Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation
AI coding assistants increasingly generate code alongside tests. How developers structure test code, whether inline with the implementation or in separate blocks, has traditionally been a matter of testing philosophy. We…
Code GenerationRiver-LLM: Large Language Model Seamless Exit Based on KV Share
Large Language Models (LLMs) have demonstrated exceptional performance across diverse domains but are increasingly constrained by high inference latency. Early Exit has emerged as a promising solution to accelerate infer…
Mathematical ReasoningCode GenerationLayer-wise MoE Routing Locality under Shared-Prefix Code Generation: Token-Identity Decomposition and Compile-Equivalent Fork Redundancy
In LLM-based code generation, multiple code candidates are often generated in parallel from the same prompt -- for example, in best-of-N sampling or multi-candidate code completion. These requests can share KV caches thr…
Code GenerationCode CompletionProbabilistic Programs of Thought
LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to reason about code, generate code for a given specification, or reason u…
Mathematical ReasoningCode GenerationSOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization
Automated simulator construction requires distributional fidelity, distinguishing it from generic code generation. We identify two failure modes in long-horizon LLM agents: contextual drift and optimization instability a…
Code GenerationProject Prometheus: Bridging the Intent Gap in Agentic Program Repair via Reverse-Engineered Executable Specifications
The transition from neural machine translation to agentic workflows has revolutionized Automated Program Repair (APR). However, existing agents, despite their advanced reasoning capabilities, frequently suffer from the `…
Machine TranslationCode GenerationProgram RepairAIRA: AI-Induced Risk Audit: A Structured Inspection Framework for AI-Generated Code
Practitioners have reported a directional pattern in AI-assisted code generation: AI-generated code tends to fail quietly, preserving the appearance of functionality while degrading or concealing guarantees. This paper i…
Code GenerationBias in the Loop: Auditing LLM-as-a-Judge for Software Engineering
Large Language Models are increasingly used as judges to evaluate code artifacts when exhaustive human review or executable test coverage is unavailable. LLM-judge is increasingly relevant in agentic software engineering…
Code GenerationCode RepairStability-Weighted Decoding for Diffusion Language Models
Diffusion large language models (dLLMs) enable parallel text generation by iteratively denoising a fully masked sequence, unmasking a subset of masked tokens at each step. Existing decoding strategies rely on static conf…
Mathematical ReasoningCode GenerationText GenerationMajority Voting for Code Generation
We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies a representative solution from multiple generations using their run…
Reinforcement LearningCode GenerationHYPERHEURIST: A Simulated Annealing-Based Control Framework for LLM-Driven Code Generation in Optimized Hardware Design
Large Language Models (LLMs) have shown promising progress for generating Register Transfer Level (RTL) hardware designs, largely because they can rapidly propose alternative architectural realizations. However, single-s…
Code GenerationCodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval
Code search, framed as information retrieval (IR), underpins modern software engineering and increasingly powers retrieval-augmented generation (RAG), improving code discovery, reuse, and the reliability of LLM-based cod…
Information RetrievalVisual GroundingCode GenerationCode SearchACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization
Large Language Models (LLMs) excel at code generation but remain heavily reliant on large-scale annotated solutions and verification-based supervision, which constrains scalability and hinders sustained self-improvement.…
Code GenerationLLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
Neural Architecture Search (NAS) aims to automatically discover high-performing deep neural network (DNN) architectures. However, conventional algorithm-driven NAS relies on carefully hand-crafted search spaces to ensure…
Neural Architecture SearchCode GenerationAdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation
Recent large language model (LLM) agents have shown promise in using execution feedback for test-time adaptation. However, robust self-improvement remains far from solved: most approaches still treat each problem instanc…
Test-time AdaptationCode GenerationSurgical Repair of Insecure Code Generation in LLMs
Large language models write production code, and yet they routinely introduce well-known vulnerabilities. We show that this is not a knowledge deficit: the same models that generate insecure code, correctly identify and …
Code GenerationVeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs
Generating synthesizable Verilog for large, hierarchical hardware designs remains a significant challenge for large language models (LLMs), which struggle to replicate the structured reasoning that human experts employ w…
Code GenerationMARS$^2$: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation
Reinforcement learning (RL) paradigms have demonstrated strong performance on reasoning-intensive tasks such as code generation. However, limited trajectory diversity often leads to diminishing returns, which constrains …
Reinforcement LearningCode GenerationStoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation
Effective code generation requires both model capability and a problem representation that carefully structures how models reason and plan. Existing approaches augment reasoning steps or inject specific structure into ho…
Code GenerationExploring LLM-based Verilog Code Generation with Data-Efficient Fine-Tuning and Testbench Automation
Recent advances in large language models have improved code generation, but their use in hardware description languages is still limited. Moreover, training data and testbenches for these models are often scarce. This pa…
Code Generation