Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
From Natural Language to Verified Code: Toward AI Assisted Problem-to-Code Generation with Dafny-Based Formal Verification
Large Language Models (LLMs) show promise in automated software engineering, yet their guarantee of correctness is frequently undermined by erroneous or hallucinated code. To enforce model honesty, formal verification re…
Code GenerationFormalScience: Scalable Human-in-the-Loop Autoformalisation of Science with Agentic Code Generation in Lean
Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specific machinery (\textit{e.g.} Dirac notatio…
Mathematical ReasoningCode GenerationFeedback Over Form: Why Execution Feedback Matters More Than Pipeline Topology in 1-3B Code Generation
Small language models (1-3B) are practical to run locally, but individually limited on harder code generation tasks. We ask whether composing them into pipelines can recover some of that lost capability. We study code ge…
Code GenerationFocus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models
This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines hardware and software co-design of transformer blocks with an optimization pipeline that reduce…
Code GenerationYou Don't Need Public Tests to Generate Correct Code
Multi-agent systems are frequently employed for autonomous code generation, demonstrating strong utility in complex algorithmic problem-solving. Recent studies tackle the difficulty of producing functionally correct prog…
Code GenerationFrom If-Statements to ML Pipelines: Revisiting Bias in Code-Generation
Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming and reveal solely overt, explicitly encoded bias. We demonstrate th…
Code GenerationLearning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems
Multi-agent systems built on large language models have shown strong performance on complex reasoning tasks, yet most work focuses on agent roles and orchestration while treating inter-agent communication as a fixed inte…
Mathematical ReasoningCode GenerationWebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning
While Large Language Models (LLMs) excel at function-level code generation, project-level tasks such as generating functional and visually aesthetic multi-page websites remain highly challenging. Existing works are often…
Reinforcement LearningCode GenerationParallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL
Modern language models demonstrate impressive coding capabilities in common programming languages (PLs), such as C++ and Python, but their performance in lower-resource PLs is often limited by training data availability.…
Code GenerationBODHI: Precise OS Kernel Specification Inference
The formal verification of operating system kernels requires precise specifications that capture the intended behavior of system calls. Writing these specifications manually demands deep domain expertise, motivating the …
Code GenerationCreativeGame:Toward Mechanic-Aware Creative Game Generation
Large language models can generate plausible game code, but turning this capability into \emph{iterative creative improvement} remains difficult. In practice, single-shot generation often produces brittle runtime behavio…
Code GenerationStratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play
Games offer a compelling paradigm for developing general reasoning capabilities in language models, as they naturally demand strategic planning, probabilistic inference, and adaptive decision-making. However, existing se…
Mathematical ReasoningCode GenerationCo-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization
Automating operations research (OR) with large language models (LLMs) remains limited by hand-crafted reasoning--execution workflows. Complex OR tasks require adaptive coordination among problem interpretation, mathemati…
Code GenerationHiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution
Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental results. However, existing approaches still use fixed sequential agent p…
Code GenerationAdversarial Arena: Crowdsourcing Data Generation through Interactive Competition
Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for multi-turn conversations. Common solutions are crowdsourcing or synth…
Code GenerationAIT Academy: Cultivating the Complete Agent with a Confucian Three-Domain Curriculum
What does it mean to give an AI agent a complete education? Current agent development produces specialists systems optimized for a single capability dimension, whether tool use, code generation, or security awareness tha…
Code GenerationTLoRA: Task-aware Low Rank Adaptation of Large Language Models
Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning method for large language models, with its effectiveness largely influenced by the allocation of ranks and scaling factors, as well a…
parameter-efficient fine-tuningNatural Language UnderstandingCode GenerationSolidCoder: Bridging the Mental-Reality Gap in LLM Code Generation through Concrete Execution
State-of-the-art code generation frameworks rely on mental simulation, where LLMs internally trace execution to verify correctness. We expose a fundamental limitation: the Mental-Reality Gap -- where models hallucinate e…
Code GenerationLeGo-Code: Can Modular Curriculum Learning Advance Complex Code Generation? Insights from Text-to-SQL
Recently, code-oriented large language models (LLMs) have demonstrated strong capabilities in translating natural language into executable code. Text-to-SQL is a significant application of this ability, enabling non-tech…
Code GenerationPARM: Pipeline-Adapted Reward Model
Reward models (RMs) are central to aligning large language models (LLMs) with human preferences, powering RLHF and advanced decoding strategies. While most prior work focuses on single-step generation, real-world applica…
Code Generation