Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation
Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generation, where the goal is to generate code using APIs from private libraries. Exi…
Code GenerationSEMAG: Self-Evolutionary Multi-Agent Code Generation
Large Language Models (LLMs) have made significant progress in handling complex programming tasks. However, current methods rely on manual model selection and fixed workflows, which limit their ability to adapt to changi…
Code GenerationDOS: Dependency-Oriented Sampler for Masked Diffusion Language Models
Masked diffusion language models (MDLMs) have recently emerged as a new paradigm in language modeling, offering flexible generation dynamics and enabling efficient parallel decoding. However, existing decoding strategies…
Mathematical ReasoningCode GenerationCode-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning
Reinforcement learning for code generation relies on verifiable rewards from unit test pass rates. Yet high-quality test suites are scarce, existing datasets offer limited coverage, and static rewards fail to adapt as mo…
Reinforcement LearningCode GenerationAn Agentic Evaluation Framework for AI-Generated Scientific Code in PETSc
While large language models have significantly accelerated scientific code generation, comprehensively evaluating the generated code remains a major challenge. Traditional benchmarks reduce evaluation to test-case matchi…
Code GenerationCoding with Eyes: Visual Feedback Unlocks Reliable GUI Code Generating and Debugging
Recent advances in Large Language Model (LLM)-based agents have shown remarkable progress in code generation. However, current agent methods mainly rely on text-output-based feedback (e.g. command-line outputs) for multi…
Code GenerationSVD Contextual Sparsity Predictors for Fast LLM Inference
Contextual sparsity is one of the approaches used to reduce computational complexity in the inference process of large language models (LLMs). Existing techniques for efficient LLM inference acceleration based on context…
Code GenerationEvolveCoder: Evolving Test Cases via Adversarial Verification for Code Reinforcement Learning
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving code generation in large language models, but its effectiveness is limited by weak and static verification signals in existing c…
Reinforcement LearningCode GenerationDesign-Specification Tiling for ICL-based CAD Code Generation
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet they underperform on domain-specific tasks such as Computer-Aided Design (CAD) code generation due to scarce training data. I…
Code GenerationA Domain-Specific Language for LLM-Driven Trigger Generation in Multimodal Data Collection
Data-driven systems depend on task-relevant data, yet data collection pipelines remain passive and indiscriminate. Continuous logging of multimodal sensor streams incurs high storage costs and captures irrelevant data. T…
Code GenerationMemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels
Reinforcement learning has emerged as a powerful paradigm for improving large language model (LLM) reasoning, where rollouts are sampled from the policy and reward signals computed on those rollouts are used to update th…
Reinforcement LearningQuestion AnsweringCode GenerationLongFlow: Efficient KV Cache Compression for Reasoning Models
Recent reasoning models such as OpenAI-o1 and DeepSeek-R1 have shown strong performance on complex tasks including mathematical reasoning and code generation. However, this performance gain comes with substantially longe…
Mathematical ReasoningCode GenerationDOne: Decoupling Structure and Rendering for High-Fidelity Design-to-Code Generation
While Vision Language Models (VLMs) have shown promise in Design-to-Code generation, they suffer from a "holistic bottleneck-failing to reconcile high-level structural hierarchy with fine-grained visual details, often re…
Code GenerationCreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges
The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve. However, the progress of …
Code GenerationMobileKernelBench: Can LLMs Write Efficient Kernels for Mobile Devices?
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile devices remains largely unexplored. In this work, we extend th…
Code GenerationQAQ: Bidirectional Semantic Coherence for Selecting High-Quality Synthetic Code Instructions
Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult to detect with current metrics. Existing data selection methods like Inst…
Code GenerationKernelFoundry: Hardware-aware evolutionary GPU kernel optimization
Optimizing GPU kernels presents a significantly greater challenge for large language models (LLMs) than standard code generation tasks, as it requires understanding hardware architecture, parallel optimization strategies…
Code GenerationUtility Function is All You Need: LLM-based Congestion Control
Congestion is a critical and challenging problem in communication networks. Congestion control protocols allow network applications to tune their sending rate in a way that optimizes their performance and the network uti…
Code GenerationH2LooP Spark Preview: Continual Pretraining of Large Language Models for Low-Level Embedded Systems Code
Large language models (LLMs) demonstrate strong code generation abilities in general-purpose programming languages but remain limited in specialized domains such as low-level embedded systems programming. This domain inv…
Continual PretrainingCode GenerationCode CompletionArtificial Intelligence as a Catalyst for Innovation in Software Engineering
The rapid evolution and inherent complexity of modern software requirements demand highly flexible and responsive development methodologies. While Agile frameworks have become the industry standard for prioritizing itera…
Code Generation