Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
Acceptance Dynamics Across Cognitive Domains in Speculative Decoding
Speculative decoding accelerates large language model (LLM) inference. It uses a small draft model to propose a tree of future tokens. A larger target model then verifies these tokens in a single batched forward pass. De…
Mathematical ReasoningLogical ReasoningCode GenerationCOEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation
LLM-based RTL code generation methods increasingly target both functional correctness and PPA quality, yet existing approaches universally decouple the two objectives, optimizing PPA only after correctness is fully achie…
Code GenerationQuantCode-Bench: A Benchmark for Evaluating the Ability of Large Language Models to Generate Executable Algorithmic Trading Strategies
Large language models have demonstrated strong performance on general-purpose programming tasks, yet their ability to generate executable algorithmic trading strategies remains underexplored. Unlike standard code benchma…
Code GenerationDataset-Level Metrics Attenuate Non-Determinism: A Fine-Grained Non-Determinism Evaluation in Diffusion Language Models
Diffusion language models (DLMs) have emerged as a promising paradigm for large language models (LLMs), yet the non-deterministic behavior of DLMs remains poorly understood. The existing non-determinism evaluations for L…
Question AnsweringCode GenerationCollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation
Automated code generation remains a persistent challenge in software engineering, as conventional multi-agent frameworks are often constrained by static planning, isolated execution, high computational overhead, and limi…
Code GenerationVeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog
Large Language Models (LLMs) have shown significant improvement in RTL code generation. Despite the advances, the generated code is often riddled with common vulnerabilities and weaknesses (CWEs) that can slip by untrain…
Code GenerationCodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation
Large language models (LLMs) can generate code from natural language, but the extent to which they capture intended program behavior remains unclear. Executable behavioral specifications, defined via preconditions and po…
Code GenerationFrontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization
Current LLM agent benchmarks, which predominantly focus on binary pass/fail tasks such as code generation or search-based question answering, often neglect the value of real-world engineering that is often captured throu…
Question AnsweringCode GenerationBeyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks
Large language models (LLMs) increasingly rely on explicit reasoning to solve coding tasks, yet evaluating the quality of this reasoning remains challenging. Existing reasoning evaluators are not designed for coding, and…
Code GenerationBalanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO
Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language models, and GRPO-style training is widely adopted for its simplicity and e…
Reinforcement LearningCode GenerationValidity-Calibrated Reasoning Distillation
Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, more efficient ones. While recent methods have shown promising gains, they typically rely on static teacher…
Mathematical ReasoningCode GenerationBEAM: Bi-level Memory-adaptive Algorithmic Evolution for LLM-Powered Heuristic Design
Large Language Model-based Hyper Heuristic (LHH) has recently emerged as an efficient way for automatic heuristic design. However, most existing LHHs just perform well in optimizing a single function within a pre-defined…
Code GenerationLightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation
On-policy distillation (OPD) is an effective post-training paradigm for large language models but requires a live teacher server throughout training, resulting in substantial infrastructure overhead. We investigate wheth…
Code GenerationRo-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation
Recent advances in large language models (LLMs) provide robots with contextual reasoning abilities to comprehend human instructions. Yet, current LLM-enabled robots typically depend on cloud-based models or high-performa…
Robot Task PlanningCode GenerationArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generation
Parametric Computer-Aided Design (CAD) of articulated assemblies is essential for product development, yet generating these multi-part, movable models from high-level descriptions remains unexplored. To address this, we …
Spatial ReasoningCode GenerationSpatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks
We introduce compute-grounded reasoning (CGR), a design paradigm for spatial-aware research agents in which every answerable sub-problem is resolved by deterministic computation before a language model is asked to genera…
Spatial ReasoningCode GenerationHow Many Tries Does It Take? Iterative Self-Repair in LLM Code Generation Across Model Scales and Benchmarks
Large language models frequently fail to produce correct code on their first attempt, yet most benchmarks evaluate them in a single-shot setting. We investigate iterative self-repair (feeding execution errors back to the…
Code GenerationStructure-Grounded Knowledge Retrieval via Code Dependencies for Multi-Step Data Reasoning
Selecting the right knowledge is critical when using large language models (LLMs) to solve domain-specific data analysis tasks. However, most retrieval-augmented approaches rely primarily on lexical or embedding similari…
Code GenerationAgentic Application in Power Grid Static Analysis: Automatic Code Generation and Error Correction
This paper introduces an LLM agent that automates power grid static analysis by converting natural language into MATPOWER scripts. The framework utilizes DeepSeek-OCR to build an enhanced vector database from MATPOWER ma…
Code GenerationLearning Hierarchical and Geometry-Aware Graph Representations for Text-to-CAD
Text-to-CAD code generation is a long-horizon task that translates textual instructions into long sequences of interdependent operations. Existing methods typically decode text directly into executable code (e.g., bpy) w…
Code Generation