Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
STRIATUM-CTF: A Protocol-Driven Agentic Framework for General-Purpose CTF Solving
Large Language Models (LLMs) have demonstrated potential in code generation, yet they struggle with the multi-step, stateful reasoning required for offensive cybersecurity operations. Existing research often relies on st…
Code GenerationHow to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data
A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like Qwen3-8B, this approach often fails to …
Code GenerationDomAgent: Leveraging Knowledge Graphs and Case-Based Reasoning for Domain-Specific Code Generation
Large language models (LLMs) have shown impressive capabilities in code generation. However, because most LLMs are trained on public domain corpora, directly applying them to real-world software development often yields …
Domain AdaptationKnowledge GraphsCode GenerationSeed1.8 Model Card: Towards Generalized Real-World Agency
We present Seed1.8, a foundation model aimed at generalized real-world agency: going beyond single-turn prediction to multi-turn interaction, tool use, and multi-step execution. Seed1.8 keeps strong LLM and vision-langua…
Code GenerationThe $\mathbf{Y}$-Combinator for LLMs: Solving Long-Context Rot with $λ$-Calculus
LLMs are increasingly used as general-purpose reasoners, but long inputs remain bottlenecked by a fixed context window. Recursive Language Models (RLMs) address this by externalising the prompt and recursively solving su…
Code GenerationLLM-Driven Heuristic Synthesis for Industrial Process Control: Lessons from Hot Steel Rolling
Industrial process control demands policies that are interpretable and auditable, requirements that black-box neural policies struggle to meet. We study an LLM-driven heuristic synthesis framework for hot steel rolling, …
Code GenerationMathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin
Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for symbolic physical reasoning in domain-specific scientific problems rema…
Mathematical ReasoningCode GenerationHCAG: Hierarchical Abstraction and Retrieval-Augmented Generation on Theoretical Repositories with LLMs
Existing Retrieval-Augmented Generation (RAG) methods for code struggle to capture the high-level architectural patterns and cross-file dependencies inherent in complex, theory-driven codebases, such as those in algorith…
Code GenerationLightweight Model Predictive Control for Spacecraft Rendezvous Attitude Synchronization
This work introduces two lightweight model predictive control (MPC) approaches for attitude tracking with reaction wheels during spacecraft rendezvous synchronization. Both approaches are based on a novel attitude deviat…
Code GenerationExploring the Agentic Frontier of Verilog Code Generation
Large language models (LLMs) have made rapid advancements in code generation for popular languages such as Python and C++. Many of these recent gains can be attributed to the use of ``agents'' that wrap domain-relevant t…
Code GenerationEnhancing Reinforcement Learning Fine-Tuning with an Online Refiner
Constraints are essential for stabilizing reinforcement learning fine-tuning (RFT) and preventing degenerate outputs, yet they inherently conflict with the optimization objective because stronger constraints limit the ab…
Reinforcement LearningCode GenerationOmni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation
We present Omni-I2C, a comprehensive benchmark designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code. We argue that this task repres…
Code GenerationVeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation
LLMs have recently demonstrated strong capabilities in automatic RTL code generation, achieving high syntactic and functional correctness. However, most methods focus on functional correctness while overlooking critical …
Code GenerationDon't Vibe Code, Do Skele-Code: Interactive No-Code Notebooks for Subject Matter Experts to Build Lower-Cost Agentic Workflows
Skele-Code is a natural-language and graph-based interface for building workflows with AI agents, designed especially for less or non-technical users. It supports incremental, interactive notebook-style development, and …
Code GenerationExecution-Grounded Credit Assignment for GRPO in Code Generation
Critic-free reinforcement learning with verifiable rewards (RLVR) improves code generation by optimizing unit-test pass rates, but GRPO-style updates suffer from coarse credit assignment: a single outcome signal is sprea…
Reinforcement LearningCode GenerationGenerative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy
We review thirteen generative systems and five supporting datasets for quantum circuit and quantum code generation, identified through a structured scoping review of Hugging Face, arXiv, and provenance tracing (January-F…
Graph GenerationCode GenerationFrom Natural Language to Executable Option Strategies via Large Language Models
Large Language Models (LLMs) excel at general code generation, yet translating natural-language trading intents into correct option strategies remains challenging. Real-world option design requires reasoning over massive…
Code GenerationDetecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning
Code generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown that these models are vulnerable to backdoor and poisoning attacks that induce th…
Code GenerationCODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization
Optimizing Register Transfer Level (RTL) code is a critical step in Electronic Design Automation (EDA) for improving power, performance, and area (PPA). We present CODMAS (Collaborative Optimization via a Dialectic Multi…
Code GenerationAI Scientist via Synthetic Task Scaling
With the advent of AI agents, automatic scientific discovery has become a tenable goal. Many recent works scaffold agentic systems that can perform machine learning research, but don't offer a principled way to train suc…
Code Generation