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arxiv: 2605.19782 · v1 · pith:B5UXBQDEnew · submitted 2026-05-19 · 💻 cs.AI · cs.LG· cs.SE

Prior Knowledge or Search? A Study of LLM Agents in Hardware-Aware Code Optimization

Dmitry Redko (1) , Albert Fazlyev (2) , Konstantin Sozykin (1) , Maria Ivanova (3 , 1) , Evgeny Burnaev (1) , Egor Shvetsov (1) ((1) Applied AI Institute , (2) AI Talent Hub
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ITMO University (3) YSDA)
This is my paper

Pith reviewed 2026-05-20 06:07 UTC · model grok-4.3

classification 💻 cs.AI cs.LGcs.SE
keywords LLM agentscode optimizationhardware-aware optimizationCUDATVM IRpretrained priorskernel generationfeedback loops
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The pith

LLMs in hardware code optimization depend on pretrained priors rather than feedback or agentic structure.

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

The paper tests if LLM agents improve code optimization mainly by using feedback in a propose-evaluate-revise loop or by drawing on knowledge from pretraining. Controlled experiments show that in black-box settings the models optimize greedily. In zero-shot kernel generation they produce the same parameters no matter what size is specified and fail on uncommon sizes. Under iterative feedback CUDA kernels improve steadily while TVM IR ones get worse, leading to the conclusion that success comes from priors more than from new information or agent design.

Core claim

The authors establish that LLM agents in hardware-aware code optimization tasks highly depend on pretrained priors rather than provided feedback or agentic structure. This is shown by their greedy behavior in black-box optimization, their convergence to identical kernel parameters regardless of explicit size instructions or temperature, their sharp performance drop on uncommon sizes, and their monotonic improvement under feedback in high-density CUDA contrasted with active degradation in low-density TVM IR.

What carries the argument

The propose-evaluate-revise loop applied to kernel optimization, tested with and without feedback across CUDA and TVM IR representations and with varying size information.

If this is right

  • In black-box optimization LLMs act as greedy optimizers rather than performing broad search.
  • Models converge to the same kernel parameters regardless of input size or temperature setting.
  • Kernel optimization performance degrades sharply for sizes uncommon in training data.
  • Iterative feedback produces monotonic improvement in high-density languages like CUDA but degradation in low-density ones like TVM IR.

Where Pith is reading between the lines

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

  • Improving results may require expanding training data to cover more kernel sizes and low-density representations instead of adding agent complexity.
  • The same pattern of prior dominance could appear in other LLM-driven technical search tasks such as algorithm design.
  • Hybrid systems that pair LLMs with conventional search or symbolic optimizers could reduce the degradation observed in unfamiliar languages.

Load-bearing premise

The performance differences between CUDA and TVM IR and the lack of effect from explicit size information result from reliance on pretrained priors rather than prompt design, model capability limits, or other experimental variables.

What would settle it

An experiment in which models generate distinct kernel parameters for different input sizes after size information is made more prominent in the prompt or after testing a model with no prior exposure to CUDA or TVM code would challenge the central claim.

Figures

Figures reproduced from arXiv: 2605.19782 by 1), (2) AI Talent Hub, (3) YSDA), Albert Fazlyev (2), Dmitry Redko (1), Egor Shvetsov (1) ((1) Applied AI Institute, Evgeny Burnaev (1), ITMO University, Konstantin Sozykin (1), Maria Ivanova (3.

Figure 1
Figure 1. Figure 1: An example of optimization traces. An LLM often follows a greedy, line-like trajectory that either converges [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Columns show the two backbones, rows show the standard prompt and BO-pretending prompt for LLMs. Steps 1-10 are uniform-random samples (white labeled circles), the best of these is highlighted in red. The green segment marks future LLM-proposed steps 11-15 and the cyan star is the true minimum. LLMs next proposal is always close to the best in history point. emitted. Full prompt templates are in Appendix F… view at source ↗
Figure 4
Figure 4. Figure 4: Zero-shot pass-rate and dominant kernel parameters across shape grids for three kernels ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Token counts (cl100k tokenizer) for GPU-related [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Feedback Loop validity and acceleration outcomes for CUDA and TVM IR generation. Bars show nested [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Iterative improvement relative to the first iteration under two agent architectures (Feedback Loop vs. Sampling [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Pipeline diagrams for the two agentic architectures. [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Expanded Feedback Loop validity and speedup under CUDA and TVM IR generation across all five feedback [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Extra-small size sweeps comparing TVM MetaSchedule and [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Small-size sweeps comparing TVM MetaSchedule and [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Medium-size sweeps comparing TVM MetaSchedule and [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Large-size sweeps comparing TVM MetaSchedule and [PITH_FULL_IMAGE:figures/full_fig_p023_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Extra-large size sweeps comparing TVM MetaSchedule and [PITH_FULL_IMAGE:figures/full_fig_p024_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: BBOB task bbob_f01_sphere_i1 [PITH_FULL_IMAGE:figures/full_fig_p024_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: BBOB task bbob_f03_rastrigin_i1 [PITH_FULL_IMAGE:figures/full_fig_p025_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: BBOB task bbob_f04_bueche_rastrigin_i1 [PITH_FULL_IMAGE:figures/full_fig_p025_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: BBOB task bbob_f06_attractive_sector_i1 [PITH_FULL_IMAGE:figures/full_fig_p026_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: BBOB task bbob_f09_rosenbrock_rotated_i1 [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: BBOB task bbob_f10_ellipsoid_rotated_i1 [PITH_FULL_IMAGE:figures/full_fig_p027_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: BBOB task bbob_f11_discus_i1 [PITH_FULL_IMAGE:figures/full_fig_p027_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: BBOB task bbob_f13_sharp_ridge_i1 [PITH_FULL_IMAGE:figures/full_fig_p028_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: BBOB task bbob_f14_different_powers_i1 [PITH_FULL_IMAGE:figures/full_fig_p028_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: BBOB task bbob_f15_rastrigin_rotated_i1 [PITH_FULL_IMAGE:figures/full_fig_p029_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: BBOB task bbob_f17_schaffers_f7_i1 [PITH_FULL_IMAGE:figures/full_fig_p029_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: BBOB task bbob_f18_schaffers_f7_ill_i1 [PITH_FULL_IMAGE:figures/full_fig_p030_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: BBOB task bbob_f19_griewank_rosenbrock_i1 [PITH_FULL_IMAGE:figures/full_fig_p030_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: BBOB task bbob_f20_schwefel_i1 [PITH_FULL_IMAGE:figures/full_fig_p031_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: BBOB task bbob_f21_gallagher_gaussian101_i1 [PITH_FULL_IMAGE:figures/full_fig_p031_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: BBOB task bbob_f22_gallagher_gaussian21_i1 [PITH_FULL_IMAGE:figures/full_fig_p032_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: BBOB task bbob_f24_lunacek_bi_rastrigin_i1 [PITH_FULL_IMAGE:figures/full_fig_p032_32.png] view at source ↗
read the original abstract

LLM discovery and optimization systems are increasingly applied across domains, implementing a common propose-evaluate-revise loop. Such optimization or discovery progresses via context conditioning on received feedback from an environment. However, as modern LLM agents are increasingly complex in their structure, it is difficult to evaluate which components contribute the most, and when and how this exploration may fail. We answer these questions through three controlled experiments. Our findings: (1) In pure black-box optimization, LLMs act as greedy optimizers. (2) In zero-shot kernel generation, providing explicit input-size information has no measurable effect, models converge to the same kernel parameters regardless of size or temperature, as though the size instruction were invisible. Moreover, when tasked to perform kernel optimization for uncommon kernel sizes, performance sharply degrades regardless of the language used. (3) In feedback-loop kernel optimization, CUDA improves monotonically under iterative feedback, while TVM IR actively degrades, which demonstrates that kernel optimization degrades when models operate with low-density language. Our results conclude that LLMs in code optimization tasks highly depend on pretrained priors rather than provided feedback or agentic structure.

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 reports three controlled experiments on LLM agents performing hardware-aware code optimization via propose-evaluate-revise loops. Experiment 1 examines black-box optimization behavior; Experiment 2 tests zero-shot kernel generation with and without explicit size information across common and uncommon sizes; Experiment 3 compares iterative feedback-driven optimization in CUDA versus low-density TVM IR. The central claim is that LLMs rely primarily on pretrained priors rather than feedback or agentic structure, evidenced by greedy optimization, size-insensitivity, convergence independent of temperature, and monotonic improvement in CUDA contrasted with degradation in TVM IR.

Significance. If the empirical patterns hold after addressing confounds, the work offers a useful diagnostic on the limits of current LLM agents in code optimization, showing that added agentic complexity and feedback may not overcome reliance on pretraining. This has direct implications for designing more effective LLM-based systems in hardware-aware tasks and for understanding when context conditioning succeeds or fails in low-density languages.

major comments (2)
  1. [abstract and §3] The interpretation in the abstract and §3 (zero-shot kernel generation) that size information has 'no measurable effect' and is 'invisible' attributes this to pretrained priors, yet the experiments do not include controls that isolate prompt-following ability (e.g., rephrased instructions, chain-of-thought variants, or comparison to non-code-pretrained models). This leaves open the possibility that the patterns arise from generic limitations in parsing numerical constraints rather than kernel-specific pretraining, directly affecting the load-bearing claim that performance differences demonstrate reliance on priors.
  2. [§4] In the feedback-loop experiment (§4), the monotonic improvement in CUDA versus active degradation in TVM IR is taken to show that optimization fails with low-density language due to prior dependence. However, without reported ablations on prompt density, instruction adherence, or model scale, the degradation could stem from coherence maintenance issues in IR tokens independent of pretraining; this alternative is not ruled out and weakens the causal link to the central conclusion.
minor comments (2)
  1. [Experiments 1-3] The manuscript does not report sample sizes, statistical tests, exact model versions, or full prompt templates; these details are needed to assess reproducibility and effect sizes.
  2. [Figures] Figure captions and axis labels should explicitly state the number of runs and any error bars to clarify the reported performance trends.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment below with clarifications on our experimental design and interpretations, while indicating where we will make revisions to improve the presentation of our results.

read point-by-point responses

  1. Referee: [abstract and §3] The interpretation in the abstract and §3 (zero-shot kernel generation) that size information has 'no measurable effect' and is 'invisible' attributes this to pretrained priors, yet the experiments do not include controls that isolate prompt-following ability (e.g., rephrased instructions, chain-of-thought variants, or comparison to non-code-pretrained models). This leaves open the possibility that the patterns arise from generic limitations in parsing numerical constraints rather than kernel-specific pretraining, directly affecting the load-bearing claim that performance differences demonstrate reliance on priors.

    Authors: We appreciate the referee's point regarding potential confounds in attributing size-insensitivity solely to pretrained priors. Our experiments demonstrate that models converge to identical kernel parameters irrespective of provided size information and exhibit sharp degradation specifically on uncommon sizes, a pattern that would not be expected from a uniform parsing limitation. Nevertheless, we acknowledge that controls such as non-code-pretrained models or additional prompt variants were not performed. In the revised manuscript, we will update the abstract and §3 to explicitly discuss this alternative explanation and qualify our interpretation as supported by the uncommon-size degradation results rather than definitively proven by them. revision: partial

  2. Referee: [§4] In the feedback-loop experiment (§4), the monotonic improvement in CUDA versus active degradation in TVM IR is taken to show that optimization fails with low-density language due to prior dependence. However, without reported ablations on prompt density, instruction adherence, or model scale, the degradation could stem from coherence maintenance issues in IR tokens independent of pretraining; this alternative is not ruled out and weakens the causal link to the central conclusion.

    Authors: We agree that the absence of ablations on prompt density, instruction adherence, and model scale leaves room for alternative accounts such as general coherence challenges with IR token sequences. At the same time, the contrast between monotonic gains under feedback in CUDA and active performance degradation in TVM IR is difficult to explain solely by coherence issues, as both languages receive identical feedback structures. We will revise §4 to include an explicit discussion of these alternative explanations and to temper the causal claim accordingly while retaining the observed language-dependent divergence as supporting evidence for prior dependence. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical study with direct experimental outcomes

full rationale

The paper reports three controlled experiments on LLM behavior in code optimization tasks, drawing conclusions from observed performance patterns such as size-insensitivity in zero-shot generation and differential improvement under feedback in CUDA versus TVM IR. No mathematical derivations, equations, fitted parameters presented as predictions, or self-citation chains are present in the provided text. The central claim that LLMs rely on pretrained priors is an interpretive summary of experimental results rather than a reduction of any result to its own inputs by construction. The study is self-contained against external benchmarks through direct measurement of agent outputs, with no load-bearing steps that qualify as circular under the defined patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper rests on standard experimental assumptions in LLM evaluation rather than new free parameters or invented entities.

axioms (1)
  • domain assumption LLM behavior in prompting and feedback loops is sufficiently stable across runs to support comparative conclusions.
    Implicit in the design of the three controlled experiments.

pith-pipeline@v0.9.0 · 5792 in / 1124 out tokens · 52805 ms · 2026-05-20T06:07:36.779541+00:00 · methodology

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Reference graph

Works this paper leans on

11 extracted references · 11 canonical work pages

  1. [1]

    x": <float>,

    CUDA agent: Large-scale agentic RL for high- performance CUDA kernel generation. Fabio Ferreira, Lucca Wobbe, Arjun Krishnakumar, Frank Hutter, and Arber Zela. 2026. Can llms beat classical hyperparameter optimization algorithms? A study on autoresearch. Steffen Finck, Nikolaus Hansen, Raymond Ros, and Anne Auger. 2009. Real-parameter black-box optimizati...

    work page 2026

  2. [9]

    using`input.options()`)

    Make code agnostic to device number, allocate output on the same GPU as the input (e.g. using`input.options()`)

    work page

  3. [10]

    Start code with # Hypothesis: ... comment. {% if hardware_info %} **Target Hardware:** {{ hardware_info }}{% if compute_capability %} (Compute Capability: {{ compute_capability }}){% endif %} {% endif %} <reference> {{ reference_code }} </reference> Start the file with a single comment line:`# Hypothesis: <your plan>`- briefly describe which specific opti...

    work page

  4. [11]

    This is the entry point used to instantiate your kernel

    **Class Definition:** You must define a class named exactly`ModelNew`. This is the entry point used to instantiate your kernel

    work page

  5. [12]

    **Inheritance:** The class must inherit from`torch.nn.Module`

    work page

  6. [13]

    **Initialization:**`__init__(self, ...)`must accept the arguments provided by the reference implementation's`get_init_inputs()`

    work page

  7. [14]

    - *Example:* If the baseline`get_inputs()`returns`[x, y]`, your method signature must be`forward(self, x, y)`

    **Forward Pass:**`forward(self, ...)`must accept the arguments provided by the reference implementation's`get_inputs()`. - *Example:* If the baseline`get_inputs()`returns`[x, y]`, your method signature must be`forward(self, x, y)`

    work page

  8. [15]

    **Output:** The return value of`forward`must have the exact same shape and data type as the reference output

    work page

  9. [16]

    Use torch.utils.cpp_extension.load_inline to compile C++/CUDA source strings

    work page

  10. [17]

    Do not write any code except described above

    work page

  11. [18]

    using`input.options()`)

    Make code agnostic to device number, allocate output on the same GPU as the input (e.g. using`input.options()`). {% if require_hypothesis %}9. Start code with # Hypothesis: ... comment.{% endif %} {% if hardware_info %} **Target Hardware:** {{ hardware_info }}{% if compute_capability %} (Compute Capability: {{ compute_capability }}){% endif %} {% endif %}...

    work page 2048