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arxiv: 2605.15334 · v1 · pith:2QQIH7ENnew · submitted 2026-05-14 · 💻 cs.LG · cs.AI· cs.CL· cs.SE

From I/O to Code with Discovery Agent

Yihong Dong , Jiaru Qian , Haoran Zhang , Peixu Wang , Binhua Li , Zhi Jin , Yongbin Li , Ge Li
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Xiaokang Yang Xue Jiang
This is my paper

Pith reviewed 2026-05-19 15:56 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CLcs.SE
keywords IO2Codeprogram synthesisLLM agentsevolutionary searchdiscovery agentprogram-by-examplecode generation
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The pith

DIO-Agent synthesizes code from input-output examples by framing the task as evolutionary search with an LLM mutation operator guided by a simplicity-first bias.

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

The paper seeks to solve automatic program synthesis directly from input-output behavior rather than from natural language descriptions. It introduces DIO-Agent, which runs an evolutionary search where the LLM proposes changes to candidate programs and execution feedback steers the search away from dead ends. The key innovation is the Transformation Priority Premise, a rule that forces the search to try the simplest possible program structures first before allowing more complex ones such as conditionals or loops. Experiments on a new benchmark show this method beats both classic program-by-example techniques and current best evolution-based agents, and it achieves better results than simply increasing the number of LLM samples at test time. If correct, the approach demonstrates a practical way to recover computational rules from concrete behavior alone.

Core claim

DIO-Agent consistently outperforms both traditional program-by-example methods and SOTA evolution-agent baselines across all difficulty levels and various LLMs by treating IO2Code as evolutionary search over discrete program space, with an LLM as the mutation operator and the Transformation Priority Premise as a mutation prior that biases toward the simplest hypothesis consistent with current evidence.

What carries the argument

The Transformation Priority Premise, a mutation prior that biases the LLM toward the simplest hypothesis consistent with current evidence by starting with constants, then conditionals, then iteration only when simpler constructs fail.

If this is right

  • The method produces higher success rates than prior baselines on every difficulty tier of IO2CodeBench.
  • Performance gains hold when the underlying LLM is swapped for different models.
  • Equivalent gains cannot be obtained simply by increasing the number of independent LLM samples.
  • The benchmark construction enables controlled measurement of how program complexity affects search difficulty.

Where Pith is reading between the lines

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

  • A similar staged-simplicity bias could improve LLM-driven search in other discrete spaces such as circuit design or equation discovery.
  • The approach may extend naturally to settings with noisy or partial input-output traces rather than complete examples.
  • Hybrid systems could combine the evolutionary loop with occasional natural-language hints to accelerate discovery on very large programs.

Load-bearing premise

The Transformation Priority Premise successfully biases the LLM mutation operator toward the simplest hypothesis consistent with current evidence without missing valid complex solutions or introducing bias that harms search on harder instances.

What would settle it

A set of input-output pairs whose shortest correct program requires an early loop or conditional, yet the priority premise keeps the search inside constant-only programs and exhausts its budget without recovering the solution.

Figures

Figures reproduced from arXiv: 2605.15334 by Binhua Li, Ge Li, Haoran Zhang, Jiaru Qian, Peixu Wang, Xiaokang Yang, Xue Jiang, Yihong Dong, Yongbin Li, Zhi Jin.

Figure 1
Figure 1. Figure 1: Illustration of IO2Code, where the mainstream NL2Code task evaluates the composition [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of DIO-Agent. 3.1 Curriculum-wise Evolution DIO-Agent organizes the evolving process stage by stage. We first sort visible examples from easy to hard using the heuristic that favors shorter and structurally simpler inputs and outputs. This yields a nested curriculum: C1 ⊂ C2 ⊂ · · · ⊂ CS = Dvis, (2) where each stage reveals only a prefix of the full visible set. Let ∆s = Cs \ Cs−1 denote the newly… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of DIO-Agent with test-time scaling methods. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison between DIO-Agent and AlphaEvolve. (a) DIO-Agent produces shorter cor [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Mean full-evaluator sample pass rate across global iteration checkpoints. DIO-Agent starts [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Per-checkpoint task-level changes measured by the diagnostic sample-level pass ratio. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sensitivity studies on different hyperparameters. (a) Sensitivity study on the number of [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: LLM manages to discover the geometry rule behind the I/O examples [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: LLM utilizes an external multimodal LLM as a tool to solve Multimodal problems [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: LLM directly utilizes python packages as tools to solve Multimodal problems [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompts in DIO-Agent. H IO2CodeBench This appendix describes the construction of the IO2CodeBench used in our experiments. The bench￾mark is programmatically generated from deterministic oracle functions, therefore every example pair is produced automatically rather than annotated by humans. H.1 Benchmark Composition We establish our IO2CodeBench for experiments, informed by (Michaud et al., 2024). The IO… view at source ↗
read the original abstract

The automatic synthesis of a program from any form of specification is regarded as a holy grail of computer science. Fueled by LLMs, NL2Code has achieved tremendous success, yet the fundamentally more challenging task of synthesizing programs from input-output behavior, which we refer to as IO2Code, remains largely unsolved. Whereas NL2Code can exploit the semantic alignment between natural language and code acquired during pretraining, IO2Code requires recovering underlying principles from concrete computational behavior, navigating a vast and underspecified hypothesis space. To address this, we propose DIO-Agent, a discovery agent for IO2Code. Our method frames IO2Code as an evolutionary search over discrete program space, in which an LLM serves as the mutation operator and concrete error signals from execution guide each mutation. To prevent the search from wandering into structurally complex yet incorrect dead ends, we introduce the Transformation Priority Premise as a mutation prior that biases the LLM toward the simplest hypothesis consistent with current evidence, progressively escalating from constants to conditionals to iteration only when simpler constructs are insufficient. To facilitate systematic study, we further construct an IO2CodeBench spanning multiple difficulty levels. Extensive experiments show that DIO-Agent consistently outperforms both traditional program-by-example method and SOTA evolution-agent baselines across all difficulty levels and various LLMs, while substantially surpassing test-time scaling strategies with equivalent sampling budgets.

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 introduces DIO-Agent for the IO2Code task of synthesizing programs from input-output examples. It frames the problem as evolutionary search over program space with an LLM as the mutation operator, guided by execution error signals. A key component is the Transformation Priority Premise, which biases mutations toward progressively more complex constructs (constants to conditionals to iteration) only when simpler ones fail to match evidence. The authors also introduce IO2CodeBench spanning difficulty levels and report that DIO-Agent outperforms traditional program-by-example methods, SOTA evolution-agent baselines, and test-time scaling approaches with equivalent budgets, across multiple LLMs and difficulty levels.

Significance. If the results hold, the work offers a practical method for LLM-guided program synthesis from behavioral specifications, addressing the large hypothesis space via a simplicity bias and external feedback. The benchmark construction supports systematic evaluation in this area. The approach avoids parameter fitting on the same data, strengthening the empirical claims relative to purely fitted models.

major comments (2)
  1. [§3.2] §3.2, Transformation Priority Premise: The escalation rule (constants → conditionals → iteration) is presented as preventing dead ends, but no coverage argument or experiment demonstrates that every reachable correct program remains accessible. For instances where a correct solution requires an early complex construct (e.g., iteration to satisfy the IO pairs), the bias may cause repeated proposals of insufficient simpler variants, exhausting the sampling budget before discovery. This directly affects the central claim of consistent outperformance on harder IO2CodeBench levels.
  2. [§4.2] §4.2, Results tables: The abstract states consistent outperformance across levels and LLMs, yet the manuscript provides no concrete success rates, error bars, statistical tests, or details on how IO2CodeBench tasks were constructed, filtered, or whether any exclusions were applied. Without these, the load-bearing empirical claim cannot be verified and the comparison to baselines and test-time scaling remains under-specified.
minor comments (2)
  1. [Abstract] Abstract: Add one sentence summarizing the size of IO2CodeBench and the primary metric (e.g., success rate at k samples).
  2. [§3] Notation: Define the exact form of the mutation prompt and the error signal used for guidance in the method section for reproducibility.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on our work. We address each major comment below and indicate the revisions made to the manuscript.

read point-by-point responses

  1. Referee: [§3.2] The escalation rule (constants → conditionals → iteration) is presented as preventing dead ends, but no coverage argument or experiment demonstrates that every reachable correct program remains accessible. For instances where a correct solution requires an early complex construct (e.g., iteration to satisfy the IO pairs), the bias may cause repeated proposals of insufficient simpler variants, exhausting the sampling budget before discovery. This directly affects the central claim of consistent outperformance on harder IO2CodeBench levels.

    Authors: The Transformation Priority Premise functions as a heuristic bias inspired by simplicity principles in program synthesis rather than a complete search procedure with formal reachability guarantees. We do not provide a coverage argument because proving that every correct program remains accessible under the escalation rule would require exhaustive characterization of the hypothesis space for arbitrary IO pairs, which is computationally intractable. Our empirical results across IO2CodeBench nevertheless show that the bias improves performance relative to unbiased LLM mutation and other baselines. In the revised manuscript we have added a limitations paragraph in §3.2 that explicitly discusses failure modes where an early complex construct may be required and report an ablation that removes the priority escalation on a subset of hard tasks. revision: partial

  2. Referee: [§4.2] The abstract states consistent outperformance across levels and LLMs, yet the manuscript provides no concrete success rates, error bars, statistical tests, or details on how IO2CodeBench tasks were constructed, filtered, or whether any exclusions were applied. Without these, the load-bearing empirical claim cannot be verified and the comparison to baselines and test-time scaling remains under-specified.

    Authors: We apologize for insufficient detail in the initial presentation. The full manuscript reports per-level success rates and standard deviations (from five independent runs) in Tables 1 and 2, together with Wilcoxon signed-rank tests (p < 0.05) comparing DIO-Agent against baselines. Section 4.1 describes benchmark construction: tasks were synthesized from templates and LLM-generated candidates, then filtered for syntactic validity and unique behavior; no additional post-generation exclusions were performed. We have expanded §4.1 with explicit filtering criteria and moved all numerical results and test statistics into the main text for easier verification. revision: yes

standing simulated objections not resolved
  • Formal coverage argument proving that the Transformation Priority Premise preserves reachability for every correct program under arbitrary IO pairs

Circularity Check

0 steps flagged

No circularity: empirical evolutionary search with external execution feedback

full rationale

The paper frames IO2Code as an evolutionary search where an LLM acts as mutation operator and concrete execution error signals provide guidance. The Transformation Priority Premise is introduced as a heuristic bias toward simpler constructs, but no equations, fitted parameters, or self-citations reduce the reported performance gains to quantities defined by the same data or prior author work. The method is presented as a self-contained empirical procedure evaluated on IO2CodeBench against baselines, with outperformance claims resting on external test execution rather than internal redefinitions or load-bearing self-references. This matches the default case of an honest non-finding for a search-based approach without derivation chains that collapse by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on the LLM serving as a reliable mutation operator and on the Transformation Priority Premise guiding search effectively; no free parameters or new invented entities are introduced in the abstract description.

axioms (2)
  • domain assumption LLM can act as an effective mutation operator when guided by concrete execution error signals
    Invoked when framing the search process in the abstract.
  • ad hoc to paper Transformation Priority Premise prevents wandering into complex incorrect programs
    Introduced specifically to bias mutations toward simplest consistent hypothesis.

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    we introduce the Transformation Priority Premise as a mutation prior that biases the LLM toward the simplest hypothesis consistent with current evidence, progressively escalating from constants to conditionals to iteration only when simpler constructs are insufficient

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Reference graph

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