Recognition: unknown
ProgramBench: Can Language Models Rebuild Programs From Scratch?
Pith reviewed 2026-05-07 15:58 UTC · model grok-4.3
The pith
Current language models cannot fully reconstruct any of 200 complex programs from scratch using only documentation and behavioral tests.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
ProgramBench requires agents to rebuild full codebases ranging from small CLI tools to large systems such as FFmpeg, SQLite, and the PHP interpreter, given only the executable and documentation. Behavioral tests generated via agent-driven fuzzing allow assessment without dictating implementation details. Across 200 tasks, no model resolves any task fully, the best model passes 95 percent of tests on only 3 percent of tasks, and all models produce monolithic single-file outputs that diverge from human-written modular code.
What carries the argument
ProgramBench benchmark of 200 tasks with agent-driven fuzzing to produce structure-agnostic behavioral tests that verify functional equivalence to reference executables.
If this is right
- Existing narrow benchmarks for code tasks overestimate language-model readiness for realistic software projects.
- Agents tasked with long-term codebase growth will require substantial human input on architecture decisions.
- Evaluation methods that avoid prescribing code structure expose large gaps between model outputs and human engineering practices.
- Scaling current models will not suffice without advances in handling high-level design and modularity.
Where Pith is reading between the lines
- The benchmark's focus on holistic rebuilding suggests future work should test iterative, multi-turn development rather than one-shot reconstruction.
- Monolithic outputs may reflect training data biases toward small scripts, pointing to a need for explicit modular-design objectives.
- Extending the task set beyond 200 programs would test whether current failure rates generalize to broader software domains.
Load-bearing premise
Agent-driven fuzzing creates tests that comprehensively capture required functionality without favoring any particular implementation style.
What would settle it
A model that passes 100 percent of tests on at least half the 200 tasks while producing multi-file modular codebases comparable to human reference implementations.
read the original abstract
Turning ideas into full software projects from scratch has become a popular use case for language models. Agents are being deployed to seed, maintain, and grow codebases over extended periods with minimal human oversight. Such settings require models to make high-level software architecture decisions. However, existing benchmarks measure focused, limited tasks such as fixing a single bug or developing a single, specified feature. We therefore introduce ProgramBench to measure the ability of software engineering agents to develop software holisitically. In ProgramBench, given only a program and its documentation, agents must architect and implement a codebase that matches the reference executable's behavior. End-to-end behavioral tests are generated via agent-driven fuzzing, enabling evaluation without prescribing implementation structure. Our 200 tasks range from compact CLI tools to widely used software such as FFmpeg, SQLite, and the PHP interpreter. We evaluate 9 LMs and find that none fully resolve any task, with the best model passing 95\% of tests on only 3\% of tasks. Models favor monolithic, single-file implementations that diverge sharply from human-written code.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces ProgramBench, a benchmark for evaluating language models and software engineering agents on the task of rebuilding complete programs from scratch. Given only a reference executable and its documentation, agents must architect and implement a matching codebase; success is measured via end-to-end behavioral tests produced by agent-driven fuzzing rather than structure-prescribing unit tests. The benchmark contains 200 tasks spanning compact CLI tools to complex real-world systems including FFmpeg, SQLite, and the PHP interpreter. Evaluation of nine LMs finds that none fully resolve any task, the strongest model reaches 95% test pass rate on only 3% of tasks, and generated solutions are overwhelmingly monolithic single-file implementations that diverge from human-written code.
Significance. If the fuzzing-based tests prove comprehensive and unbiased, the work supplies a demanding new evaluation axis for agentic coding that moves beyond bug fixing or single-feature tasks. The empirical demonstration that current models cannot produce functionally correct, architecturally plausible implementations at scale would be a useful signal for the field and could motivate research on long-horizon planning and modular design in LLMs. The decision to avoid prescribing implementation structure via behavioral testing is a methodological strength worth preserving if coverage and validity can be demonstrated.
major comments (2)
- [Abstract / benchmark construction] Abstract and benchmark-construction section: the central claim that 'none fully resolve any task' and the reported failure rates depend on the assertion that agent-driven fuzzing produces tests that 'comprehensively capture required functionality without prescribing implementation structure.' No coverage metrics, mutation strategy details, number of generated tests per task, or validation against human-written test suites for reference programs (FFmpeg, SQLite, PHP interpreter) are provided. For programs with substantial internal state, error paths, or non-determinism, incomplete coverage would allow a correct implementation to fail the benchmark or an incorrect one to pass, directly undermining the headline result.
- [Results / evaluation] Results section (performance tables): the statement that the best model passes 95% of tests on only 3% of tasks is presented without accompanying information on statistical significance, run-to-run variance, or the exact definition of 'fully resolve.' This makes it difficult to judge whether the reported percentages are robust or sensitive to small changes in test generation.
minor comments (2)
- [Abstract] Abstract contains the typo 'holisitically' (should be 'holistically').
- [Benchmark description] The description of the 200 tasks would benefit from a summary table or breakdown by category (e.g., CLI tools vs. interpreters) and average task size or complexity metrics to help readers gauge representativeness.
Simulated Author's Rebuttal
We thank the referee for the detailed and constructive review. The comments highlight important aspects of benchmark validity and result robustness that we have addressed through revisions to the manuscript.
read point-by-point responses
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Referee: [Abstract / benchmark construction] Abstract and benchmark-construction section: the central claim that 'none fully resolve any task' and the reported failure rates depend on the assertion that agent-driven fuzzing produces tests that 'comprehensively capture required functionality without prescribing implementation structure.' No coverage metrics, mutation strategy details, number of generated tests per task, or validation against human-written test suites for reference programs (FFmpeg, SQLite, PHP interpreter) are provided. For programs with substantial internal state, error paths, or non-determinism, incomplete coverage would allow a correct implementation to fail the benchmark or an incorrect one to pass, directly undermining the headline result.
Authors: We agree that the original manuscript provided insufficient methodological detail on the agent-driven fuzzing process. In the revised version, we have added a dedicated subsection under benchmark construction that specifies: the average number of tests generated per task (ranging from 40 for simple CLIs to over 150 for complex systems), the mutation and exploration strategies (input fuzzing, state perturbation, error-path triggering, and documentation-guided scenario generation), and quantitative coverage metrics (statement and branch coverage) computed on a stratified sample of 40 tasks. For validation, we report overlap with available human-written test suites on smaller reference programs and note that for FFmpeg, SQLite, and PHP the tests prioritize observable I/O and documented behaviors. We have also inserted a limitations paragraph acknowledging that full coverage for programs with extensive internal state remains challenging and that non-determinism is mitigated by repeated execution with fixed seeds. revision: yes
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Referee: [Results / evaluation] Results section (performance tables): the statement that the best model passes 95% of tests on only 3% of tasks is presented without accompanying information on statistical significance, run-to-run variance, or the exact definition of 'fully resolve.' This makes it difficult to judge whether the reported percentages are robust or sensitive to small changes in test generation.
Authors: We have clarified the definition of 'fully resolve' in both the abstract and results section as achieving a 100% pass rate on the complete set of generated tests for a given task. The results section now includes per-model mean pass rates and standard deviations computed across five independent runs (different random seeds for both test generation and model sampling). We additionally report that the proportion of tasks on which the strongest model exceeds the 95% threshold is statistically significantly lower than the proportion exceeding 80% or 90% thresholds (p < 0.01, paired t-test). These changes demonstrate that the headline 3% figure is stable under the observed variance. revision: yes
Circularity Check
No circularity: empirical benchmark with external references
full rationale
The paper is a purely empirical benchmark study that introduces ProgramBench to evaluate language models on holistic software development tasks. It contains no mathematical derivations, equations, fitted parameters, or first-principles claims that could reduce to their own inputs. All results are measured by direct execution against external reference executables and fuzzing-derived behavioral tests, with no self-definitional loops, self-citation load-bearing premises, or renamed known results. The evaluation chain is self-contained against independent benchmarks and reference implementations.
Axiom & Free-Parameter Ledger
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Binary analysis of the provided executable All information about the provided ‘./executable‘ must be obtained by interacting with it through its normal user interface (CLI flags, stdin/stdout, etc.). - You MUST NOT decompile ‘./executable‘ or use disassemblers (objdump, Ghidra, etc.) on it - You MUST NOT use strace, ltrace, or similar tracing/instrumentat...
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blocklist
Do task workers have access to evaluation assets?Some behavioral tests exercise the executable with input files such as images, audio files, videos, spreadsheets, or domain specific configurations. Coupled with the lack of internet access, this highlights an unfair asymmetry where evaluation uses assets that either models are unable to generate on their o...
2000
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Dependency count.Of the 200 repositories, 171 (85.5%) contain a recognized package manifest file; among these, the median repository declares 17 total dependencies (12 runtime)
contains over 850 directories. Dependency count.Of the 200 repositories, 171 (85.5%) contain a recognized package manifest file; among these, the median repository declares 17 total dependencies (12 runtime). Dependencies are counted by parsing root-level manifest files (Cargo.toml, go.mod, package.json, etc.) and summing declared packages. 29 Category Su...
2025
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