Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
SEVerA: Verified Synthesis of Self-Evolving Agents
Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm, a planner LLM synthesizes an agent program that invokes parametric mode…
Code GenerationProgram RepairReCUBE: Evaluating Repository-Level Context Utilization in Code Generation
Large Language Models (LLMs) have recently emerged as capable coding assistants that operate over large codebases through either agentic exploration or full-context generation. Existing benchmarks capture a broad range o…
Code GenerationFactors Influencing the Quality of AI-Generated Code: A Synthesis of Empirical Evidence
Context: The rapid adoption of AI-assisted code generation tools, such as large language models (LLMs), is transforming software development practices. While these tools promise significant productivity gains, concerns r…
Code GenerationAre LLMs Overkill for Databases?: A Study on the Finiteness of SQL
Translating natural language to SQL for data retrieval has become more accessible thanks to code generation LLMs. But how hard is it to generate SQL code? While databases can become unbounded in complexity, the complexit…
Code GenerationRealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation
Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains larg…
Code GenerationOn Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins
LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and sensor data. However, resilience to LLM hallucination, human oversight, and real-ti…
Code GenerationSketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Models
Converting process sketches into executable simulation models remains a major bottleneck in process systems engineering, requiring substantial manual effort and simulator-specific expertise. Recent advances in generative…
Code GenerationDUPLEX: Agentic Dual-System Planning via LLM-Driven Information Extraction
While Large Language Models (LLMs) provide semantic flexibility for robotic task planning, their susceptibility to hallucination and logical inconsistency limits their reliability in long-horizon domains. To bridge the g…
Information ExtractionCode GenerationThe Price Reversal Phenomenon: When Cheaper Reasoning Models Cost More
Developers and consumers increasingly choose reasoning models (RMs) based on their listed API prices. However, how accurately do these prices reflect actual inference costs? We conduct the first systematic study of this …
Code GenerationA Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
Reinforcement learning (RL) has emerged as a powerful paradigm for improving large language models beyond supervised fine-tuning, yet sustaining performance gains at scale remains an open challenge, as data diversity and…
Synthetic Data GenerationReinforcement LearningCode GenerationThe Specification Gap: Coordination Failure Under Partial Knowledge in Code Agents
When multiple LLM-based code agents independently implement parts of the same class, they must agree on shared internal representations, even when the specification leaves those choices implicit. We study this coordinati…
Code GenerationQuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation
Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks, making it difficult to separate quantum reasoning from framework fam…
Code GenerationGeoTikzBridge: Advancing Multimodal Code Generation for Geometric Perception and Reasoning
Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities. However, they struggle to perceive fine-grained geometric structures, constraining their ability of geome…
Visual ReasoningCode GenerationAPreQEL: Adaptive Mixed Precision Quantization For Edge LLMs
Today, large language models have demonstrated their strengths in various tasks ranging from reasoning, code generation, and complex problem solving. However, this advancement comes with a high computational cost and mem…
Code GenerationMemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation
LLM agents increasingly rely on memory mechanisms to reuse knowledge from past problem-solving experiences. However, existing methods typically construct memory for a single agent and reuse it with the same underlying mo…
Mathematical ReasoningCode GenerationCustomized User Plane Processing via Code Generating AI Agents for Next Generation Mobile Networks
Generative AI is envisioned to have a crucial impact on next generation mobile networking, making the sixth generation (6G) system considerably more autonomous, flexible, and adaptive than its predecessors. By leveraging…
Code GenerationCode Review Agent Benchmark
Software engineering agents have shown significant promise in writing code. As AI agents permeate code writing, and generate huge volumes of code automatically -- the matter of code quality comes front and centre. As the…
Code GenerationRevisiting Quantum Code Generation: Where Should Domain Knowledge Live?
Recent advances in large language models (LLMs) have enabled the automation of an increasing number of programming tasks, including code generation for scientific and engineering domains. In rapidly evolving software eco…
Code GenerationChimera: Latency- and Performance-Aware Multi-agent Serving for Heterogeneous LLMs
Multi-agent applications often execute complex tasks as multi-stage workflows, where each stage is an LLM call whose output becomes part of context for subsequent steps. Existing LLM serving systems largely assume homoge…
Code GenerationLLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface
Textual Large Language Models (LLMs) provide a simple and familiar interface: a string of text is used for both input and output. However, the information conveyed to an LLM often has a richer structure and semantics, wh…
Code Generation