Papers Code Generation
“Code Generation” 태그가 달린 논문 3,062편 · 필터 해제
Retrofitting Code Using LLMs to Support Exceptional Behavior
Exception Related Code (ERC), which includes throw statements, conditions (if statements) that guard those throw statements, and try/catch blocks, is an essential component of software systems, allowing developers to det…
Code GenerationΦ-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?
Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in developing and optimizing the very infrastructure that powers them. …
Code GenerationRISE: Recursive Improvement via Self-Extrapolating Policy Distillation
On-policy distillation (OPD) provides dense, per-token supervision for language model post-training, but its effectiveness is bottlenecked by teacher quality: external teachers suffer from distribution mismatch, while se…
Mathematical ReasoningCode GenerationSubstrate-Aware AI Agents: Execution Context as a First-Class Input
Autonomous AI agents increasingly select actions in environments whose memory, execution-time, runtime, compute, and operational constraints determine what counts as a suitable plan. We call the absence of this execution…
Code GenerationAutoLR: Automating the Path from Research to Launch Review in Industrial Recommender Systems
Improving an industrial recommender is an iterative research-and-engineering process rather than a direct path from idea to deployment. In \textbf{DASHEN, NetEase's gaming-community app}, algorithm engineers typically id…
Code GenerationBeyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle
AI coding systems are moving from autocomplete and chat toward agents that can inspect repositories, edit multiple files, run tools, write tests, open pull requests, and work for long periods with limited supervision. Th…
Code GenerationDynamic Adaptation of the LLM Context for Generating Routines with Coupled Semantics
LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one routine is defined by the runtime behavior of another, a relationship that cannot be resolved from textual desc…
Code GenerationDistilled Continuous Diffusion Language Models Can Write Code in Few Steps---or One
Language generation is almost universally treated as a sequential process: autoregressive models emit one token at a time, while diffusion language models replace token-level seriality with a long trajectory of iterative…
Code GenerationPaperCompiler: Faithful Paper-to-Code Generation via Repository-Level Specification Compilation
Faithfully translating research papers into repository-level implementations remains challenging because papers often describe methods at a high level, leave implementation assumptions implicit, and require generated rep…
Code GenerationExecRetrieval: Measuring the Functional-Correctness Gap in Code-Embedding Retrieval
Embedding-based code retrieval is a core component of coding agents and retrieval-augmented code generation, where retrieving correct code matters more than retrieving lexically similar code. Existing code-retrieval benc…
Code GenerationHidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text
Synthetic data is increasingly used to train large language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that models can inherit behavioral traits from…
Code GenerationDARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging
Model merging combines multiple task-specific fine-tuned LLMs into a single multi-task model without additional training. However, merged models are known to suffer from representation bias: systematic drift between the …
Mathematical ReasoningInstruction FollowingCode GenerationRubric-to-Code Credit Assignment for Reinforcement Learning
Interactive web application generation requires models to produce usable HTML, CSS, and JavaScript applications from natural language requests. Unlike conventional code generation, application quality depends on multiple…
Reinforcement LearningCode GenerationProgRouter: Online Progress-Guided Orchestration for Multi-Agent LLM Workflows under Quality-Cost Tradeoffs
Multi-agent large language model (LLM) workflows have emerged as a powerful paradigm for solving complex, open-ended tasks through collaborative reasoning among specialized LLM agents, but they incur substantial operatin…
Mathematical ReasoningQuestion AnsweringCode GenerationMACGen: Toward Functionally Correct and Secure Code Generation via Multi-Agent Collaboration
Despite their strong ability to generate code, large language models often fail to produce secure code, as their outputs frequently contain security vulnerabilities. Secure code generation is inherently challenging becau…
Code GenerationRubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation
Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify a fundamental obstacle, termed visual repair coupling: a local code…
Code GenerationCan Coding Agents Build Robust Baselines? A Skill-Based Approach for Automating the Medical Imaging Model-Development Pipeline
Developing competitive deep learning baselines for medical imaging remains a highly iterative process requiring literature review, implementation, experimentation, and expert refinement. Existing automation approaches ty…
Domain GeneralizationCode GenerationLongWoF-Bench: Evaluating EvoMap Genes for Verifiable Long-Workflow Tasks
Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and producing artifacts that satisfy strict end-to-end verification. Yet succes…
Mathematical ReasoningCode GenerationMARS: Multi-Specialist LLM Relay System for Competitive Programming
Large Language Models excel at code generation, yet competitive programming exposes a persistent failure mode: existing multi-agent pipelines distribute work over generic planner, coder, and debugger roles and delegate t…
Code GenerationTESTNAV: Pareto-Guided Search for Compositional Robustness Testing
Deep learning models remain vulnerable to real-world input perturbations, especially when multiple corruptions co-occur in the same input (e.g., brightness shifts and motion blur). Compositional testing reveals these int…
Code Generation