General Modular Harness for LLM Agents in Multi-Turn Gaming Environments

Haojian Jin; Haoyang Yu; Hao Zhang; Lanxiang Hu; Yuxuan Zhang

REVIEW 4 major objections 6 minor 26 references

General Modular Harness for LLM Agents in Multi-Turn Gaming Environments

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

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Pith's one-line read A three-module harness of perception, memory, and reasoning lifts LLM gameplay performance across four classic games, with statistically significant gains per game.

desk verdict The harness's claimed gains over unharnessed baselines are likely inflated by an unmatched prompt-optimization condition; this is a solid ablation study that needs a control fix. read the letter →

arxiv 2507.11633 v1 pith:WK46PZBF submitted 2025-07-15 cs.AI

Yuxuan Zhang , Haoyang Yu , Lanxiang Hu , Haojian Jin , Hao Zhang This is my paper · ORCID

classification cs.AI

keywords LLMagentsmodularharnessperceptionmodulememoryreasoningmulti-turngamespromptoptimizationgamebenchmarks

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

The paper tries to show that a fixed set of three modules—perception, memory, and reasoning—wrapped around any single LLM or VLM backbone raises multi-turn gameplay performance across four diverse games (Sokoban, Candy Crush, 2048, Tetris) without domain-specific engineering. Across ten models, the full harness improves scores relative to the unharnessed backbone with paired-sample $t$-tests significant at $p<0.05$ on every game, and harnessed runs show a much stronger separation from random play (mean Glass's $\delta$ effect size 2.757 versus 0.009). Ablations reveal complementary roles: perception scaffolding matters most in spatially structured games (Sokoban, Tetris), while memory/reflection matters most in long-horizon games (2048, Candy Crush). A prompt-optimization stage built into the harness also reduces sensitivity to prompt choice. If correct, this supports the view that modular scaffolding, not task-specific engineering, is a broadly effective route to generalist game agents.

What carries the argument

The carrying mechanism is the three-module harness itself, designed to be toggleable so each module's marginal contribution can be isolated. Perception converts game states into deterministic structured tables (and optionally overlaid grid images) so the model receives exact spatial information; memory stores the last $N$ (state, action) pairs and prompts the model to reflect on its previous action by comparing current and prior states, functioning as an internal reward signal in the spirit of verbal self-critique; reasoning is the controller that integrates perception and memory outputs and selects the next action. A second machinery is the two-stage prompt-optimization pipeline, which starts from empirically designed prompt templates and then refines them with a search-based compiler across five optimizer models, selecting the template with the best average development score across target models; the paper reports this standardization reduces cross-prompt variance.

What would settle it

Re-run the four games with the unharnessed baseline using the exact DSPy/SIMBA-optimized prompt that the harness uses, and increase runs per model per game well beyond three; if the performance gap vanishes or reverses, the perception and memory modules are not the cause of the gains.

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Extended reading notes

Core claim

The central discovery, stated on the paper's own terms, is that a modular harness decomposing an agent into perception, memory, and reasoning components yields consistent, statistically significant performance gains over an unharnessed backbone across all four tested games for the ten models with paired runs. The improvements are reported per game: Candy Crush +217.50 points ($t(9)=4.22$, $p=0.0022$), Sokoban +1.97 ($t(9)=3.02$, $p=0.0144$), 2048 +17.81 ($t(9)=2.36$, $p=0.0424$), and Tetris +5.60 ($t(9)=2.27$, $p=0.0490$). The paper also finds through ablations that perception alone is the dominant contributor in grid-based spatial games, memory alone is critical for temporally extended games, and combining both is usually additive or better, which the authors take as evidence that the harness exposes distinct module-level capability patterns that zero-shot evaluation misses.

Load-bearing premise

The unharnessed control may not have been given the same automatically optimized prompt as the harnessed condition, so part of the measured improvement could be due to prompt quality rather than the perception and memory modules.

Editorial extensions

If this is right

The same three-module harness should transfer to other Gymnasium- or Retro-compatible environments with minimal engineering, giving a general recipe for agent scaffolding rather than per-environment hacks.
Module priorities can be predicted from task structure: perception scaffolding for spatially or visually demanding games, memory scaffolding for long-horizon or delayed-reward games, and both for the largest gains.
Because the combined-module setting spreads model scores more widely, it can serve as a higher-resolution benchmark that distinguishes models that look similar under zero-shot evaluation.
Prompt-optimized templates lower performance variance across models and games, making cross-model comparisons more reliable and reproducible.

Reading between the lines

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

If the memory module is what rescues weak zero-shot models in 2048 and Candy Crush, the same mechanism may lift LLM performance in other partially observable multi-turn workflows, such as web or desktop automation, where agents currently lose track of state.
The large average gains (Candy Crush +217.50 points) suggest much of the unharnessed deficit comes from interface failures rather than reasoning failures; giving the baseline the exact optimized prompt used by the harness is a cheap way to test that claim.
The borderline Tetris result ($p=0.0490$) with only three runs per model is fragile; a pre-registered replication with more runs would establish whether the effect size is real or a small-sample artifact.
The reported correlations between game scores and standard reasoning benchmarks (Sokoban with math/code, Tetris/2048 with pattern puzzles) hint that this game suite could act as a low-cost proxy for general reasoning evaluations, but the correlation evidence alone does not establish that causality.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's the short version: this is a useful empirical study with a clean ablation design, but its headline result is not as clean as the paper says. The main problem is the control condition. The harness uses a DSPy/SIMBA-optimized prompt P* (Section 3.4). The paper never tells us what prompt the "No Harness" baseline received, and Section 4.2 defines it only as "zero-shot without any module support or memory prompt." There is no trajectory store or reflection in the no-harness condition, so P* can't be instantiated verbatim; if the control used a hand-written prompt, the gains in Table 1 conflate prompt optimization with the perception and memory modules. And Section 4.3 shows prompt optimization alone can move scores by hundreds of points in 2048. That confound affects all four games, not just the borderline ones.

To be fair, what's new here is the systematic comparison: 13 backbones, four games, paired t-tests, Glass's delta, and an ablation that separates perception and memory. The pattern—memory helps in long-horizon puzzles (2048, Candy Crush), perception helps in spatially structured games (Sokoban, Tetris)—is plausible, and the correlation study with standard benchmarks is a nice idea even if 8 models is too few to trust the Spearman values.

Soft spots beyond the prompt confound: the Tetris t-test is fragile. p=0.049 with t(9) depends on including o1 and o3, both single-run models; drop them and the difference likely loses significance. There's also a clear transcription error: Table 2 lists gpt-4o's Tetris '+Both' score as 106.7, identical to its 2048 row; should be ~14. And three runs per model (one for o1/o3) is thin for games with high variance like Candy Crush.

The "wide spectrum" language overstates things—four turn-based grid/tile games, no real-time or 3D environments—so I'd want the generality claims scaled back.

Who's this for? People designing agent harnesses and benchmark suites. It deserves a serious referee, but the revision must specify the control prompt, ideally run the same prompt optimization on both conditions, release code, fix the table, and soften the claims.

I'd send it to review, with the prompt confound as the required major revision.

Referee Report

4 major / 6 minor

Summary. The paper introduces a modular harness for LLM/VLM agents in multi-turn game environments, composed of perception, memory, and reasoning modules. It evaluates 13 models across four Gym-compatible games (Sokoban, Tetris, 2048, Candy Crush), reporting that the full harness significantly outperforms an unharnessed baseline under paired t-tests (Section 4.1.2) and presenting ablations that attribute gains to perception and memory in different game types (Section 4.2). The authors also describe a two-stage prompt-optimization pipeline using DSPy/SIMBA (Section 3.4) and a correlation study between game scores and established LLM benchmarks (Section 4.4). The central empirical claim is that the modular harness yields consistent, statistically significant performance improvements over the unharnessed backbone.

Significance. If the central comparison were unconfounded, this would be a useful contribution: the work proposes a unified, Gym-compatible evaluation methodology for modular agent design, evaluates a broad set of frontier models, reports paired statistics and effect sizes, and includes ablations that isolate perception and memory. The prompt-standardization analysis in Section 4.3 is a worthwhile practical result, and the correlation study adds a broader view of what game-based performance predicts. However, the headline claim in Section 4.1.2 is currently not interpretable as a module effect because the no-harness control condition is not specified to have received the same prompt-optimization treatment. The paper does not provide code or raw trajectories, so the statistical summaries cannot currently be independently verified or reproduced.

major comments (4)

[§3.4, §4.1.2, Table 1] The no-harness control condition is not adequately specified. Section 3.4 defines the harness prompt as [{J[min(0,i−N):i−1]}, R_{i-1}, s_i], and Algorithm 1 selects a single optimized prompt P* on development environments of the same games reported in Table 1. A no-harness agent has no trajectory store J or reflection R, so P* cannot be instantiated verbatim, and the paper never states what prompt the control received or whether it underwent any DSPy optimization. Since Table 5 shows that different prompt templates can shift 2048 scores by hundreds of points, the paired gains in Section 4.1.2 may be due to prompt quality rather than the perception and memory modules. This confound affects all four games and undermines the causal reading of the headline claim.
[§4.1.2, Table 1] The Tetris significance result is fragile. The paired t-test (t(9)=2.27, p=0.049) includes o1 and o3, which are marked in Table 1 as single-run models. Excluding those two models and using the reported means for the remaining eight models yields a mean paired difference of about 2.86, t(7)≈1.56, p≈0.16, which is no longer significant. The paper should report the t-tests with and without single-run models and should temper the claim of consistent gains across all four games.
[Table 2] Table 2 contains an apparent data error: for gpt-4o-2024-11-20 in Tetris under '+Both', the reported value is 106.7±3.5, which is identical to that model's 2048 '+Both' score and far above the maximum Tetris score observed anywhere in Table 1 (42.0 for o3). This cell invalidates the specific claim that combined perception and memory support produces strong Tetris gains for gpt-4o and calls for correction and re-analysis of the affected ablation comparisons.
[§3.4, Algorithm 1] Algorithm 1 and Section 3.4 cite SIMBA (Wu et al., 2019) as the prompt-optimization method, but that reference describes an image-reconstruction technique, not a prompt optimizer. As a result, the actual DSPy optimization procedure is not specified, which prevents reproduction of P* and obscures the treatment that distinguishes the harnessed from the unharnessed condition. The authors should either provide the correct reference for the SIMBA optimizer or give a precise description of the optimization algorithm.

minor comments (6)

[§3.4] The indexing expression J[min(0,i−N):i−1] is likely a typo for max(0,i−N), since min(0,i−N) is nonpositive for i≥0; as written, the trajectory window is undefined.
[§3.3] The memory module's reflection is described as 'an internal reward signal' and assigned the same signature R:S×A×S→R as the environment reward, which conflates self-generated critique with the game's reward function.
[§4.1, Table 1] Section 4.1 states that each model has three evaluation runs per game, but Table 1 notes that models marked * (o1 and o3) were run only once; these statements should be reconciled.
[Appendix B] The appendix heading 'Harness Analsysi' contains a typo, and 'Finaly' appears at the start of Section 4; the manuscript also has other minor typos such as 'deisgn' in the Table 5 caption.
[General] No code, environment seeds, or raw per-run scores are provided, despite the small sample sizes; releasing these artifacts would materially improve reproducibility and allow readers to verify the paired t-tests.
[Abstract, §4] The abstract's phrase 'Extensive experiments' overstates the scale of a study with three runs per model for most conditions and only one run for two models; the wording should match the reported setup.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the harness gains are measured empirically, not derived from the inputs; the prompt-optimization-on-dev-environments issue is a validity concern, not a circular step.

full rationale

This paper is an empirical systems evaluation rather than a formal derivation chain. The central claim that the modular harness improves over the unharnessed baseline is supported by paired t-tests and effect sizes computed from measured game scores, not by an equation that maps the harness definition into the result. No parameter is fit such that the reported improvement is forced by construction. The DSPy/SIMBA prompt optimization in Section 3.4 does select a best prompt P* on development environments, and the reported games appear to be the same environments, but this is a selection/overfitting and control-condition confound, not circularity: the optimization objective does not guarantee a significant improvement over the no-harness baseline in all four games, and the measured differences remain empirical outcomes. The paper does not claim a first-principles derivation, and the self-citations to GameArena and LMGame-Bench appear only in related work and are not load-bearing for the main result. Therefore no circular step can be exhibited under the required standard.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper is an empirical evaluation rather than a derivation, so there are no fitted constants in a mathematical sense. The load-bearing choices are the hand-selected score transformations, the memory window size N (unreported), and the DSPy optimization budget. The domain assumptions are that games proxy general agent tasks, that the three-module decomposition is correct, and that the statistical tests are valid despite small samples. No new physical or theoretical entities are introduced.

free parameters (3)

2048 score transformation = 10 × log2(total merged sum)
Chosen by hand in Appendix A to put scores on a continuous linear scale; affects reported magnitudes but not the paired comparisons.
memory window size N = not reported
Section 3.3 says the memory module keeps 'the past N game states and actions', but N is never specified, so an important implementation detail is missing.
DSPy optimization steps k = 20
Set to 20 in Appendix D.3; a hyperparameter of the SIMBA prompt optimization loop.

assumptions (4)

domain assumption Games are a low-barrier, high-diversity testbed for general-purpose agent abilities.
Section 1 argues that games are clean abstractions of multi-turn interactive tasks; the transfer to non-game domains is assumed, not demonstrated.
domain assumption Perception, memory, and reasoning are the core interlocking faculties of cognition.
Section 1 invokes Newell's Unified Theories of Cognition to justify decomposing the agent into these three modules.
domain assumption Paired t-tests across 10 models with 3 runs per model yield valid significance levels.
Section 4.1.2 reports t(9) p-values; normality and independence are assumed despite small samples and unequal variances across games.
domain assumption The Gymnasium reward R(s,a,s') and the chosen metric transformations faithfully reflect game progression.
Section 3.2 defines the progression and long-horizon rewards; the specific transformations are author-selected and affect the scale of measured improvements.

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Cite this review

Pith. "Pith review of General Modular Harness for LLM Agents in Multi-Turn Gaming Environments." pith.science (2026). https://pith.science/paper/WK46PZBF

@misc{pith2026250711633,
  author       = {Pith},
  title        = {Pith review of: General Modular Harness for LLM Agents in Multi-Turn Gaming Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WK46PZBF}},
  note         = {Machine review of arXiv:2507.11633}
}

read the original abstract

We introduce a modular harness design for LLM agents that composes of perception, memory, and reasoning components, enabling a single LLM or VLM backbone to tackle a wide spectrum of multi turn gaming environments without domain-specific engineering. Using classic and modern game suites as low-barrier, high-diversity testbeds, our framework provides a unified workflow for analyzing how each module affects performance across dynamic interactive settings. Extensive experiments demonstrate that the harness lifts gameplay performance consistently over un-harnessed baselines and reveals distinct contribution patterns, for example, memory dominates in long-horizon puzzles while perception is critical in vision noisy arcades. These findings highlight the effectiveness of our modular harness design in advancing general-purpose agent, given the familiarity and ubiquity of games in everyday human experience.

Figures

Figures reproduced from arXiv: 2507.11633 by the authors.

**Figure 1.** Our agent uses modular harnesses—such as perception, memory, and reasoning modules—to systematically extend a model’s game-playing capabilities, allowing the model to engage with a simulated game environment through iterative interaction loops. marily reflect an agent’s core cognitive abilities—perception, memory, and reasoning—rather than reliance on domainspecific hacks. To ground our study, we select four widely… view at source ↗

**Figure 2.** Distribution of paired score improvements (Harness – No Harness) across ten models for each game. Together, these modules enhance not only raw performance but also the discriminative power of evaluations—making them essential tools for rigorous benchmarking. 4.3. Prompt Standardization Prompt engineering is known to significantly affect LLM performance in interactive environments (Wang et al.). However, we find tha… view at source ↗

**Figure 3.** Spearman correlation among our benchmark and other benchmarks. Memory Module Prompt Template Game 2048 Memory Module system prompt: You are an analytical assistant for a 2048 AI agent. Your task is to generate a brief, insightful reflection on the game state changes and the effectiveness of recent actions.Focus on strategic insights and patterns that would help the agent make better decisions. Keep your reflections … view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

26 extracted references · 24 canonical work pages

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Building patterns - Common patterns include: (1) Snake/Zig-zag pattern: Arrange tiles in decreasing order in a zigzag; (2) Corner anchoring: Keep the highest tile in a corner and build around it. Avoid: - Getting high-value tiles stuck in the middle of the board - Creating scattered small values that block potential merges - Making moves that could lead t...

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[6]

Any strategic insights for future actions Keep your reflection under 100 words and focus only on the most important insights. C. Benchmark List for Correlation Study We use 20 publicly available benchmarks spanning seven capability categories, including factual knowledge, physics, mathematics, coding, visual reasoning, language understanding, and puzzle s...

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Build a stable structure - Keep your highest value tiles in a corner

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Maintain a clear path - Always have a direction where you can combine tiles

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[9]

Chain reactions - Set up sequences of merges that can happen in a single move
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Look ahead - Think about the consequences of your moves 2-3 steps ahead
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Establish your highest-value tile in one corner and build a descending value structure around it
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Maintain consistent movement patterns that preserve your high-value corner configuration while allowing for regular merges
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Anticipate how each potential move affects not just the immediate board state but your options 2-3 moves ahead
[15]

Create opportunities for chain reactions where multiple merges can occur in a single directional move
[16]

state →action

Implement proven arrangements such as: - Decreasing value snakes that zigzag across the board. - Corner-anchored structures with decreasing values along the edges. ### Pitfalls to Avoid Certain decisions consistently lead to board deterioration: - Allowing high-value tiles to ...

2025
[17]

**Corner Anchoring**: Position your highest-value tile in a corner and build around it
[18]

**Structural Stability**: Arrange surrounding tiles in descending order to create a stable formation
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**Maintaining Merge Paths**: Always keep at least one direction available for safe combinations
[20]

**Creating Chain Reactions**: Set up moves that trigger multiple merges in a single action
[21]

up”, “down

**Forward Planning**: Think 2-3 moves ahead to avoid grid-lock and maintain empty spaces ## Pitfalls to Avoid - Allowing high-value tiles to drift into central positions - Scattering small-value tiles that obstruct potential merges - Making moves that leave the board with no f...

2025
[22]

**Corner anchor** - Park your highest tile in one corner and build around it
[23]

**Stable structure** - Arrange surrounding tiles in descending order to protect the corner
[24]

**Clear merging path** - Keep at least one direction available for safe combinations
[25]

**Chain reactions** - Set up moves that trigger multiple merges in one swipe
[26]

Preserve empty spaces for new tiles

**Look ahead** - Plan 2–3 moves in advance to avoid grid-lock. Preserve empty spaces for new tiles. — ## Avoid - Letting high-value tiles drift into the center. - Scattering small tiles that block merges. - Moves that leave the board with no follow-up merges. — ## Previous Gam...

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Newell, A

URL https://openreview.net/forum? id=sAxVIWQOzo. Newell, A. Unified Theories of Cognition. Harvard Univer- sity Press, 1990. Paglieri, D., Cupiał, B., Coward, S., Piterbarg, U., Wolczyk, M., Khan, A., Pignatelli, E., Kuci´nski, Ł., Pinto, L., Fer- gus, R., et al. Balrog: Bench...

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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How the game state changed after the last action

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cc/paper_files/paper/2024/file/ c848b7d3adc08fcd0bf1df3101ba6728-Paper-Conference

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Patterns or issues to be aware of

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Whether the action was effective for the situation

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Building patterns - Common patterns include: (1) Snake/Zig-zag pattern: Arrange tiles in decreasing order in a zigzag; (2) Corner anchoring: Keep the highest tile in a corner and build around it. Avoid: - Getting high-value tiles stuck in the middle of the board - Creating scattered small values that block potential merges - Making moves that could lead t...

work page 2025

[6] [6]

Any strategic insights for future actions Keep your reflection under 100 words and focus only on the most important insights. C. Benchmark List for Correlation Study We use 20 publicly available benchmarks spanning seven capability categories, including factual knowledge, physics, mathematics, coding, visual reasoning, language understanding, and puzzle s...

work page 2025

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Build a stable structure - Keep your highest value tiles in a corner

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Maintain a clear path - Always have a direction where you can combine tiles

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