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arxiv: 2604.19825 · v1 · submitted 2026-04-20 · 💻 cs.SE · cs.AI

SolidCoder: Bridging the Mental-Reality Gap in LLM Code Generation through Concrete Execution

Woojin Lee , Jin-Xia Huang This is my paper

Pith reviewed 2026-05-10 04:41 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords LLM code generationmental simulationexecution groundingproperty-based oraclesedge casescode synthesissandbox verification
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The pith

SolidCoder replaces mental simulation of code execution with sandboxed runs and forced edge-case planning to close the gap between imagined and actual behavior in LLM code generation.

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

The paper claims that LLMs generate buggy code because they rely on internal mental traces that often hallucinate correctness instead of reflecting reality. It identifies two separate problems: missing edge cases when planning the code and then failing to catch flaws during verification. SolidCoder counters both by requiring explicit edge-case consideration before writing the algorithm and by substituting imagined execution with actual runs inside a sandbox that uses automatically generated property-based oracles to test behavior. When applied to GPT-4o this produces higher pass rates on standard coding benchmarks, and the gains hold for other models as well.

Core claim

The central claim is that the Mental-Reality Gap in LLM code generation consists of a Specification Gap and a Verification Gap, and that both can be closed by the S.O.L.I.D. architecture: first forcing edge-case awareness during planning and then grounding verification in concrete sandbox execution driven by property-based oracles rather than imagined traces.

What carries the argument

The S.O.L.I.D. architecture, which mandates edge-case awareness before algorithm design and substitutes hallucinated execution traces with sandboxed execution using property-based oracles.

If this is right

  • Edge-case awareness before design yields the single largest performance lift among the architecture's components.
  • Execution grounding catches categories of errors that cannot be fixed by improving the specification alone.
  • The same gains appear when the method is applied to models that have already undergone RL post-training.
  • The approach raises pass@1 on HumanEval, CodeContests, and APPS without requiring changes to the underlying LLM.

Where Pith is reading between the lines

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

  • Similar concrete-grounding steps could be tested in non-code reasoning domains where models currently simulate outcomes internally.
  • Better automatic generation of property oracles would directly increase the reliability of the verification step.
  • The separation of planning from verification suggests that future systems might run the two phases with different models or different prompting strategies.

Load-bearing premise

Property-based oracles generated during the process can reliably detect incorrect behavior on all relevant inputs without introducing false negatives or containing errors themselves.

What would settle it

A controlled experiment on a benchmark containing hidden bugs where the oracles are deliberately allowed to miss those bugs, measuring whether pass rates then fall back to the level achieved by mental-simulation baselines.

Figures

Figures reproduced from arXiv: 2604.19825 by Jin-Xia Huang, Woojin Lee.

Figure 1
Figure 1. Figure 1: Comparative Architecture Overview: CodeSIM vs. SolidCoder. The top pipeline illustrates the CodeSIM baseline, which relies on mental simulation ("imagines") for verification during planning and debugging, resulting in a "Mental-Reality Gap" prone to LLM hallucination. The bottom pipeline demonstrates SolidCoder, which bridges this gap by grounding verification in concrete execution ("executes"). SolidCoder… view at source ↗
Figure 2
Figure 2. Figure 2: The Mental-Reality Gap in Action. A comparative example on a list rotation problem. Left (CodeSIM): Mental Simulation traces through the code and incorrectly concludes “Output matches expected. PASS”—the LLM hallucinates correct behavior despite a bug. Right (SolidCoder): Live Execution runs the actual code, revealing the bug through concrete failure: AssertionError: [3,1,2] != [2,3,1]. This demonstrates h… view at source ↗
read the original abstract

State-of-the-art code generation frameworks rely on mental simulation, where LLMs internally trace execution to verify correctness. We expose a fundamental limitation: the Mental-Reality Gap -- where models hallucinate execution traces and confidently validate buggy code. This gap manifests along two orthogonal dimensions: the Specification Gap (overlooking edge cases during planning) and the Verification Gap (hallucinating correct behavior for flawed code). We propose SolidCoder with a simple principle: don't imagine -- execute. The S.O.L.I.D. architecture addresses both dimensions by forcing edge-case awareness before algorithm design and replacing imagined traces with sandboxed execution using property-based oracles. With GPT-4o, SolidCoder achieves state-of-the-art pass@1 performance: 95.7% on HumanEval (+0.6%p), 77.0% on CodeContests (+4.3%p), and 26.7% on APPS (+3.4%p). Ablation reveals that edge-case awareness provides the largest individual gain, while execution grounding catches categorically different errors that specification improvements cannot address. These gains generalize to RL post-trained models, validating that bridging both gap dimensions is essential for robust code synthesis. We release our code and framework to facilitate future research.

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

3 major / 2 minor

Summary. The manuscript introduces SolidCoder to address the Mental-Reality Gap in LLM code generation, decomposed into a Specification Gap (overlooking edge cases in planning) and a Verification Gap (hallucinating correct execution traces for buggy code). The S.O.L.I.D. architecture enforces edge-case awareness before algorithm design and replaces mental simulation with sandboxed execution driven by property-based oracles. With GPT-4o, it reports state-of-the-art pass@1 scores of 95.7% on HumanEval (+0.6%p), 77.0% on CodeContests (+4.3%p), and 26.7% on APPS (+3.4%p). Ablations indicate that edge-case awareness yields the largest single gain while execution grounding addresses distinct error classes; results are claimed to generalize to RL post-trained models. Code and framework are released.

Significance. If the empirical claims hold after addressing the gaps below, the work supplies concrete evidence that forcing execution over mental simulation can improve LLM code synthesis on public benchmarks, with the larger relative gains on CodeContests and APPS suggesting utility on harder problems. The public release of code supports reproducibility and future work. The absolute improvements remain modest on the most saturated benchmark (HumanEval), so the practical significance is incremental rather than revolutionary.

major comments (3)
  1. [Abstract and Experiments] Abstract and Experiments section: The claim that execution grounding 'catches categorically different errors that specification improvements cannot address' is load-bearing for the two-gap framing and the reported +4.3%p gain on CodeContests. Yet no quantitative validation of the property-based oracles is supplied—no false-negative rates on held-out buggy variants, no completeness metrics across the input space, and no comparison against human-crafted oracles. If the oracles are LLM-synthesized, they risk inheriting the Verification Gap the paper seeks to close.
  2. [Ablation study] Ablation study (presumably §4.2): The ablation results attribute the largest gain to edge-case awareness and claim orthogonal contributions from execution grounding, but the text provides neither error bars, multiple random seeds, nor statistical significance tests. With absolute gains as small as +0.6%p on HumanEval, variance in sampling or prompt ordering could explain the differences, weakening the cross-component comparison.
  3. [Results] Results and generalization claim: The abstract states that gains 'generalize to RL post-trained models,' but no specific numbers, tables, or experimental details for those models appear in the reported results. This leaves the central claim that bridging both gap dimensions is 'essential' without direct supporting evidence for the RL setting.
minor comments (2)
  1. [Introduction] The S.O.L.I.D. acronym is used throughout but never expanded in the abstract or early sections; a one-sentence definition would improve readability.
  2. [Experiments] Benchmark tables should report the number of samples or temperature settings used for pass@1 to allow direct comparison with prior work.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review of our manuscript. The comments highlight key areas where we can improve the rigor and clarity of our presentation. We address each major comment point by point below and indicate the revisions we plan to make.

read point-by-point responses

  1. Referee: Abstract and Experiments section: The claim that execution grounding 'catches categorically different errors that specification improvements cannot address' is load-bearing for the two-gap framing and the reported +4.3%p gain on CodeContests. Yet no quantitative validation of the property-based oracles is supplied—no false-negative rates on held-out buggy variants, no completeness metrics across the input space, and no comparison against human-crafted oracles. If the oracles are LLM-synthesized, they risk inheriting the Verification Gap the paper seeks to close.

    Authors: We concur that quantitative validation of the property-based oracles is necessary to support the claim that execution grounding addresses a distinct Verification Gap. In the revised manuscript, we will include an evaluation of the oracles' effectiveness, reporting false-negative rates on held-out sets of buggy code variants and completeness metrics. We will also specify the oracle construction process, noting that although LLMs may assist in initial generation, the oracles are property-based and their correctness is validated through repeated sandbox executions, thereby reducing the risk of propagating the Verification Gap. These additions will provide the missing empirical grounding for the orthogonal contributions of the two components. revision: yes

  2. Referee: Ablation study (presumably §4.2): The ablation results attribute the largest gain to edge-case awareness and claim orthogonal contributions from execution grounding, but the text provides neither error bars, multiple random seeds, nor statistical significance tests. With absolute gains as small as +0.6%p on HumanEval, variance in sampling or prompt ordering could explain the differences, weakening the cross-component comparison.

    Authors: The absence of statistical measures in the ablation study is a valid concern, particularly given the modest absolute improvements on saturated benchmarks. We will revise the ablation analysis to include results averaged over multiple random seeds, with error bars indicating standard deviation, and apply appropriate statistical tests (e.g., Wilcoxon signed-rank test) to assess the significance of the differences between ablated variants. This will strengthen the evidence for the distinct contributions of edge-case awareness and execution grounding. revision: yes

  3. Referee: Results and generalization claim: The abstract states that gains 'generalize to RL post-trained models,' but no specific numbers, tables, or experimental details for those models appear in the reported results. This leaves the central claim that bridging both gap dimensions is 'essential' without direct supporting evidence for the RL setting.

    Authors: We acknowledge that the current manuscript does not present the RL post-trained model results with sufficient detail in the main body. To address this, we will expand the Results section to include a dedicated paragraph and table summarizing the performance gains on RL post-trained models across the benchmarks, along with the experimental configuration. This will provide the direct evidence needed to support the generalization claim. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurements on public benchmarks with no self-referential derivations

full rationale

The paper presents an empirical framework (SolidCoder) and reports direct pass@1 measurements on fixed public benchmarks (HumanEval, CodeContests, APPS). No equations, first-principles derivations, fitted parameters, or predictions appear in the abstract or described method. Performance deltas are computed from external test suites rather than quantities defined by the authors' own prior outputs. No self-citation chains or ansatzes are invoked to justify core claims; the architecture is described procedurally (edge-case awareness + sandboxed execution with property-based oracles) without reducing to self-definition. The central results therefore remain independent of the paper's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on domain assumptions about LLM prompt-following for edge cases and the sufficiency of property-based oracles; no free parameters are fitted in the reported results, and the new conceptual entities (gaps and architecture) lack independent falsifiable evidence beyond the benchmark gains.

axioms (2)
  • domain assumption LLMs can be reliably prompted to enumerate and incorporate edge cases into code plans
    The Specification Gap component depends on this prompt effectiveness.
  • domain assumption Property-based oracles in a sandbox can verify functional correctness without missing critical behaviors
    The Verification Gap solution assumes oracles are both complete and accurate.
invented entities (2)
  • Mental-Reality Gap no independent evidence
    purpose: Conceptual label for the discrepancy between LLM internal simulation and actual code behavior
    New framing introduced to organize the two sub-gaps.
  • S.O.L.I.D. architecture no independent evidence
    purpose: Structured pipeline combining edge-case planning and execution grounding
    New system name and components proposed in the paper.

pith-pipeline@v0.9.0 · 5521 in / 1600 out tokens · 45472 ms · 2026-05-10T04:41:25.285227+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

35 extracted references · 35 canonical work pages · 3 internal anchors

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    Special pattern (all same, alternating, etc.) or Boundary values ## Plan Write a detailed plan that handles these edge cases. The plan should be step-by-step and easy to implement. B.2 [O] Oracle-based Assertion Prompt The Oracle prompt instructs the LLM to act as a “Red Team” tester, generating property-based assertions rather than exact output predictio...

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    "" import sys import math from typing import List, Dict, Any, Optional, Union, Tuple {code} {test_script}

    Include an assert that would fail if the assumption is violated. Format: Assumption: <short text> Test Script: “‘python # call the function defined above result = <call> assert <oracle about result> “‘ B.3 [L] Live Execution Live Execution does not use a separate prompt—it directly executes the test scripts generated by the Oracle component in a sandboxed...

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    FAIL_CRASH

    Predict the final output. If you find a logic error or mismatch with the plan, output: **CODE_SIMULATION_FAILED** If the code seems correct, output: **CODE_SIMULATION_PASSED** B.5 [D] Defensive Accumulation Defensive Accumulation is implemented as a run- time mechanism rather than a prompt. When Live Execution detects a failing test case, the test is auto...

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    Wrap the code in “‘{language}

    Is it a fair test? If the test case is valid, output: **VALID** If it violates constraints or expects wrong output, output: **INVALID** B.7 Code Fix Prompt When a vulnerability is found, SolidCoder uses this prompt to fix the code: Code Fix Prompt ## Problem {problem} ## Code “‘{language} {code} “‘ ## Vulnerability Found The following test script failed (...

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    Maximum constraint input

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    The plan should be step-by-step and easy to implement

    Special pattern (all same, alternating, etc.) or Boundary values ## Plan Write a detailed plan that handles these edge cases. The plan should be step-by-step and easy to implement. LLM Response: To solve the problem of determining if any two numbers in a list are closer to each other than a given threshold, we need to consider potential edge cases and dev...

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    Empty/Minimal Input: • An empty list or a list with a single element should return False because there are no pairs of numbers to compare

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    The solution should be efficient in terms of time and space complexity to handle large inputs

    Maximum Constraint Input: • Consider the scenario where the list contains the maximum number of elements allowed by the system’s memory. The solution should be efficient in terms of time and space complexity to handle large inputs

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    • Lists with alternating values or values that are very close to each other but not within the threshold

    Special Pattern or Boundary Values: • Lists where all elements are the same, which should return True if the threshold is greater than zero. • Lists with alternating values or values that are very close to each other but not within the threshold. • Lists with very large or very small floating-point numbers to test precision and handling of floating-point ...

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    Initial Checks: • If the list numbers is empty or contains only one element, return False immediately since no pairs exist

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    Sorting helps in efficiently finding close elements because if two numbers are close, they will be adjacent in a sorted list

    Sorting: • Sort the list of numbers. Sorting helps in efficiently finding close elements because if two numbers are close, they will be adjacent in a sorted list

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    • For each pair(numbers[i], numbers[i+1]), calculate the absolute difference

    Iterate and Compare: • Iterate through the sorted list and compare each pair of adjacent elements. • For each pair(numbers[i], numbers[i+1]), calculate the absolute difference. • If the difference is less than the threshold, returnTrue

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    Return Result: • If no such pair is found after checking all adjacent pairs, returnFalse

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    Thus, the overall time complexity isO(nlogn), which is efficient for large inputs

    Complexity Consideration: • Sorting the list takes O(nlogn) time, and iterating through the list takes O(n) time. Thus, the overall time complexity isO(nlogn), which is efficient for large inputs

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    Plan Simulation Prompt: You are a programmer tasked with verifying a plan to solve a given problem using the **Python3** programming language

    Precision Handling: • Ensure that floating-point arithmetic is handled correctly by using Python’s built-in functions which are designed to manage floating-point precision. Plan Simulation Prompt: You are a programmer tasked with verifying a plan to solve a given problem using the **Python3** programming language. [Problem and Plan shown above] **Expected...

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    Select a sample input

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    Trace the code execution step-by-step

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    Track variable values

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    Predict the final output. If you find a logic error or mismatch with the plan, output: **CODE_SIMULATION_FAILED** If the code seems correct, output: **CODE_SIMULATION_PASSED** LLM Response: Let’s select a sample input and trace the code execution step-by-step. Sample Input: numbers = [1.0, 2.8, 3.0, 4.0, 5.0, 2.0] threshold = 0.3 Code Execution:

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    •len(numbers) = 6, so the check fails, and we proceed to the next step

    Initial Check: • The code checks if the length ofnumbersis less than 2. •len(numbers) = 6, so the check fails, and we proceed to the next step

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    • Sortednumbers = [1.0, 2.0, 2.8, 3.0, 4.0, 5.0]

    Sorting the List: • The listnumbersis sorted. • Sortednumbers = [1.0, 2.0, 2.8, 3.0, 4.0, 5.0]

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    •Iteration 1(i = 0): –Comparenumbers[0]andnumbers[1]:|1.0−2.0|= 1.0 –1.0≥0.3, so continue to the next iteration

    Iterate and Compare Adjacent Elements: We iterate over the sorted list and compare adjacent elements. •Iteration 1(i = 0): –Comparenumbers[0]andnumbers[1]:|1.0−2.0|= 1.0 –1.0≥0.3, so continue to the next iteration. •Iteration 2(i = 1): –Comparenumbers[1]andnumbers[2]:|2.0−2.8|= 0.8 –0.8≥0.3, so continue to the next iteration. •Iteration 3(i = 2): –Compare...

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    Conclusion:The code correctly identifies that there are two numbers in the list that are closer to each other than the given threshold

    Return Result: • Since we found two numbers ( 2.8 and 3.0) that are closer than the threshold ( 0.3), the function returnsTrue. Conclusion:The code correctly identifies that there are two numbers in the list that are closer to each other than the given threshold. The logic and execution match the expected behavior. Output: CODE_SIMULATION_PASSED [O] Oracl...

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    Identify a weak assumption (Type, Value, Structure, or Relationship)

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    Produce a **Python test script** that calls the target function with a breaking input

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    The function should return False when the list contains NaN, as NaN comparisons are not valid

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    Does the input satisfy all constraints (e.g. range, type, format)?

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    Is the asserted output (if any) logically correct?

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    Is it a fair test? If the test case is valid, output: **VALID** If it violates constraints or expects wrong output, output: **INVALID** Judge Response: To determine if the proposed test cases are valid and correct for the given problem, let’s analyze each aspect:

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    The proposed test cases use lists containingfloat(’nan’)andfloat(’inf’), which are valid float values in Python

    Input Constraints:The function has_close_elements accepts a list of floats and a float threshold. The proposed test cases use lists containingfloat(’nan’)andfloat(’inf’), which are valid float values in Python. Therefore, the input satisfies the type constraints

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    Therefore, the presence ofNaN should not affect the determination of whether any two numbers are closer than the threshold

    Logical Correctness of the Asserted Output: • For the test case with float(’nan’): In Python, any comparison with NaN (Not a Number) is always false, including equality and inequality checks. Therefore, the presence ofNaN should not affect the determination of whether any two numbers are closer than the threshold. The function should returnFalseas no vali...

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    "" {code} {test_script}

    Fairness of the Test:The test cases are fair as they check the function’s behavior with special float values (NaNandInfinity), which are edge cases that can occur in floating-point computations. Given these points, the proposed test cases are logically correct and do not violate any constraints. They are valid tests for the function’s behavior with specia...

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