Structurally rich task descriptions make LLMs robust to prompt under-specification, and under-specification can enhance code correctness by disrupting misleading lexical or structural cues.
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6 Pith papers cite this work. Polarity classification is still indexing.
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EnvGraph improves executable repository-level code generation by jointly modeling external dependencies and internal references through a dual-layer environment representation and targeted iterative alignment.
SpecValidator detects lexical vagueness, under-specification, and syntax-formatting defects in LLM code-generation prompts with F1 0.804, outperforming GPT-5-mini and Claude Sonnet 4, and shows that under-specification is the most damaging defect type while richer benchmarks are more resilient.
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
A3D is an agentic AI system that automates end-to-end hardware accelerator design for complex applications like LAMMPS and QMCPACK with no human intervention.
REA-Coder improves LLM code generation by iteratively aligning requirements with model understanding and verifying outputs against the aligned spec.
citing papers explorer
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When Prompt Under-Specification Improves Code Correctness: An Exploratory Study of Prompt Wording and Structure Effects on LLM-Based Code Generation
Structurally rich task descriptions make LLMs robust to prompt under-specification, and under-specification can enhance code correctness by disrupting misleading lexical or structural cues.
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Toward Executable Repository-Level Code Generation via Environment Alignment
EnvGraph improves executable repository-level code generation by jointly modeling external dependencies and internal references through a dual-layer environment representation and targeted iterative alignment.
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Defective Task Descriptions in LLM-Based Code Generation: Detection and Analysis
SpecValidator detects lexical vagueness, under-specification, and syntax-formatting defects in LLM code-generation prompts with F1 0.804, outperforming GPT-5-mini and Claude Sonnet 4, and shows that under-specification is the most damaging defect type while richer benchmarks are more resilient.
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REAgent: Requirement-Driven LLM Agents for Software Issue Resolution
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
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A3D: Agentic AI flow for autonomous Accelerator Design
A3D is an agentic AI system that automates end-to-end hardware accelerator design for complex applications like LAMMPS and QMCPACK with no human intervention.
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Bridging the Gap between User Intent and LLM: A Requirement Alignment Approach for Code Generation
REA-Coder improves LLM code generation by iteratively aligning requirements with model understanding and verifying outputs against the aligned spec.