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arxiv: 2606.27406 · v1 · pith:2YFTWZ3Unew · submitted 2026-06-25 · 💻 cs.SE · cs.AI

Towards Evaluation of Implicit Software World Models in Coding LLMs

Egor Bogomolov , Yaroslav Zharov This is my paper

Pith reviewed 2026-06-29 01:56 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords software world modelscoding LLMsexecution predictionSWE-benchruntime resourcesprofiler outputsimplicit models
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The pith

Coding LLMs show modest performance predicting peak memory, runtime, and profiler outputs on real tasks.

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

Software engineering requires reasoning about how programs actually run, not merely how their source appears on the page. The paper shifts evaluation from control-flow checks to resource predictions, asking models to forecast peak memory, wall-clock time, and ranked profiler outputs at method and line levels. Tasks are drawn from SWE-bench Verified to keep the setting close to genuine engineering work. Frontier models display only modest accuracy and brittle results across these signals.

Core claim

All tested models, frontier ones included, show modest performance and brittle behaviour when asked to predict peak memory, wall-clock time, and ranked profiler outputs, suggesting a notable lack of understanding of how software is executed as opposed to how its source code is written.

What carries the argument

Augmented prediction tasks on SWE-bench Verified that require models to output peak memory, wall-clock time, and ranked profiler data at method and line granularity alongside test outcomes.

If this is right

  • Benchmarks limited to test outcomes and exception classes miss substantial aspects of runtime behavior that matter for engineering.
  • Brittle performance implies current models remain sensitive to small changes in code or input when estimating execution cost.
  • Progress toward reliable AI software agents may require training signals that explicitly target execution dynamics rather than syntax alone.

Where Pith is reading between the lines

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

  • If the resource-prediction gap persists, scaling model size alone is unlikely to close it without new data or objectives focused on execution traces.
  • The same evaluation axis could be applied to other languages or to non-functional properties such as energy consumption to test generality.

Load-bearing premise

Accurate forecasts of memory use, execution time, and profiler rankings serve as a valid proxy for possessing an implicit model of software execution that supports engineering reasoning.

What would settle it

A model that scores high on resource predictions yet still fails at downstream software engineering tasks such as bug fixing on the same SWE-bench instances would indicate the proxy does not capture the intended capability.

read the original abstract

Software engineering, whether performed by humans or by AI agents, requires reasoning about how software behaves. We call the internal model that supports such reasoning the software world model, and view current code-execution benchmarks as covering one well-studied slice of it -- control flow. In this paper, we take a step toward a broader evaluation by shifting the observable axis to execution resources: alongside test outcome and exception class, we predict peak memory, wall-clock time, and ranked profiler outputs at method and line granularity. We use SWE-bench Verified as the source of data to hold the test close to real-world software engineering tasks. All tested models, frontier ones included, show modest performance and brittle behaviour, suggesting a notable lack of understanding of how software is executed, as opposed to how its source code is written.

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 / 2 minor

Summary. The paper claims that coding LLMs, including frontier models, exhibit only modest performance and brittle behavior when tasked with predicting not only test outcomes and exception classes but also peak memory usage, wall-clock time, and ranked profiler outputs (at method and line granularity) on tasks drawn from SWE-bench Verified. The authors interpret these results as evidence that current models lack an implicit 'software world model' for reasoning about execution behavior, as opposed to merely understanding source code syntax and control flow.

Significance. If the chosen observables are shown to be valid proxies for the internal models required to support software-engineering reasoning (e.g., bug localization, refactoring, or control-flow analysis), the work would usefully expand the evaluation axis beyond existing control-flow benchmarks and highlight a concrete capability gap. The use of real-world tasks from SWE-bench Verified is a positive step toward ecological validity. However, the manuscript provides no empirical link between resource-prediction accuracy and downstream SE-task performance, so the significance remains conditional on that untested assumption.

major comments (2)
  1. [Abstract] Abstract (and §1): The central interpretive claim—that modest performance on peak-memory, wall-clock, and profiler-output prediction demonstrates a 'notable lack of understanding of how software is executed'—rests on the unexamined assumption that these particular observables constitute a sufficient proxy for an implicit world model capable of supporting SE reasoning. No correlation, ablation, or downstream-task experiment is reported that would establish this link or rule out the possibility that models could perform well on SE tasks without accurate resource prediction (or vice versa). This assumption is load-bearing for the paper's conclusion.
  2. [Methods] Methods / Experimental Setup (assumed §3–4): The manuscript does not report the precise prediction formulation (regression vs. ranking loss), the exact metrics and error bars used, the data splits, or the baselines against which 'modest performance' is judged. Without these details it is impossible to assess whether the reported brittleness is an artifact of the evaluation protocol rather than a genuine limitation of the models' internal representations.
minor comments (2)
  1. [Introduction] The shift from control-flow benchmarks is noted but not justified with a concrete argument for why resource metrics capture a distinct and relevant slice of the world model; a short paragraph clarifying this choice would improve clarity.
  2. [Abstract] Notation for 'ranked profiler outputs' is introduced without an explicit definition or example; adding a small illustrative table would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and §1): The central interpretive claim—that modest performance on peak-memory, wall-clock, and profiler-output prediction demonstrates a 'notable lack of understanding of how software is executed'—rests on the unexamined assumption that these particular observables constitute a sufficient proxy for an implicit world model capable of supporting SE reasoning. No correlation, ablation, or downstream-task experiment is reported that would establish this link or rule out the possibility that models could perform well on SE tasks without accurate resource prediction (or vice versa). This assumption is load-bearing for the paper's conclusion.

    Authors: We agree the link to downstream SE-task performance is untested in the current work. The observables were chosen as direct probes of runtime behavior beyond control flow, but we acknowledge this does not yet demonstrate necessity for SE reasoning. In revision we will (a) soften the abstract and §1 wording from 'demonstrates a notable lack' to 'suggests a potential gap in modeling execution dynamics' and (b) add an explicit limitations paragraph stating that future work must correlate these metrics with bug localization, refactoring, etc. No new experiments are feasible at this stage, but the framing change addresses the load-bearing assumption. revision: partial

  2. Referee: [Methods] Methods / Experimental Setup (assumed §3–4): The manuscript does not report the precise prediction formulation (regression vs. ranking loss), the exact metrics and error bars used, the data splits, or the baselines against which 'modest performance' is judged. Without these details it is impossible to assess whether the reported brittleness is an artifact of the evaluation protocol rather than a genuine limitation of the models' internal representations.

    Authors: We accept this criticism. The current draft omits these protocol details. In the revised manuscript we will insert a new subsection (likely §3.3) that specifies: (i) regression heads with MSE loss for memory/time and pairwise ranking loss for profiler outputs; (ii) metrics including MAE, Spearman rank correlation, and top-k accuracy with 95% bootstrap CIs over 5 seeds; (iii) train/test splits drawn from SWE-bench Verified with no task leakage; and (iv) baselines consisting of random predictors, static heuristic estimators, and a fine-tuned smaller model. This will allow readers to judge whether brittleness is protocol-dependent. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark study with no derivations or self-referential fits

full rationale

The paper is an empirical evaluation of LLMs on resource prediction tasks drawn from SWE-bench Verified. It introduces the term 'software world model' as the internal model supporting execution reasoning and contrasts it with existing control-flow benchmarks, then reports model performance on new observables (peak memory, wall-clock time, profiler outputs). No equations, parameter fits, or derivations appear in the provided text. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The central claim rests on experimental outcomes rather than any reduction of a 'prediction' to its own inputs by construction. The proxy-validity concern raised in the skeptic note is a question of external validity, not circularity in the paper's own chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no free parameters, axioms, or invented entities are described or extractable.

pith-pipeline@v0.9.1-grok · 5661 in / 1016 out tokens · 19933 ms · 2026-06-29T01:56:12.953690+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

31 extracted references · 3 canonical work pages

  1. [1]

    and Kaiser,

    Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser,. Advances in Neural Information Processing Systems 30 , title =. 2017 , pages =

    2017

  2. [2]

    arXiv preprint arXiv:2512.14917 , year=

    Evaluating Code Reasoning Abilities of Large Language Models Under Real-World Settings , author=. arXiv preprint arXiv:2512.14917 , year=

    Pith/arXiv arXiv

  3. [3]

    Proceedings of the IEEE/ACM 47th International Conference on Software Engineering , pages =

    Chen, Junkai and Pan, Zhiyuan and Hu, Xing and Li, Zhenhao and Li, Ge and Xia, Xin , title =. Proceedings of the IEEE/ACM 47th International Conference on Software Engineering , pages =. 2025 , isbn =

    2025

  4. [4]

    2025 , eprint=

    CWM: An Open-Weights LLM for Research on Code Generation with World Models , author=. 2025 , eprint=

    2025

  5. [5]

    2024 , editor =

    Gu, Alex and Roziere, Baptiste and Leather, Hugh James and Solar-Lezama, Armando and Synnaeve, Gabriel and Wang, Sida , booktitle =. 2024 , editor =

    2024

  6. [6]

    2025 , eprint=

    ThrowBench: Benchmarking LLMs by Predicting Runtime Exceptions , author=. 2025 , eprint=

    2025

  7. [7]

    2025 , eprint=

    CodeMind: Evaluating Large Language Models for Code Reasoning , author=. 2025 , eprint=

    2025

  8. [8]

    2024 , url=

    Carlos E Jimenez and John Yang and Alexander Wettig and Shunyu Yao and Kexin Pei and Ofir Press and Karthik R Narasimhan , booktitle=. 2024 , url=

    2024

  9. [9]

    2026 , eprint=

    Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces , author=. 2026 , eprint=

    2026

  10. [10]

    2025 , eprint=

    BigO(Bench) -- Can LLMs Generate Code with Controlled Time and Space Complexity? , author=. 2025 , eprint=

    2025

  11. [11]

    2026 , howpublished =

    2026

  12. [12]

    2023 , eprint=

    ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation , author=. 2023 , eprint=

    2023

  13. [13]

    2023 , eprint=

    RunBugRun -- An Executable Dataset for Automated Program Repair , author=. 2023 , eprint=

    2023

  14. [14]

    2107.03374 , archivePrefix =

    Chen, Mark and Tworek, Jerry and Jun, Heewoo and Yuan, Qiming and Pinto, Henrique Ponde de Oliveira and Kaplan, Jared and Edwards, Harri and Burda, Yuri and Joseph, Nicholas and Brockman, Greg and others , year =. 2107.03374 , archivePrefix =

    Pith/arXiv arXiv

  15. [15]

    and Cardie, Claire and Gall

    Zhao, Wenting and Jiang, Nan and Lee, Celine and Chiu, Justin T. and Cardie, Claire and Gall. 2024 , eprint =

    2024

  16. [16]

    2108.07732 , archivePrefix =

    Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and Sutton, Charles , year =. 2108.07732 , archivePrefix =

    Pith/arXiv arXiv

  17. [17]

    2025 , howpublished =

    2025

  18. [18]

    AVATAR : A Parallel Corpus for J ava-Python Program Translation

    Ahmad, Wasi Uddin and Tushar, Md Golam Rahman and Chakraborty, Saikat and Chang, Kai-Wei. AVATAR : A Parallel Corpus for J ava-Python Program Translation. Findings of the Association for Computational Linguistics: ACL 2023. 2023. doi:10.18653/v1/2023.findings-acl.143

    work page doi:10.18653/v1/2023.findings-acl.143 2023

  19. [19]

    , month = oct, year =

    Lam, Man Ho and Wang, Chaozheng and Huang, Jen-tse and Lyu, Michael R. , month = oct, year =. doi:10.48550/arXiv.2504.14119 , abstract =

    work page doi:10.48550/arxiv.2504.14119

  20. [20]

    Nature , author =

    Faster sorting algorithms discovered using deep reinforcement learning , volume =. Nature , author =. 2023 , pages =. doi:10.1038/s41586-023-06004-9 , abstract =

    work page doi:10.1038/s41586-023-06004-9 2023

  21. [21]

    and Rajamani, Sriram , year =

    Agrawal, Lakshya and Kanade, Aditya and Goyal, Navin and Lahiri, Shuvendu K. and Rajamani, Sriram , year =. Monitor-. Thirty-seventh

  22. [22]

    and Kanade, Aditya and Goyal, Navin and Lahiri, Shuvendu K

    Agrawal, Lakshya A. and Kanade, Aditya and Goyal, Navin and Lahiri, Shuvendu K. and Rajamani, Sriram K. , month = nov, year =. Guiding

  23. [23]

    The elements of statistical learning: data mining, inference and prediction , url =

    Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome , year =. The elements of statistical learning: data mining, inference and prediction , url =

  24. [24]

    arXiv:1902.04601 [cs, stat] , author =

    Contrastive. arXiv:1902.04601 [cs, stat] , author =. 2019 , note =

    Pith/arXiv arXiv 1902

  25. [25]

    arXiv:1907.12934 [cs, stat] , author =

    Weakly. arXiv:1907.12934 [cs, stat] , author =. 2019 , note =

    arXiv 1907

  26. [26]

    arXiv:1907.13590 [cs, eess] , author =

    Unsupervised. arXiv:1907.13590 [cs, eess] , author =. 2019 , note =

    arXiv 1907

  27. [27]

    arXiv:1502.02734 [cs] , author =

    Weakly- and. arXiv:1502.02734 [cs] , author =. 2015 , note =

    Pith/arXiv arXiv 2015

  28. [28]

    arXiv:1907.10473 [cs] , author =

    Switchable. arXiv:1907.10473 [cs] , author =. 2019 , note =

    Pith/arXiv arXiv 1907

  29. [29]

    arXiv:1905.05055 [cs] , author =

    Object. arXiv:1905.05055 [cs] , author =. 2019 , note =

    arXiv 1905

  30. [30]

    arXiv:1702.05464 [cs] , author =

    Adversarial. arXiv:1702.05464 [cs] , author =. 2017 , note =

    Pith/arXiv arXiv 2017

  31. [31]

    arXiv:1706.05587 [cs] , author =

    Rethinking. arXiv:1706.05587 [cs] , author =. 2017 , note =

    Pith/arXiv arXiv 2017