Papers Code Generation
“Code Generation” 태그가 달린 논문 3,075편 · 필터 해제
RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Execution
Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose RunAgent, a multi-agent plan execution platform that interprets natural-…
Code GenerationLLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning
Hallucinations, outputs that sound plausible but are factually incorrect, remain an open challenge for deployed LLMs. In code generation, models frequently hallucinate non-existent software packages, recommending imports…
Code GenerationTo Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing
Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bottlenecks, posing challenges for interactive coding assistants that de…
Code GenerationBoostLoRA: Growing Effective Rank by Boosting Adapters
Parameter-efficient fine-tuning (PEFT) methods face a tradeoff between adapter size and expressivity: ultra-low-parameter adapters are confined to fixed low-rank subspaces, capping performance even with extended training…
parameter-efficient fine-tuningCode GenerationDiagnosing Capability Gaps in Fine-Tuning Data
Fine-tuning large language models (LLMs) for domain-specific tasks requires training datasets that comprehensively cover the target capabilities a practitioner needs. Yet identifying which capabilities a dataset fails to…
Code GenerationTokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models
Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners. While recent tools provide insights into model internals …
Code GenerationPhysical Foundation Models: Fixed hardware implementations of large-scale neural networks
Foundation models are deep neural networks (such as GPT-5, Gemini~3, and Opus~4) trained on large datasets that can perform diverse downstream tasks -- text and code generation, question answering, summarization, image c…
Image ClassificationQuestion AnsweringCode GenerationFrom Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation
Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots into Python scripts. A circuit diagram can be viewed as a visual domai…
Visual GroundingCode GenerationOMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms
In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms, that starts at idea generation and ends with executable code. Our system …
Prompt EngineeringCode GenerationTDD Governance for Multi-Agent Code Generation via Prompt Engineering
Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows. While test-driven development (TDD) pr…
Prompt EngineeringCode GenerationTurning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dL…
Code GenerationExploring the Limits of Pruning: Task-Specific Neurons, Model Collapse, and Recovery in Task-Specific Large Language Models
Neuron pruning is widely used to reduce the computational cost and parameter footprint of large language models, yet it remains unclear whether neurons in task-specific models contribute uniformly to task performance. In…
Mathematical ReasoningCode GenerationJURY-RL: Votes Propose, Proofs Dispose for Label-Free RLVR
Reinforcement learning with verifiable rewards (RLVR) enhances the reasoning of large language models (LLMs), but standard RLVR often depends on human-annotated answers or carefully curated reward specifications. In mach…
Reinforcement LearningMathematical ReasoningCode GenerationSAFEdit: Does Multi-Agent Decomposition Resolve the Reliability Challenges of Instructed Code Editing?
Instructed code editing is a significant challenge for large language models (LLMs). On the EditBench benchmark, 39 of 40 evaluated models obtain a task success rate (TSR) below 60 percent, highlighting a gap between gen…
Code GenerationCan Code Evaluation Metrics Detect Code Plagiarism?
Source Code Plagiarism Detection (SCPD) plays an important role in maintaining fairness and academic integrity in software engineering education. Code Evaluation Metrics (CEMs) are developed for assessing code generation…
Code GenerationCarbon-Taxed Transformers: A Green Compression Pipeline for Overgrown Language Models
The accelerating adoption of Large Language Models (LLMs) in software engineering (SE) has brought with it a silent crisis: unsustainable computational cost. While these models demonstrate remarkable capabilities in diff…
Code GenerationRecursive Multi-Agent Systems
Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning. We extend such scaling principle from a single mo…
Code GenerationQASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limitation often requires hardware-facing capabilities beyond gate-sequen…
Code GenerationEntropy Centroids as Intrinsic Rewards for Test-Time Scaling
An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy and Gemini Deep Think. Existing selection methods often rely on exter…
Logical ReasoningCode GenerationLearning from Execution: Self-Evolving Memory for Private-Library Code Generation
Large Language Models (LLMs) have achieved strong performance on general code generation, but their effectiveness drops sharply in enterprise settings where software development relies on internal private libraries absen…
Code Generation