Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models
Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requiring correctness like mathematical reasoning and code generation. Howev…
Reinforcement LearningMathematical ReasoningCode GenerationpySpatial: Generating 3D Visual Programs for Zero-Shot Spatial Reasoning
Multi-modal Large Language Models (MLLMs) have demonstrated strong capabilities in general-purpose perception and reasoning, but they still struggle with tasks that require spatial understanding of the 3D world. To addre…
Spatial Reasoning3D ReconstructionCode GenerationCARD: Towards Conditional Design of Multi-agent Topological Structures
Large language model (LLM)-based multi-agent systems have shown strong capabilities in tasks such as code generation and collaborative reasoning. However, the effectiveness and robustness of these systems critically depe…
Code GenerationFrom Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code
Large language models (LLMs) are increasingly used for high-stakes decision-making, yet existing approaches struggle to reconcile scalability, interpretability, and reproducibility. Black-box models obscure their reasoni…
Code GenerationRelayCaching: Accelerating LLM Collaboration via Decoding KV Cache Reuse
The increasing complexity of AI tasks has shifted the paradigm from monolithic models toward multi-agent large language model (LLM) systems. However, these collaborative architectures introduce a critical bottleneck: red…
Mathematical ReasoningGeneral KnowledgeCode GenerationDenoiseFlow: Uncertainty-Aware Denoising for Reliable LLM Agentic Workflows
Autonomous agents are increasingly entrusted with complex, long-horizon tasks, ranging from mathematical reasoning to software generation. While agentic workflows facilitate these tasks by decomposing them into multi-ste…
Mathematical ReasoningCode GenerationAre LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement
Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers often rely on LLMs to verify if code implem…
Code GenerationTopoEdge: Topology-Grounded Agentic Framework for Edge Networking Code Generation and Repair
TopoEdge is a topology-grounded, edge-deployable framework for end-to-end software-defined networking (SDN) configuration generation and repair, motivated by the brittleness of configuration artefacts under topology vari…
Graph Neural NetworkCode GenerationRLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models
Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often costly to train and exhibit poor generalization in out-of-…
Reinforcement LearningCode GenerationAgentic Scientific Simulation: Execution-Grounded Model Construction and Reconstruction
LLM agents are increasingly used for code generation, but physics-based simulation poses a deeper challenge: natural-language descriptions of simulation models are inherently underspecified, and different admissible reso…
Code GenerationCUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
GPU kernel optimization is fundamental to modern deep learning but remains a highly specialized task requiring deep hardware expertise. Despite strong performance in general programming, large language models (LLMs) rema…
Reinforcement LearningCode GenerationLearning to Generate Secure Code via Token-Level Rewards
Large language models (LLMs) have demonstrated strong capabilities in code generation, yet they remain prone to producing security vulnerabilities. Existing approaches commonly suffer from two key limitations: the scarci…
Reinforcement LearningCode GenerationParamMem: Augmenting Language Agents with Parametric Reflective Memory
Self-reflection enables language agents to iteratively refine solutions, yet often produces repetitive outputs that limit reasoning performance. Recent studies have attempted to address this limitation through various ap…
Multi-hop Question AnsweringMathematical ReasoningCode GenerationAn Evaluation of Context Length Extrapolation in Long Code via Positional Embeddings and Efficient Attention
The rapid advancement of large language models (LLMs) has led to a significant increase in automated tools in the software engineering, capable of performing various code-related tasks such as code generation, completion…
Code GenerationCode CompletionBrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning
Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.However, most existing approaches rely on task-specifi c models requiring structural modifi ca…
Code GenerationPoint CloudsID-LoRA: Efficient Low-Rank Adaptation Inspired by Matrix Interpolative Decomposition
LoRA has become a universal Parameter-Efficient Fine-Tuning (PEFT) technique that equips Large Language Models (LLMs) to adapt quickly to new tasks. However, when these models are scaled up, even the latest LoRA variants…
parameter-efficient fine-tuningMathematical ReasoningCode GenerationFrom Prompts to Performance: Evaluating LLMs for Task-based Parallel Code Generation
Large Language Models (LLM) show strong abilities in code generation, but their skill in creating efficient parallel programs is less studied. This paper explores how LLMs generate task-based parallel code from three kin…
Code GenerationManiBench: A Benchmark for Testing Visual-Logic Drift and Syntactic Hallucinations in Manim Code Generation
Traditional benchmarks like HumanEval and MBPP test logic and syntax effectively, but fail when code must produce dynamic, pedagogical visuals. We introduce ManiBench, a specialized benchmark evaluating LLM performance i…
Code GenerationWhy Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training
Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-answer reasoning. It defines success if any of $k$ independently sampled …
Mathematical ReasoningCode GenerationCodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions
The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases. However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect …
Code Generation