Papers Code Generation
“Code Generation” 태그가 달린 논문 3,062편 · 필터 해제
Repo0: Design-Driven Zero-to-All Code Generation
Large language model agents have made substantial progress in code generation, yet most existing systems assume a predefined repository architecture. This assumption does not hold in zero-to-all code generation, where an…
Code GenerationGrading the Graders: Verification Autonomy Levels (L0-L5) for LLM Reasoning
Large language models (LLMs) are increasingly paired with verifiers (step checkers, self-consistency filters, tool-based fact checkers, formal proof assistants) that claim to detect the model's errors. Yet the verificati…
Medical DiagnosisCode GenerationSemaPLC: A Project-Grounded, Verification-Gated Agent Harness for PLC Code Generation
Programmable logic controllers (PLCs) run industrial plants, and large language models can already generate independent program organization units (POUs) for them. Whether such logic integrates into an existing PLC proje…
Code GenerationLLM as Detector: An In-context Learning Approach for Tabular Anomaly Detection
Anomaly detection in tabular data is challenging because abnormal samples often arise as violations of cross-feature dependencies rather than simple marginal deviations. Existing detectors rely on geometric or reconstruc…
Anomaly DetectionCode GenerationTokEval: A Tokenizer Evaluation Suite
Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities. This can be partly attributed to a limited understanding of which t…
Mathematical ReasoningCode GenerationA Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design
Metasurfaces have revolutionized the development of photonic devices by enabling unprecedented precision in light manipulation. However, their design processes are often constrained by computationally expensive simulatio…
Code GenerationWhen State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents
Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the co…
Text GenerationCode GenerationTDD-Agent: Test-Driven Reasoning for Code Generation
Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging. Existing approaches often use generated tests as static …
Code GenerationRemote-Sensing City Layout Extraction with MLLM
Remote-sensing systems usually describe urban content with detection boxes, semantic masks, or vector boundaries. Such outputs locate classes and support image-plane scoring, yet they do not by themselves constitute an e…
Code GenerationCompeting at Every Price Point with Agentic Evolution over a Menu of LLMs
Consider a firm that surveys its competition for a particular agentic task and seeks to offer superior accuracy at every competitor price point. A firm that Pareto-dominated its competitors would leave no rational custom…
Code GenerationSecuring AI-Generated Code: A Just-in-Time Vulnerability Detection and Remediation Pipeline
AI-assisted development tools generate vulnerable code at significant rates, yet few automated mechanisms exist to detect, enrich, fix, and verify security issues at development velocity, particularly ones that ground re…
Vulnerability DetectionCode GenerationDetecting Contaminated Code-Generation Prompt Batches via Influence Functions
Large language models (LLMs) are increasingly used for code generation, yet they remain vulnerable to prompts that elicit insecure implementations. Existing defenses typically rely on predefined threat models or known vu…
Code GenerationAgentic Transaction: Towards ACID-Compliant Agent Systems
Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation. As agents in…
Code GenerationVero: Can AI Agents Build Formally Verified Software Repositories?
AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an implementation and a machine-checked pr…
Code GenerationEdit2TikZ: A Comprehensive and Challenging Benchmark for Scientific Figure Editing with TikZ
Although multimodal large language models (MLLMs) have shown substantial potential in visual understanding and graphic code generation, editing scientific figures through code presents a greater challenge: a model must j…
Instruction FollowingVisual LocalizationCode GenerationStateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems
Large language model based multi-agent systems usually communicate in text, i.e., using discrete tokens. However, text introduces a discrete bottleneck. Converting the sender's continuous hidden states into discrete toke…
Question AnsweringCode GenerationLatent On-Policy Self-Distillation
Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-te…
Code GenerationDoes ISO-Grounded NFR Specification Improve LLM Code Generation? A Comparison of Rich and Structured Interventions against a Natural-Language Baseline
In LLM-based code generation, Non-Functional Requirements (NFRs) are often specified as terse one-line phrases. We ask whether grounding those specifications in ISO/IEC 25010 Quality Model, either as rich natural-languag…
Code GenerationINSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators
Large Language Model (LLM)-based simulators often reproduce observable actions but fail to capture the underlying reasoning behind them. In education, where student simulation is increasingly used for various application…
Code GenerationParameter Exploration for RLVR via Variational Learning
Exploration has been a focus of reinforcement learning research for a long time. Recently, there has been growing evidence that it is also an important ingredient in LLM reinforcement learning recipes that can significan…
Mathematical ReasoningReinforcement LearningCode Generation