From I/O to Code with Discovery Agent

• URL: http://arxiv.org/abs/2605.15334v1
• Date: Thu, 14 May 2026 18:57:32 GMT
• Title: From I/O to Code with Discovery Agent
• Abstract summary: We propose DIO-Agent, a discovery agent for IO2Code.<n>Our method frames IO2Code as an evolutionary search over discrete program space.<n>Extensive experiments show that DIO-Agent consistently outperforms both traditional program-by-example method and SOTA evolution-agent baselines.
• Score: 103.88427301265669
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• The Imitation Game: When LLMs Learn to Reason Like Programs via Code-Centric Reasoning Data Synthesis [50.473217152006875]
  MIMIC is a framework that leverages executable code as a rigorous medium for reasoning data synthesis.<n>MIMIC transforms algorithms into verifiable reasoning trajectories through narrative fusion, code-guided test synthesis, and dynamic code instrumentation.<n>Our method significantly elevates accuracy across general reasoning, complex mathematical benchmarks, and fine-grained deterministic tasks.
  arXiv  Detail & Related papers  (2026-09-13T17:04:16Z)
• DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode [56.14374797825548]
  We introduce LLM-based pseudocode execution, which grounds prediction on more error-resilient pseudocode.<n>We propose DuET, a dual-execution framework that combines both approaches by functional majority voting.<n>On LiveCodeBench, DuET achieves the state-of-the-art performance, improving Pass@1 by 13.6 pp.
  arXiv  Detail & Related papers  (2026-04-13T14:18:58Z)
• From Paper to Program: Accelerating Quantum Many-Body Algorithm Development via a Multi-Stage LLM-Assisted Workflow [4.866193347313755]
  Large language models (LLMs) can generate code rapidly but remain unreliable for scientific algorithms.<n>We introduce a multi-stage LLM-assisted workflow that separates theory extraction, formal specification, and code implementation.
  arXiv  Detail & Related papers  (2026-04-05T12:12:54Z)
• Breaking Validity-Induced Boundaries to Expand Algorithm Search Space: A Two-Stage AST-Based Operator for LLM-Driven Automated Heuristic Evolution [0.0]
  We propose a two-stage, structure-based evolutionary operator for LLM-AHD.<n>In the first stage, our approach directly performs crossover and mutation on the Abstract Syntax Trees (ASTs) of code.<n>In the second stage, the LLM is employed to repair these invalids into executable, high-quality code.
  arXiv  Detail & Related papers  (2026-04-03T07:35:43Z)
• DUET: Agentic Design Understanding via Experimentation and Testing [6.787641711048685]
  DUET is a general methodology for developing Design Understanding via Experimentation and Testing.<n>It iteratively generates hypotheses, tests them with EDA tools, and integrates the results to build a bottom-up understanding of the design.<n>We show that DUET improves AI agent performance on formal verification, when compared to a baseline flow without experimentation.
  arXiv  Detail & Related papers  (2025-12-06T02:16:28Z)
• SciML Agents: Write the Solver, Not the Solution [69.5021018644143]
  We introduce two new datasets: a diagnostic dataset of adversarial "misleading" problems; and a large-scale benchmark of 1,000 diverse ODE tasks.<n>We evaluate open- and closed-source LLM models along two axes: (i) unguided versus guided prompting with domain-specific knowledge; and (ii) off-the-shelf versus fine-tuned variants.<n>Preliminary results indicate that careful prompting and fine-tuning can yield a specialized LLM agent capable of reliably solving simple ODE problems.
  arXiv  Detail & Related papers  (2025-09-12T02:53:57Z)
• DecoRTL: A Run-time Decoding Framework for RTL Code Generation with LLMs [0.0]
  We show that large language models (LLMs) exhibit low confidence in regions of structural ambiguity or semantic complexity.<n>We introduce DecoRTL, a novel run-time decoding strategy, that is both syntax-aware and contrastive for RTL code generation.<n>Our approach operates entirely at inference time without requiring any additional model fine-tuning.
  arXiv  Detail & Related papers  (2025-07-03T01:17:44Z)
• QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation [51.393569044134445]
  Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification.<n> Extending RLVR to automatically generating hardware description languages (HDLs) like Verilog from natural-language (NL) specifications, however, poses three key challenges.<n>We introduce CodeV-R1, an RLVR framework for training Verilog generation LLMs.
  arXiv  Detail & Related papers  (2025-05-30T03:51:06Z)
• Program Semantic Inequivalence Game with Large Language Models [20.43560028315856]
  Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.<n>In this work, we explore a method to synthetically generate code reasoning training data based on a semantic inequivalence game SInQ.<n>We prove that this setup enables theoretically unlimited improvement through self-play in the limit of infinite computational resources.
  arXiv  Detail & Related papers  (2025-05-02T20:03:35Z)
• Case2Code: Scalable Synthetic Data for Code Generation [105.89741089673575]
  Large Language Models (LLMs) have shown outstanding breakthroughs in code generation.<n>Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs.<n>We propose a textbfCase2Code task by exploiting the expressiveness and correctness of programs.
  arXiv  Detail & Related papers  (2024-07-17T11:35:00Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.