arxiv: 2604.21598 · v2 · submitted 2026-04-23 · 💻 cs.SE · cs.AI

Recognition: unknown

You Don't Need Public Tests to Generate Correct Code

Kaushitha Silva , Srinath Perera

Authors on Pith no claims yet

Pith reviewed 2026-05-09 20:54 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords code generationlarge language modelsself-simulationtest-free debuggingalgorithmic problem solvingmulti-agent systems

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The pith

Large language models can generate correct code by creating and simulating their own test cases without any public tests.

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

The paper argues that code generation systems do not need human-provided public test cases to reach functional correctness. Large language models can instead plan solutions, generate their own input examples, simulate how the code would run, and fix issues through repeated internal checks. This removes a key practical barrier because complete test suites are rarely available before code is written in real development. The resulting DryRUN approach reaches performance levels similar to methods that do use external tests on recent benchmarks while producing shorter outputs overall.

Core claim

DryRUN removes the need for ground-truth test data by letting the LLM iteratively plan, synthesize its own test inputs, and run simulated executions for self-correction, achieving performance comparable to test-dependent baselines on LiveCodeBench v6 while reducing token usage and avoiding overfitting to public examples.

What carries the argument

The DryRUN framework, which replaces external test cases with the model's own generation of inputs and internal simulation of execution flows to enable self-correction during code production.

If this is right

Code generation systems can function in open-ended settings where no pre-written tests exist.
The process avoids overfitting to limited public examples and handles hidden cases more reliably.
Fewer output tokens are needed, which reduces the compute cost of producing each solution.
Multi-agent coding setups can proceed without waiting for or depending on external execution signals.

Where Pith is reading between the lines

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

The same self-simulation loop could apply to generating scripts or small programs outside competitive programming problems.
Deeper integration of internal simulation into model reasoning might further decrease the need for any form of external validation.
Applying the method to domains with different correctness criteria, such as data processing or API usage, would test its broader reach.

Load-bearing premise

Large language models can construct accurate test inputs and simulate program runs well enough to correct code without introducing new errors that reduce final correctness.

What would settle it

Evaluating the method on a benchmark with held-out tests and observing that the generated code passes fewer hidden cases than test-using baselines, or that many self-generated inputs are invalid, would indicate the approach fails to deliver claimed correctness.

Figures

Figures reproduced from arXiv: 2604.21598 by Kaushitha Silva, Srinath Perera.

**Figure 1.** Figure 1: State-of-the-art Code Generation methods (top) rely heavily on sample examples provided in the problem specifi [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Figure 2: LLM is prompted to remove the concrete sample examples (highlighted in red) and synthesize generalized input and [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Figure 4: Analysis of the Overconfidence Gap (gemini-3-flash, [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 3.** Figure 3: Analysis of the Overconfidence Gap (gpt-5-mini, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 6.** Figure 6: Example of Simulation of DryRUN An excerpt from [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 5.** Figure 5: Example of a failure case of CodeSIM’s Simula [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

read the original abstract

Multi-agent systems are frequently employed for autonomous code generation, demonstrating strong utility in complex algorithmic problem-solving. Recent studies tackle the difficulty of producing functionally correct programs by leveraging simulation-guided planning and debugging, wherein language models step through execution traces to validate logic. Nevertheless, these methods rely heavily on human-authored public test cases to anchor the simulation and debugging cycles. Hand-crafting exhaustive input-output pairs creates a significant, labor-intensive bottleneck within the software development lifecycle. Since ground-truth examples are seldom accessible before actual implementation in real-world scenarios, this reliance limits existing approaches primarily to curated competitive programming datasets. Additionally, we demonstrate that depending on these public tests creates an "overconfidence gap," leading frameworks to overfit to basic examples and underperform on hidden test suites. Conversely, we note that external input samples are not an absolute requirement for successful code generation. We show that large language models possess the capability to autonomously construct valid inputs and simulate execution flows for self-correction. Building on this, we introduce DryRUN, a framework that removes the necessity for ground-truth data by enabling the LLM to iteratively plan, synthesize its own test inputs, and run simulated executions, thereby mitigating algorithmic overconfidence. Assessments using the LiveCodeBench v6 dataset (post-March 2025) reveal that DryRUN achieves comparable performance to CodeSIM, a state-of-the-art, test-dependent baseline. Notably, it does so entirely without public tests or external execution signals, all while decreasing overall output token usage.

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.

Desk Editor's Note private letter to a colleague

DryRUN claims LLMs can match CodeSIM on LiveCodeBench v6 by inventing their own inputs and simulating runs without public tests, but the abstract gives no checks on whether those simulations are accurate.

read the letter

The main point is that DryRUN lets an LLM generate code by creating its own test inputs, planning, and simulating execution internally, reaching performance levels close to the test-dependent CodeSIM baseline on LiveCodeBench v6 while cutting token use. This removes the need for human-written public tests that earlier simulation-guided methods require. The paper notes that relying on those public tests creates an overconfidence gap, where models overfit to simple cases and do worse on hidden tests. DryRUN tries to sidestep that by making the LLM self-sufficient in input synthesis and trace simulation. That direction is useful because real software work rarely starts with ready-made test suites. The write-up does a reasonable job framing the practical bottleneck and showing a lower-cost alternative in token terms. The soft spots sit in the evidence. The abstract reports comparable results but supplies no ablation studies, no statistical tests, and no direct measure of how often the LLM's simulated outputs match what an actual interpreter would produce on the self-generated inputs. Without those checks, the performance parity could come from the base model or dataset quirks rather than the simulation step itself. The assumption that LLMs can reliably build valid inputs and simulate flows without introducing new errors stays untested in the visible material. This paper is for researchers working on multi-agent code generation and autonomous debugging. A reader focused on moving beyond curated datasets would get value from the framing and the token-saving result. It deserves a serious referee because the problem is real and the proposed shift is concrete, even if the current experiments need more grounding on simulation fidelity. I would send it to peer review with requests for those validation details and controls.

Referee Report

3 major / 1 minor

Summary. The paper introduces DryRUN, a framework for autonomous code generation in which LLMs iteratively plan, synthesize their own test inputs, and simulate execution traces for self-correction and debugging. It argues that reliance on human-authored public tests creates an overconfidence gap that causes overfitting to simple cases and poor generalization to hidden tests; DryRUN removes this dependency by enabling fully internal simulation. On the LiveCodeBench v6 dataset (post-March 2025), the method is reported to reach performance comparable to the test-dependent CodeSIM baseline while reducing output token usage.

Significance. If the empirical claims hold under rigorous controls, the result would be significant for autonomous code generation: it would demonstrate that LLMs can reliably self-simulate valid inputs and execution flows, thereby eliminating a major practical bottleneck (the need for pre-existing public tests) and extending simulation-guided techniques beyond curated competitive-programming settings. The reported token savings would also be a useful engineering contribution.

major comments (3)

[Abstract] Abstract: the central claim that autonomous input synthesis and simulated execution enable reliable self-correction (and thus parity with CodeSIM) rests on the unverified assumption that LLM-generated traces match real interpreter behavior closely enough to avoid propagating errors. No measurement of simulation fidelity (e.g., mismatch rates between predicted and actual outputs on self-generated inputs) is described, which is load-bearing for attributing success to the DryRUN mechanism rather than base-model strength or dataset properties.
[Abstract] Abstract: the reported performance comparability to CodeSIM is stated at a high level without reference to experimental controls, statistical tests, ablation studies isolating the simulation component, or the number of problems and exact metrics on LiveCodeBench v6. This absence prevents assessment of whether the result is robust or reproducible.
[Abstract] Abstract: the demonstration of the 'overconfidence gap' in test-dependent methods appears to rely on internal analysis whose independence from the proposed DryRUN framework is not established, risking circularity in the argument that removing public tests improves generalization.

minor comments (1)

[Abstract] The abstract introduces the LiveCodeBench v6 dataset (post-March 2025) without indicating whether it is publicly released or how hidden tests were accessed, which affects reproducibility claims.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below, clarifying aspects of the manuscript and indicating planned revisions to strengthen the presentation of our claims.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that autonomous input synthesis and simulated execution enable reliable self-correction (and thus parity with CodeSIM) rests on the unverified assumption that LLM-generated traces match real interpreter behavior closely enough to avoid propagating errors. No measurement of simulation fidelity (e.g., mismatch rates between predicted and actual outputs on self-generated inputs) is described, which is load-bearing for attributing success to the DryRUN mechanism rather than base-model strength or dataset properties.

Authors: We agree that explicit quantification of simulation fidelity would strengthen attribution of results to the DryRUN mechanism. The current manuscript provides illustrative examples of the planning, input synthesis, and trace simulation steps along with end-to-end performance, but does not report mismatch rates between LLM-predicted outputs and actual interpreter results on self-generated inputs. In the revision we will add a dedicated analysis (new subsection in Experiments or an appendix) that measures fidelity on a representative sample of problems by executing the self-generated inputs in a real interpreter and reporting agreement rates. This will allow readers to assess how closely the internal simulations track ground-truth behavior. revision: yes
Referee: [Abstract] Abstract: the reported performance comparability to CodeSIM is stated at a high level without reference to experimental controls, statistical tests, ablation studies isolating the simulation component, or the number of problems and exact metrics on LiveCodeBench v6. This absence prevents assessment of whether the result is robust or reproducible.

Authors: The abstract summarizes the key outcome due to length limits, but the full manuscript details the LiveCodeBench v6 evaluation (post-March 2025 split), the exact number of problems, the primary metric (pass rate), and direct numerical comparison to CodeSIM. Ablation studies isolating the contribution of autonomous input synthesis and trace simulation are also present. We did not include formal statistical significance tests in the original version. In the revision we will (1) expand the abstract to include the problem count and headline metric values and (2) add a brief statistical comparison (e.g., paired t-test or bootstrap confidence intervals) in the Experiments section. revision: partial
Referee: [Abstract] Abstract: the demonstration of the 'overconfidence gap' in test-dependent methods appears to rely on internal analysis whose independence from the proposed DryRUN framework is not established, risking circularity in the argument that removing public tests improves generalization.

Authors: The overconfidence-gap analysis is performed on existing test-dependent baselines (including CodeSIM) by comparing their behavior on public tests versus held-out hidden tests; it does not invoke any DryRUN components or self-simulation. This section is presented as motivation before DryRUN is introduced. To eliminate any ambiguity we will revise the manuscript to explicitly label the analysis as an independent evaluation of prior methods and to restate the logical separation between the gap demonstration and the subsequent DryRUN experiments. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on external benchmarks

full rationale

The paper's core result is an empirical performance comparison of DryRUN against the external baseline CodeSIM on the external LiveCodeBench v6 dataset (post-March 2025 hidden tests). No equations, fitted parameters renamed as predictions, or self-citations are present in the provided text. The claim that LLMs can autonomously synthesize inputs and simulate executions is tested via end-to-end correctness on held-out tests rather than being true by construction or reduced to internal analysis alone. The overconfidence gap demonstration is described as an observation from public-test reliance, not a definitional loop. This matches the default expectation of a self-contained empirical paper against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that LLMs can generate valid test inputs and perform accurate internal simulations without external signals; no free parameters or invented entities beyond the named framework are explicitly introduced.

axioms (1)

domain assumption Large language models can autonomously construct valid inputs and simulate execution flows with sufficient accuracy for effective self-correction.
Invoked when describing DryRUN's ability to replace public tests with self-generated ones.

invented entities (1)

DryRUN framework no independent evidence
purpose: Enables iterative planning, self-synthesis of test inputs, and simulated executions for test-free code generation.
New named system introduced to operationalize the self-correction approach.

pith-pipeline@v0.9.0 · 5559 in / 1477 out tokens · 69638 ms · 2026-05-09T20:54:29.419059+00:00 · methodology

discussion (0)

Reference graph

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