Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development
Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some setting…
Prompt EngineeringCode GenerationCode Generation by Differential Test Time Scaling
Test-time scaling has emerged as a promising approach for improving code generation by exploring large solution spaces at inference time. However, existing methods often rely on public test cases that are unavailable in …
Code GenerationEXG: Self-Evolving Agents with Experience Graphs
Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents remain behaviorally static, with knowledge …
Code GenerationBacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting
Quantitative backtesting is essential for evaluating trading strategies but remains hampered by high technical barriers and limited scalability. While Large Language Models (LLMs) offer a transformative path to automate …
Code GenerationEnhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning
Code reasoning refers to the task of predicting the output of a program given its source code and specific inputs. It can measure the reasoning capability of large language models (LLMs) and also benefit downstream tasks…
Reinforcement LearningMathematical ReasoningCode GenerationAutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code
Vectorization via Single Instruction, Multiple Data (SIMD) architectures is a cornerstone of high-performance computing. To fully exploit hardware potential, developers often resort to explicit vectorization using intrin…
Reinforcement LearningCode GenerationA-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback
Large Language Models (LLMs) demonstrate strong potential for automated code generation, yet their ability to iteratively refine solutions using execution feedback remains underexplored. Competitive programming offers an…
Program SynthesisCode GenerationSCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants, and a growing body of benchmarks evaluates their capabilities across knowledge retrieval, reasoning, code generation, and tool use. Th…
Code GenerationLearning When to Adapt
Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method, yet its learned correction is static: the same low-rank update is applied to every input. This input-agnostic approach creates an inevit…
parameter-efficient fine-tuningMathematical ReasoningCode GenerationRethinking Code Review in the Age of AI: A Vision for Agentic Code Review
Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual and cognitively demanding process. The rise of Artificial Intelligence (AI) coding …
Code GenerationAdaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
Many inference-time language-model pipelines combine a cheap reward signal with an expensive verifier, such as exact answer checking in mathematical reasoning or hidden-test execution in code generation. We formalize thi…
Mathematical ReasoningCode GenerationVeriCache: Turning Lossy KV Cache into Lossless LLM Inference
The large size of the KV cache has become a major bottleneck for serving LLMs with increasing context lengths. In response, many KV cache compression methods, such as token dropping and quantization, have been proposed. …
Code GenerationStop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
Large Reasoning Models (LRMs) achieve strong performance by generating long chains of thought (CoT), but often overthink, continuing to reason after a solution has already stabilized and thereby wasting tokens and increa…
Code GenerationConstrained Code Generation with Discrete Diffusion
Discrete diffusion models are a powerful, emerging paradigm for code generation. They construct programs through iterative refinement of partially corrupted token sequences and enable parallel token refinement. Important…
Code GenerationNGM: A Plug-and-Play Training-Free Memory Module for LLMs
Recent studies introduce conditional memory modules that decouple knowledge storage from neural computation, enabling more direct knowledge access. Compared to MoE, which relies on dynamic computation paths, explicit loo…
Code GenerationTask Abstention for Large Language Models in Code Generation
Large language models (LLMs) have revolutionized automated code generation. One serious concern, however, is the so-called ``hallucination'', i.e., LLMs may generate seemingly plausible but functionally incorrect code. I…
Code GenerationHyDRA: Hybrid Dynamic Routing Architecture for Heterogeneous LLM Pools
Production LLM deployments increasingly maintain heterogeneous model pools spanning order-of-magnitude cost differences. Existing routers make binary strong-vs-weak decisions and couple learned parameters to specific mod…
Code GenerationBeyond Execution: Static-Analysis Rewards and Hint-Conditioned Diffusion RL for Code Generation
Reinforcement Learning (RL) is an important paradigm for aligning Diffusion Language Models (DLMs) toward functional correctness in code generation. However, these models often encounter a ``capability cliff'' on complex…
Reinforcement LearningCode GenerationSee Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generation
Large language models can generate executable code for educational animations, but the resulting renders often exhibit visual defects, including element overlap, misalignment, and broken animation continuity. These defec…
Code GenerationSyntax Without Semantics: Teaching Large Language Models to Code in an Unseen Language
Large language models (LLMs) achieve high pass rates on code generation benchmarks, yet whether they can transfer this ability to languages absent from pretraining remains poorly understood. We introduce PyLang, a minima…
Multi-Task LearningCode Generation