Machine theory of mind") is cited as an example of "structured planning environments and strategic dialogue systems," but that paper is about theory-of-mind inference; either replace the reference or adjust the sentence.
  6. [V.D] The model name "Gemini-2.0-Flask" appears to be a typo; should it be "Gemini-2.0-Flash"?

Circularity Check

1 steps flagged · score 5.0 of 10

The central 'planning depth' result is the operational definition of the measure; the conclusion that larger models plan more deeply restates the observation that they produce longer generated rationales.

  1. self definitional [Section V.C, Planning Depth Analysis (Figure 3)]
    "Figure 3 show the planning depth—measured as the number of steps generated per move—across different LLM architectures. ... These findings suggest that larger models possess not only better language understanding but also more advanced planning capabilities in complex, rule-based environments like Ô Ăn Quan."

    Planning depth is explicitly defined as the number of generated steps per move, and the conclusion that larger models possess more advanced planning is drawn solely from that count. Since the measure is the count itself, the 'finding' that Llama-3.3-70B has the highest planning depth is true by definition of the metric; no independent notion of planning (search depth, legal moves considered, move quality) is introduced. The jump from 'more generated steps' to 'more advanced planning capabilities' relabels the measurement rather than deriving a separate result, so the central claim reduces to the operationalization.

full rationale

Most of the paper's measurements are self-contained: win and draw rates (Table II) and persona-based scores (Table I) come from games played under the stated rules, and the reasoning-type distribution (Table III) is an external zero-shot classification by Gemini, unvalidated but not circular. The only load-bearing circular step is the planning-depth analysis in Section V.C, where planning depth is defined as the number of steps generated per move and then offered as evidence that larger models have more advanced planning capabilities. That conclusion is definitionally equivalent to observing that larger instruction-tuned models emit longer rationales, and the Limitations section's concession that 'evaluation is based on reasoning labels instead of full gameplay outcomes' reinforces that no external planning measure anchors the result. The paper does not rely on self-citation chains or imported uniqueness theorems, and the remaining experimental claims are independent measurements. The score of 5.0 reflects partial circularity: the headline scale-and-planning claim is tied to the metric by construction, while the other results stand on their own.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper does not fit any numeric parameters to data. Its central claims instead rest on unvalidated measurement choices: treating generated step counts as planning depth, trusting an external classifier for reasoning labels, and assuming the implemented rule set matches the real game. These are design assumptions rather than fitted constants.

assumptions (4)
  • ad hoc to paper The number of steps an LLM generates per move reflects its planning depth.
    The paper equates planning depth with the count of generated reasoning steps, a proxy that is never validated against actual search depth or move quality.
  • domain assumption Gemini-2.0-Flash correctly classifies reasoning traces into short-term, long-term, and ambiguous categories.
    The distribution of reasoning types in Table III relies entirely on an external model's zero-shot labels, with no human agreement check.
  • domain assumption The five extra rules E1-E5 define the official rules of Ô Ăn Quan as implemented.
    The game environment and all outcomes depend on this rule set, but no external validation or reference to an authoritative rules source is provided beyond a Wikipedia link.
  • domain assumption A RandomAgent baseline selects uniformly among legal moves, providing a fair reference.
    The strategy experiments compare persona agents against this baseline, but no check is reported that the random agent's distribution is balanced or that 50 games are sufficient.

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Cite this review

Pith. "Pith review of Can LLMs Play \^O \u{A}n Quan Game? A Study of Multi-Step Planning and Decision Making." pith.science (2026). https://pith.science/paper/CK5RHDAD

@misc{pith2026250703711,
  author       = {Pith},
  title        = {Pith review of: Can LLMs Play \^O \uAn Quan Game? A Study of Multi-Step Planning and Decision Making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CK5RHDAD}},
  note         = {Machine review of arXiv:2507.03711}
}
read the original abstract

In this paper, we explore the ability of large language models (LLMs) to plan and make decisions through the lens of the traditional Vietnamese board game, \^O \u{A}n Quan. This game, which involves a series of strategic token movements and captures, offers a unique environment for evaluating the decision-making and strategic capabilities of LLMs. Specifically, we develop various agent personas, ranging from aggressive to defensive, and employ the \^O \u{A}n Quan game as a testbed for assessing LLM performance across different strategies. Through experimentation with models like Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, and Llama-3.3-70B-Instruct, we aim to understand how these models execute strategic decision-making, plan moves, and manage dynamic game states. The results will offer insights into the strengths and weaknesses of LLMs in terms of reasoning and strategy, contributing to a deeper understanding of their general capabilities.

Figures

Figures reproduced from arXiv: 2507.03711 by the authors.

Figure 1
Figure 1. Overview of the LLM-based agent framework for [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. the Scattering Phase, where a player selects one of their positions and redistributes all peasant tokens it contains clockwise or counterclockwise around the board; and the Capturing Phase, where tokens may be captured based on specific spatial configurations. The game concludes when both Mandarin tokens are captured or no legal moves remain. Beyond its simple rules, Oˆ An Quan presents a rich set of ˘ strategic cha… view at source ↗
Figure 2
Figure 2. Illustration of token movement in Oˆ An Quan: from scattering to capturing. ˘ tion. As shown in Algorithm 1, the player selects a direction and distributes all tokens from the chosen position one by one into subsequent positions, wrapping around the board as needed. Algorithm 2 Basic Capturing Require: Final scatter index i, direction D ∈ {LTR, RTL}, board state B[0 . . . N − 1] 1: Captured ← 0 2: while True do 3: i… view at source ↗
Figures from the paper (2 more)
Figure 3
Figure 3. Figure 3: Planning step distributions over 25 rounds, aggregated across 50 games played against Llama-3.3-70B-Instruct. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]
Figure 4
Figure 4. Figure 4: Reasoning type distributions over 25 rounds, aggregated across 50 games played against Llama-3.3-70B-Instruct. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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