Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
Coupling Models for One-Step Discrete Generation
Generative modeling over discrete structures underpins applications across deep learning, from biological sequence design and code generation to large language models, yet generation often remains sequential, relying on …
Code GenerationKernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts
Generating high-performance GPU kernels remains challenging due to the need for both correctness and hardware-aware optimization. While large language models (LLMs) show promise in code generation, they often fail to pro…
Code GenerationPaT: Planning-after-Trial for Efficient Test-Time Code Generation
Beyond training-time optimization, scaling test-time computation has emerged as a key paradigm to extend the reasoning capabilities of Large Language Models (LLMs). However, most existing methods adopt a rigid Planning-b…
Code GenerationMage: Multi-Axis Evaluation of LLM-Generated Executable Game Scenes Beyond Compile-Pass Rate
Compile-pass rate is the dominant evaluation signal for LLM code generation, yet for multi-component domain-specific artifacts it can be actively misleading. We demonstrate this on executable game scene synthesis with a …
Code GenerationGameGen-Verifier: Parallel Keypoint-Based Verification for LLM-Generated Games via Runtime State Injection
LLM-based game generation promises to turn natural-language specifications into executable games, but progress is limited by the lack of reliable automated verification. Unlike conventional code generation, game correctn…
Code GenerationMemQ: Integrating Q-Learning into Self-Evolving Memory Agents over Provenance DAGs
Episodic memory allows LLM agents to accumulate and retrieve experience, but current methods treat each memory independently, i.e., evaluating retrieval quality in isolation without accounting for the dependency chains t…
Multimodal ReasoningCode GenerationPYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents
Local LLM-based coding agents increasingly work in settings where correctness is earned through execution feedback, persistent state, and bounded repair, not through a single fluent answer. Static retrieval, long-context…
Reinforcement LearningCode GenerationMid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models
The effectiveness of Reinforcement Learning (RL) in Large Language Models (LLMs) depends on the nature and diversity of the data used before and during RL. In particular, reasoning problems can often be approached in mul…
Reinforcement LearningMathematical ReasoningCode GenerationVeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation
Large language models can generate useful code from natural language, but their outputs come without correctness guarantees. Verifiable code generation offers a path beyond testing by requiring models to produce not only…
Program SynthesisCode GenerationRetrieval-Conditioned Topology Selection with Provable Budget Conservation for Multi-Agent Code Generation
Multi-agent LLM systems for code generation face a fundamental routing problem: the optimal orchestration topology depends on the structural complexity of the code under modification, yet existing systems select topologi…
Code GenerationCircuitFormer: A Circuit Language Model for Analog Topology Design from Natural Language Prompt
Automating analog circuit design remains a longstanding challenge in Electronic Design Automation (EDA). While Transformer-based Large Language Models (LLMs) have revolutionized software code generation, their applicatio…
Code GenerationRethinking Adapter Placement: A Dominant Adaptation Module Perspective
Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method that places trainable low-rank adapters into frozen pre-trained models. Recent studies show that using fewer LoRA adapters may still main…
parameter-efficient fine-tuningMathematical ReasoningInstruction FollowingCode GenerationLLM Translation of Compiler Intermediate Representation
GCC and LLVM underpin much of modern software infrastructure, relying on distinct Intermediate Representations (IRs) to drive optimizations and code generation. However, the semantic and structural differences between th…
Code GenerationConstraint Decay: The Fragility of LLM Agents in Backend Code Generation
Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, production-grade software requires strict adherence to structural constraints, such as a…
Code GenerationMIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes
The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. While modern Large Language Models (LLMs) already demonstrate strong to…
Code GenerationDelulu: A Verified Multi-Lingual Benchmark for Code Hallucination Detection in Fill-in-the-Middle Tasks
Large Language Models for code generation frequently produce hallucinations in Fill-in-the-Middle (FIM) tasks -- plausible but incorrect completions such as invented API methods, invalid parameters, undefined variables, …
Code GenerationBridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
Large language models (LLMs) frequently generate defective outputs in code generation tasks, ranging from logical bugs to security vulnerabilities. While these generation failures are often treated as model-level limitat…
Code GenerationDelta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs
Large language models (LLMs) show strong potential for neural architecture generation, yet existing approaches produce complete model implementations from scratch -- computationally expensive and yielding verbose code. W…
Neural Architecture SearchCode GenerationArchitectural Constraints Alignment in AI-assisted, Platform-based Service Development
AI-assisted development tools enable rapid prototyping of services but often lack awareness of architectural constraints, infrastructure dependencies, and organizational standards required in production environments. Con…
Code GenerationRAG over Thinking Traces Can Improve Reasoning Tasks
Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as math and code generation. We challenge thi…
Code Generation