Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
3D Primitives are a Spatial Language for VLMs
Vision-language models (VLMs) exhibit a striking paradox: they can generate executable code that reconstructs a 3D scene from geometric primitives with correct object counts, classes, and approximate positions, yet the s…
Spatial ReasoningVisual ReasoningCode GenerationCoT-Guard: Small Models for Strong Monitoring
Monitoring the chain-of-thought (CoT) of reasoning models is a promising approach for detecting covert misbehavior (i.e., hidden objectives) in code generation tasks. While large models (GPT-5, Gemini-3-Flash) can serve …
Reinforcement LearningCode GenerationLossless Anti-Distillation Sampling
Frontier commercial generative models face a growing threat from distillation, whereby a distiller harvests generated responses and trains a competing model of its own at drastically lower cost. Existing defenses either …
Mathematical ReasoningImage GenerationCode GenerationTriVAL: A Tri-Validation Framework for Faithful Automatic Optimization Modeling
Optimization modeling serves as the pivotal bridge between natural-language problem descriptions and optimization solvers, and remains a cornerstone for bringing operations research (OR) into real-world decision making. …
Code GenerationDecision MakingRADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
Compared with individual agents, large language model based multi-agent systems have shown great capabilities consistently across diverse tasks, including code generation, mathematical reasoning, and planning, etc. Despi…
Mathematical ReasoningCode GenerationHELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models
Low-Rank Adaptation (LoRA) dominates parameter-efficient fine-tuning of large language models, yet most variants target dense architectures. Mixture-of-Experts (MoE) models scale parameters at near-constant per-token com…
parameter-efficient fine-tuningMathematical ReasoningCode GenerationThe Agent Use of Agent Beings: Agent Cybernetics Is the Missing Science of Foundation Agents
LLM-based foundation agents that perceive, reason, and act across thousands of reasoning steps are rapidly becoming the dominant paradigm for deploying artificial intelligence in open-ended, long-horizon complex tasks. D…
Code GenerationBenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD
Industrial Computer-Aided Design (CAD) code generation requires models to produce executable parametric programs from visual or textual inputs. Beyond recognizing the outer shape of a part, this task involves understandi…
Visual Question AnsweringReinforcement LearningProgram SynthesisCode GenerationInternalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning
In LLM Reinforcement Fine-Tuning (RFT), curriculum learning drives both efficiency and performance. Yet, current methods externalize curriculum judgment via handcrafted heuristics or auxiliary models, risking misalignmen…
Mathematical ReasoningCode GenerationPrimal Generation, Dual Judgment: Self-Training from Test-Time Scaling
Code generation is typically trained in the primal space of programs: a model produces a candidate solution and receives sparse execution feedback, often a single pass/fail bit. Test-time scaling enriches the inference p…
Code GenerationVision2Code: A Multi-Domain Benchmark for Evaluating Image-to-Code Generation
Image-to-code generation tests whether a vision-language model (VLM) can recover the structure of an image enough to express it as executable code. Existing benchmarks either focus on narrow visual domains, depend on pai…
Code GenerationPDEAgent-Bench: A Multi-Metric, Multi-Library Benchmark for PDE Solver Generation
PDE-to-solver code generation aims to automatically synthesize executable numerical solvers from partial differential equation (PDE) specifications. This task requires not only understanding the mathematical structure of…
Computational EfficiencyCode GenerationCodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents
Clinical reasoning agents based on large language models (LLMs) aim to automate tasks such as intensive care unit (ICU) monitoring and patient state tracking from electronic health records (EHRs). Existing systems typica…
Code GenerationEntropy-informed Decoding: Adaptive Information-Driven Branching
Large language models (LLMs) achieve remarkable generative performance, yet their output quality is dependent on the decoding strategy. While sampling-based methods (e.g., top-k, nucleus) and search-and-select based meth…
Mathematical ReasoningCode GenerationFrom Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
Generative artificial intelligence (GenAI) and agentic systems are moving software engineering from code-centric production toward intent-centric human-agent work in which natural language, repository context, tools, tes…
Code GenerationSemantic Voting: Execution-Grounded Consensus for LLM Code Generation
LLM code-generation pipelines often sample multiple candidates and select one final answer without access to a complete oracle. Existing pipelines mix textual voting, ranking, and execution-based agreement, but the relat…
Code GenerationPrepBench: How Far Are We from Natural-Language-Driven Data Preparation?
Data preparation is a central and time-consuming stage in data analysis workflows. Traditionally, commercial tools have relied on graphical user interfaces (GUIs) to simplify data preparation, allowing users to define tr…
Code GenerationMDGYM: Benchmarking AI Agents on Molecular Simulations
The promise of AI-driven scientific discovery hinges on whether AI agents can autonomously design and execute the computational workflows that underpin modern science. Molecular dynamics (MD) simulation presents a natura…
Physical IntuitionCode GenerationDACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models
Diffusion large language models are a compelling alternative to autoregressive models, yet existing RL methods for diffusion treat all denoising steps as equally important and rely on biased, high-variance likelihood est…
Reinforcement LearningMathematical ReasoningCode GenerationGoal-Conditioned Supervised Learning for LLM Fine-Tuning
Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divided into online and offline paradigms. Online methods, such as RL-based …
Code Generation