pith. sign in

arxiv: 2605.22238 · v1 · pith:IPBCX2E2new · submitted 2026-05-21 · 💻 cs.AI

Evaluating Large Language Models as Live Strategic Agents: Provider Performance, Hybrid Decomposition, and Operational Gaps in Timed Risk Play

H. C. Ekne This is my paper

Pith reviewed 2026-05-22 05:32 UTC · model grok-4.3

classification 💻 cs.AI
keywords large language modelsstrategic agentsRisk gamehybrid evaluationplanning and executionobjective trackinglive agent performance
0
0 comments X

The pith

Gemini wins 20 of 32 timed Risk games against other models, but planner performance equalizes when execution is fixed to one scaffold.

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

The paper places large language models inside repeated planning and execution loops inside a timed multi-phase Risk game that has explicit victory targets and failure modes. It runs a 32-game championship across providers under frozen rules and finds that one model wins far more often than chance would predict. A second set of games holds execution constant on a single cheaper model while swapping only the planner; here the win rates become statistically indistinguishable. The work then examines saved traces to link the full-system advantage to more frequent references to the terminal objective and higher conversion of turns into long conquest sequences. These patterns matter because deployed agents must operate under time limits, formatting rules, and repeated cycles rather than answering isolated questions.

Core claim

In a replicated 32-game cross-provider championship under frozen rules, gemini-3.1-pro-preview won 20 of 32 games against gpt-5.1, claude-opus-4-7, and kimi-k2.6, and the pooled winner distribution differs strongly from an equal-strength null (p approx 1.5 x 10^-5). Under the hybrid design with standardized execution on a Gemini Flash scaffold, a pooled 32-game planner bakeoff is consistent with near-equality (p approx 0.821). Analysis of saved planning and execution traces shows Gemini refers to the terminal objective far more often than the other models and increases that focus as victory approaches, while also converting more turns into deep conquest chains even though it is not the clean

What carries the argument

The hybrid decomposition that separates planning from execution by standardizing the executor on a single Gemini Flash scaffold while varying only the planner model.

If this is right

  • Provider differences in full end-to-end play arise mainly from system-level behavior rather than from planning skill in isolation.
  • Gemini refers to the terminal objective more frequently than competitors and increases that focus as victory nears.
  • Gemini converts more turns into deep conquest chains despite lower runtime cleanliness in some cases.
  • Live-agent performance depends on objective tracking, execution conversion, cost, and runtime reliability.
  • LLMs should be evaluated as components inside bounded workflows rather than as isolated benchmark respondents.

Where Pith is reading between the lines

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

  • Pairing a strong planner with a reliable executor from a different provider could be a practical way to reduce cost while preserving performance.
  • The same planning-versus-execution split could be tested in other constrained strategy domains such as resource-allocation games or turn-based logistics tasks.
  • If planner-equality holds only under a narrow choice of executor, then full-system testing remains necessary for any production deployment.
  • Small differences in how often a model mentions the victory condition may compound across many turns into large win-rate gaps.

Load-bearing premise

That standardizing execution on one cheaper Gemini Flash scaffold isolates planning performance without introducing systematic bias from model-specific execution styles, compatibility differences, or interactions between planner and executor.

What would settle it

Re-running the 32-game planner bakeoff while standardizing execution on a different scaffold, such as one based on GPT or Claude, and checking whether the near-equality in win rates remains or whether provider differences reappear.

Figures

Figures reproduced from arXiv: 2605.22238 by H. C. Ekne.

Figure 1
Figure 1. Figure 1: Full-stack provider championship. Pooled wins over 32 games under the frozen full-stack provider setup; Gemini is the only stack with a large replicated lead. This result is stable across two independent blocks. In this live-agent harness, Gemini 3.1 Pro Preview was the strongest tested full-stack provider representative. We should keep the claim narrow. The result applies to this bounded strategic environ… view at source ↗
Figure 2
Figure 2. Figure 2: Kimi anchor experiments. Kimi is competitive with older strong closed-model tiers but clearly below the current Gemini 3.1 frontier result. Google announced Gemini 2.5 Pro on March 25, 2025, with general availability on June 17, 2025. Moonshot announced Kimi K2.6 on April 21, 2026. So depending on which Gemini release milestone one uses, Kimi 2.6 arrives roughly 10–13 months later. Yet in this live-agent e… view at source ↗
Figure 3
Figure 3. Figure 3: Gemini execution cost gate. The hybrid of Gemini 3.1 planning plus Gemini 3 Flash execution preserves most of the strength while cutting cost materially. This result changes the practical benchmark choice. The best benchmark agent may come from a hybrid design in which a stronger model plans and a cheaper faster model executes. 10 Planner Rankings Shrink Once Execution Is Standardized After locking a share… view at source ↗
Figure 4
Figure 4. Figure 4: Full-stack spread versus planner-only spread. Once execution is standardized to Gemini Flash, the provider spread compresses sharply. Once execution was fixed to the same cheap Gemini Flash layer, the provider spread compressed sharply. The pooled 32-game planner result was consistent with near-equality, with an omnibus 7 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Goal-directedness trace analysis. Gemini references the terminal objective far more often than the other providers and increases that focus as it approaches victory. This pattern does not come from verbosity alone. Gemini remains the clear outlier even after normalizing by plan length. Its share of plans with explicit goal language rises from 39.8% when it holds 0–9 territories, to 69.9% in the 10–19 range… view at source ↗
Figure 6
Figure 6. Figure 6: Execution chain depth distribution. Gemini produces deep conquest chains more often than the rest of the provider field. Gemini produced 6 or more successful conquests on 38.7% of its turns. Claude reached that mark on 28.9% of turns, Kimi on 26.7%, and GPT-5.1 on 23.4%. Gemini also had the best midgame territory conversion. When it started a turn with 10–19 territories, it gained 5.363 territories per tur… view at source ↗
Figure 7
Figure 7. Figure 7: Execution profile summary. Gemini is not the cleanest runtime. It still combines acceptable reliability with the strongest midgame conversion. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

Static benchmarks capture only part of how large language models behave in practice. Real systems place models inside repeated loops with time limits, formatting constraints, and failure modes. We study this setting in a timed multi-phase Risk environment with explicit victory targets and repeated planning and execution cycles. In a replicated 32-game cross-provider championship under frozen rules, gemini-3.1-pro-preview won 20 of 32 games against gpt-5.1, claude-opus-4-7, and kimi-k2.6, and the pooled winner distribution differs strongly from an equal-strength null (p approx 1.5 x 10^-5). We then separate planning from execution by standardizing execution on a cheaper Gemini Flash scaffold. Under this design, a pooled 32-game planner bakeoff is consistent with near-equality (p approx 0.821), which indicates that much of the earlier provider spread came from end-to-end system behavior rather than planning alone. To study mechanism, we analyze saved planning and execution traces from the provider championship. Gemini refers to the terminal objective far more often than the other models and increases that focus as victory approaches. Gemini also converts more turns into deep conquest chains, even though it is not the cleanest runtime. These results show that live-agent performance depends on objective tracking, execution conversion, cost, and runtime reliability, and they support evaluating LLMs as components in bounded workflows rather than as isolated benchmark respondents.

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 / 3 minor

Summary. The manuscript evaluates LLMs as live strategic agents in a timed multi-phase Risk game with victory targets and repeated planning-execution cycles. It reports results from a replicated 32-game cross-provider championship under frozen rules, where gemini-3.1-pro-preview wins 20 of 32 games against gpt-5.1, claude-opus-4-7, and kimi-k2.6, with the pooled winner distribution differing strongly from an equal-strength null (p ≈ 1.5 × 10^{-5}). A hybrid design then standardizes execution on a Gemini Flash scaffold; the resulting 32-game planner bakeoff is consistent with near-equality (p ≈ 0.821). Trace analysis shows Gemini references the terminal objective more often (increasing near victory) and converts more turns into deep conquest chains. The paper concludes that live-agent performance depends on objective tracking, execution conversion, cost, and runtime reliability, favoring workflow-based evaluation over isolated benchmarks.

Significance. If the decomposition is robust, the work is significant for shifting LLM evaluation toward integrated, time-bounded strategic workflows rather than static benchmarks. Strengths include the replicated 32-game design with external statistical tests, direct game-outcome metrics, and mechanistic trace analysis that identifies concrete behavioral differences (objective focus and conquest-chain conversion). These elements provide falsifiable, component-level insights into operational gaps and support treating LLMs as modular agents in bounded systems.

major comments (2)
  1. [§3.2 (Hybrid Decomposition)] §3.2 (Hybrid Decomposition): The claim that standardizing execution on a single Gemini Flash scaffold isolates planning performance (yielding p ≈ 0.821 and attributing original gaps to execution) is load-bearing but rests on an untested assumption. No cross-scaffold control or planner-executor compatibility metrics (e.g., format rejection rates by planner provider) are reported; differential handling of output styles could artifactually flatten differences. This directly affects the central inference that planning abilities are roughly equal.
  2. [§2 (Experimental Protocol)] §2 (Experimental Protocol): The abstract and results report p-values and win counts but omit details on game-rule implementation, randomization procedures, exact time limits, error-recovery logic, and the precise statistical test (e.g., multinomial vs. chi-square) plus tie/incomplete-game handling. These omissions undermine independent verification of both the championship and bakeoff claims.
minor comments (3)
  1. [Abstract] Abstract: Model identifiers ('gemini-3.1-pro-preview', 'gpt-5.1', 'claude-opus-4-7', 'kimi-k2.6') should include exact API versions, snapshot dates, or parameter counts for reproducibility.
  2. [Trace Analysis] Trace Analysis: The term 'deep conquest chains' lacks an operational definition or illustrative trace excerpt; a short example would clarify the metric.
  3. [Overall] Overall: A summary table reporting win rates, average cost per game, and runtime reliability broken down by condition and provider would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for highlighting key areas for improvement in our manuscript on evaluating LLMs as live strategic agents. We address the major comments point-by-point below, indicating where revisions will be made to enhance the manuscript's clarity, reproducibility, and robustness of conclusions.

read point-by-point responses

  1. Referee: §3.2 (Hybrid Decomposition): The claim that standardizing execution on a single Gemini Flash scaffold isolates planning performance (yielding p ≈ 0.821 and attributing original gaps to execution) is load-bearing but rests on an untested assumption. No cross-scaffold control or planner-executor compatibility metrics (e.g., format rejection rates by planner provider) are reported; differential handling of output styles could artifactually flatten differences. This directly affects the central inference that planning abilities are roughly equal.

    Authors: We recognize that the hybrid decomposition relies on the assumption that the standardized executor handles outputs from different planners equivalently. We did not include explicit cross-scaffold controls or compatibility metrics in the original submission. In the revision, we will add a discussion of this assumption in §3.2, including any available data on output format handling from the traces, and acknowledge the limitation that a full cross-scaffold experiment was not performed. This will clarify the strength of the inference that planning abilities are roughly equal while noting that execution differences likely contributed to the original performance gaps. revision: partial

  2. Referee: §2 (Experimental Protocol): The abstract and results report p-values and win counts but omit details on game-rule implementation, randomization procedures, exact time limits, error-recovery logic, and the precise statistical test (e.g., multinomial vs. chi-square) plus tie/incomplete-game handling. These omissions undermine independent verification of both the championship and bakeoff claims.

    Authors: We agree that additional details on the experimental protocol are necessary for full reproducibility. In the revised manuscript, we will expand §2 to explicitly detail the game-rule implementation, including the exact modifications to standard Risk rules, the randomization procedures, the precise time limits for each phase, the error-recovery logic for API failures, the statistical test employed, and the handling of any incomplete games or ties. We will also update the abstract to reference these elements briefly. This will allow independent verification of the reported p-values and win counts. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical outcomes and statistical tests stand independently

full rationale

The paper reports direct experimental results from 32-game tournaments, win counts, and p-value calculations on observed distributions. The hybrid planner bakeoff compares game outcomes under a fixed Gemini Flash executor scaffold. No equations, fitted parameters, or derivations are present that reduce reported differences to quantities defined by the inputs themselves. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The central inference (planning near-equality when execution is standardized) rests on external game traces and null-hypothesis testing rather than any self-referential construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the chosen Risk rules and victory conditions constitute a valid proxy for live strategic agent behavior and that the hybrid scaffold cleanly separates planning from execution without confounding interactions.

axioms (1)
  • domain assumption The Risk game with explicit victory targets and repeated planning-execution cycles under time limits fairly tests the relevant dimensions of LLM agent performance.
    Invoked by the choice of environment and the interpretation of win rates and trace statistics as evidence about objective tracking and execution conversion.

pith-pipeline@v0.9.0 · 5800 in / 1480 out tokens · 55821 ms · 2026-05-22T05:32:40.958552+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

11 extracted references · 11 canonical work pages · 6 internal anchors

  1. [1]

    Science , volume =

    Human-level play in the game of Diplomacy by combining language models with strategic reasoning , author =. Science , volume =. 2022 , doi =

    work page 2022

  2. [9]

    2024 , url =

    Yao, Shunyu and Shinn, Noah and Razavi, Pedram and Narasimhan, Karthik , journal =. 2024 , url =

    work page 2024

  3. [10]

    Human-level play in the game of diplomacy by combining language models with strategic reasoning

    Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, et al. Human-level play in the game of diplomacy by combining language models with strategic reasoning. Science, 378 0 (6624): 0 1067--1074, 2022. doi:10.1126/science.ade9097

    work page doi:10.1126/science.ade9097 2022

  4. [11]

    Gamebench: Evaluating strategic reasoning abilities of llm agents.arXiv preprint arXiv:2406.06613, 2024

    Anthony Costarelli, Mat Allen, Roman Hauksson, Grace Sodunke, Suhas Hariharan, Carlson Cheng, Wenjie Li, Joshua Clymer, and Arjun Yadav. Gamebench: Evaluating strategic reasoning abilities of llm agents. arXiv preprint arXiv:2406.06613, 2024. URL https://arxiv.org/abs/2406.06613

    work page arXiv 2024

  5. [12]

    Kanishk Gandhi, Dorsa Sadigh, and Noah D. Goodman. Strategic reasoning with language models. arXiv preprint arXiv:2305.19165, 2023. URL https://arxiv.org/abs/2305.19165

    work page arXiv 2023

  6. [13]

    Measuring Massive Multitask Language Understanding

    Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020. URL https://arxiv.org/abs/2009.03300

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  7. [14]

    Holistic Evaluation of Language Models

    Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, et al. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110, 2022. URL https://arxiv.org/abs/2211.09110

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  8. [15]

    AgentBench: Evaluating LLMs as Agents

    Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, et al. Agentbench: Evaluating llms as agents. arXiv preprint arXiv:2308.03688, 2023. URL https://arxiv.org/abs/2308.03688

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  9. [16]

    GAIA: a benchmark for General AI Assistants

    Gr \'e goire Mialon, Cl \'e mentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, and Thomas Scialom. GAIA : a benchmark for general ai assistants. arXiv preprint arXiv:2311.12983, 2023. URL https://arxiv.org/abs/2311.12983

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  10. [17]

    Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

    Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615, 2022. URL https://arxiv.org/abs/2206.04615

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  11. [18]

    $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

    Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik Narasimhan. -bench: A benchmark for tool-agent-user interaction in real-world domains. arXiv preprint arXiv:2406.12045, 2024. URL https://arxiv.org/abs/2406.12045

    work page internal anchor Pith review Pith/arXiv arXiv 2024