Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
RefEvo: Agentic Design with Co-Evolutionary Verification for Agile Reference Model Generation
As the complexity of System-on-Chip (SoC) designs grows, the shift-left paradigm necessitates the rapid development of high-fidelity reference models (typically written in SystemC) for early architecture exploration and …
Code GenerationAligned Multi-View Scripts for Universal Chart-to-Code Generation
Chart-to-code generation converts a chart image into an executable plotting script, enabling faithful reproduction and editable visualizations. Existing methods are largely Python-centric, limiting practical use and over…
Code GenerationDepthKV: Layer-Dependent KV Cache Pruning for Long-Context LLM Inference
Long-context reasoning is a critical capability of large language models (LLMs), enabling applications such as long-document understanding, summarization, and code generation. However, efficient autoregressive inference …
Code GenerationLeveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
Large language models (LLMs) perform strongly on general-purpose code generation, yet their applicability to enterprise domain-specific languages (DSLs) remains underexplored, especially for repository-scale change gener…
parameter-efficient fine-tuningResponse GenerationCode GenerationDefective Task Descriptions in LLM-Based Code Generation: Detection and Analysis
Large language models are widely used for code generation, yet they rely on an implicit assumption that the task descriptions are sufficiently detailed and well-formed. However, in practice, users may provide defective d…
Code GenerationLarge Language Models Explore by Latent Distilling
Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limiting semantic exploration. In this paper,…
Code GenerationLarge Language Models for Multilingual Code Intelligence: A Survey
Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Python, with weaker performance in languages like Rust and OCaml. Since …
Code TranslationCode GenerationContext-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49%
AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product decisions that are invisible in the source code alone. We introduce a con…
Code GenerationDo Prompt-Elicited Trajectories Reflect Training-Time Reward Hacking? A Systematic Study on Monitoring Trainig-Time Reward Hacking in Code Generation
Reward hacking in code generation, where models exploit evaluation loopholes to obtain high reward without correctly solving the intended task, poses a critical challenge for Reinforcement Learning (RL) and the deploymen…
Reinforcement LearningCode GenerationPhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement
Physics-aware symbolic simulation of 3D scenes is critical for robotics, embodied AI, and scientific computing, requiring models to understand natural language descriptions of physical phenomena and translate them into e…
Code GenerationTandem: Riding Together with Large and Small Language Models for Efficient Reasoning
Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit step-by-step reasoning before generating final answers. While such app…
Mathematical ReasoningCode GenerationBenchmarking Testing in Automated Theorem Proving
Recent advances in large language models (LLMs) have shown promise in formal theorem proving, yet evaluating semantic correctness remains challenging. Existing evaluations rely on indirect proxies such as lexical overlap…
Automated Theorem ProvingCode GenerationNo Test Cases, No Problem: Distillation-Driven Code Generation for Scientific Workflows
Existing multi-agent Large Language Model (LLM) frameworks for code generation typically use execution feedback and improve iteratively using Input/Output (I/O) test cases. However, this does not work for scientific work…
Knowledge DistillationCode GenerationHidden States Know Where Reasoning Diverges: Credit Assignment via Span-Level Wasserstein Distance
Group Relative Policy Optimization (GRPO) performs coarse-grained credit assignment in reinforcement learning with verifiable rewards (RLVR) by assigning the same advantage to all tokens in a rollout. Process reward mode…
Reinforcement LearningMathematical ReasoningCode GenerationAn Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code
Large language models (LLMs) have demonstrated strong performance on a wide range of software engineering tasks, including code generation and analysis. However, most prior work relies on cloud-based models or specialize…
Code GenerationCharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution
Chart-to-code generation demands strict visual precision and syntactic correctness from Vision-Language Models (VLMs). However, existing approaches are fundamentally constrained by data-centric limitations: despite the a…
Code GenerationT2MM: An LLM Supported Architecture For Inquiry-Based Modeling
Model Construction is a foundational practice in science learning that relies on visualization and interactivity. Large Language Models, increasingly augmented with multimodal capabilities, have been integrated in educat…
Code GenerationNavigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA
This paper introduces the task of analytical question answering over large, semi-structured document collections. We present MuDABench, a benchmark for multi-document analytical QA, where questions require extracting and…
Information ExtractionQuestion AnsweringCode GenerationBLAST: Benchmarking LLMs with ASP-based Structured Testing
Large Language Models (LLMs) have demonstrated remarkable performance across a broad spectrum of tasks, including natural language understanding, dialogue systems, and code generation. Despite evident progress, less atte…
Natural Language UnderstandingCode GenerationSSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking
Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing approaches, the KGW scheme is particularly attractive due to its versatili…
Mathematical ReasoningCode Generation