Coding papers

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47 papers found

ReviewPaperPublishedTopics

7.2Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool ScenariosUnderstanding the limitations and capabilities of LLMs in generating complete software systems can guide future improvements in AI-driven software development.Jul 20, 2026LLM Benchmark Coding

7.5Democratizing Agent Deployment Safety: A Structural Monitoring ApproachAs AI agents increasingly modify critical systems, ensuring their safe deployment is essential for organizations lacking sophisticated monitoring capabilities.Jul 18, 2026Security Safety Coding

7.5Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code QualityUnderstanding language bias in code generation can lead to improved multilingual programming tools and practices, enhancing accessibility and usability for diverse developers.Jul 18, 2026Coding Evaluation Benchmark

7.2Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow ExecutionUnderstanding the benefits of specialist agents over generalist ones can lead to more efficient and reliable software development tools, particularly in business process automation.Jul 18, 2026LLM Agent Coding

6.8Reliable and Developer-Aligned Evaluation of Agents for Software EngineeringUnderstanding and improving the evaluation of LLM agents in software engineering can lead to more effective and reliable tools for developers.Jul 9, 2026LLM Agent Evaluation

7.5Beyond Correctness: Enhancing Architectural Reasoning in Code LLMs via Scalable Labeling with Agentic JudgmentThis research addresses a critical gap in software engineering by providing a scalable method for evaluating architectural quality, which is essential for maintaining code quality in real-world applications.Jul 8, 2026LLM Agent Reasoning

7.5CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based ProgrammingThis research addresses the practical need for effective AI tutors in educational settings, particularly in K-12 environments, where privacy and cost are significant concerns.Jul 8, 2026LLM Evaluation Benchmark

7.5A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS DevelopmentThis research provides a structured approach to improve the reliability of AI systems in geospatial engineering, which is critical for accurate decision-making in various applications.Jul 3, 2026Agent Coding Infra

7.5Beyond the Library: An Agentic Framework for Autoformalizing Research MathematicsThis work is significant as it enhances the reliability of mathematical proofs generated by LLMs, potentially transforming how research mathematics is formalized and verified.Jul 3, 2026LLM Agent Reasoning

6.8A Deterministic Control Plane for LLM Coding AgentsThis work is relevant for AI engineers and researchers looking to improve the security and manageability of LLM coding agents, which are increasingly used in software development.Jun 27, 2026LLM Agent Security

7.2Multi-LCB: Extending LiveCodeBench to Multiple Programming LanguagesUnderstanding LLMs' performance across multiple programming languages is crucial for their application in real-world software engineering, where diverse language support is often required.Jun 20, 2026LLM Evaluation Benchmark

7.2FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style ReasoningThis work is significant for AI engineers and researchers as it addresses the challenge of ensuring code correctness in large-scale systems generated by LLMs, potentially reducing human effort in writing formal specifications and improving software reliability.Jun 20, 2026LLM Agent Reasoning

7.2LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic ReasoningThis approach could simplify the deployment of LLMs in complex reasoning tasks by reducing the need for manual task-specific engineering and improving logical consistency.Jun 12, 2026LLM Reasoning Coding

7.5Beyond Problem Solving: UOJ-Bench for Evaluating Code Generation, Hacking, and Repair in Competitive ProgrammingUnderstanding LLMs' capabilities in educational contexts can enhance their integration into programming education and improve human learning outcomes.Jun 12, 2026LLM Evaluation Benchmark

7.2Agents All the Way Down; A Methodology for Building Custom AI Agents from Substrate to ProductionThis methodology could streamline the process of creating tailored AI agents, making it easier for engineers to implement specialized solutions in various applications.Jun 11, 2026Agent Coding

6.8AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively VulnerableUnderstanding the strengths and weaknesses of AI coding agents in social science can guide their deployment and highlight areas needing improvement, particularly in mitigating interpretive biases.Jun 11, 2026LLM Agent Evaluation

6.8Exploration Structure in LLM Agents for Multi-File Change LocalizationUnderstanding and improving how AI agents localize software changes can enhance the efficiency and accuracy of software maintenance and development processes.Jun 11, 2026LLM Agent Coding

6.8Mind the Gap: Can Frontier LLMs Pass a Standardized Office Proficiency Exam?Understanding the limitations of LLMs in practical office tasks is crucial for researchers and engineers aiming to improve automation capabilities and develop more effective AI systems.Jun 10, 2026LLM Evaluation Benchmark

7.5Flaws in the LLM Automation NarrativeUnderstanding the limitations of LLMs in critical applications is essential for researchers and engineers to set realistic expectations and improve model evaluations.Jun 10, 2026LLM Evaluation Benchmark

7.2Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMsUnderstanding the nuanced performance of code generation models across different languages and problem types can guide developers in selecting the right model for specific tasks and highlight areas for improvement in model development.Jun 9, 2026LLM Evaluation Benchmark

6.8SWE-IF: Aligning Code Evaluation with Human PreferenceUnderstanding and improving LLMs' ability to follow code instructions can lead to more user-aligned and effective coding assistance tools.Jun 8, 2026LLM Evaluation Coding

7.2LLM Agent-Assisted Reverse Engineering with Quantitative Readability MetricsImproving the readability of decompiled code can significantly aid reverse engineering efforts, making it more accessible for developers and researchers in security and software analysis.Jun 8, 2026LLM Coding Evaluation

7.5A Study of LLMs' Preferences for Libraries and Programming LanguagesUnderstanding LLMs' preferences can inform better model training and evaluation, leading to more effective code generation in diverse programming contexts.Jun 6, 2026LLM Coding Evaluation

6.8Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent WorkflowsUnderstanding the efficiency and effectiveness of multi-agent systems in LLM workflows can guide the development of more cost-effective and accurate AI systems.Jun 6, 2026LLM Agent Evaluation

7.2Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming TasksUnderstanding the stability of LLMs in deterministic tasks is crucial for reliable deployment in real-world applications where consistent performance is required.Jun 2, 2026LLM Evaluation Coding

6.5Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor NetworksUnderstanding the role of LLMs in algorithm development can enhance efficiency in computational tasks, particularly in complex areas like tensor networks.Jun 2, 2026LLM Coding Evaluation

7.5Reliable Reasoning with Large Language Models via Preference-Based Maximum SatisfiabilityThis research addresses a significant limitation in LLMs' ability to handle complex optimization tasks, making it relevant for applications in robotics and other constraint-heavy domains.May 29, 2026LLM Reasoning Robotics

6.8LACUNA: Safe Agents as Recursive Program HolesThis work is relevant for AI engineers and researchers interested in developing more expressive and safe agent systems that can dynamically interact with their runtime environments.May 28, 2026LLM Agent Safety

6.8Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game DevelopmentUnderstanding how LLM-based agents can effectively collaborate in software engineering tasks is crucial for developing autonomous systems that can reliably produce correct solutions.May 26, 2026LLM Agent Coding

7.0Code2UML: Agentic LLMs with context engineering for scalable software visualizationThis work addresses the challenge of scaling LLM-based code analysis to large codebases, which is crucial for automating software documentation and improving developer productivity.May 26, 2026LLM Agent Coding

7.8Autonomous Intelligent Agents for Natural-Language-Driven Web Execution with Integrated Security AssuranceThis work addresses critical challenges in web testing and security, providing a practical solution that can save developers time and improve software reliability.May 18, 2026Agent Security Coding

7.2Distribution-Aware Algorithm Design with LLM AgentsThis research could lead to more efficient algorithm design by leveraging LLMs to tailor solver code to specific distributions, potentially reducing computational costs and improving performance in practical applications.May 16, 2026LLM Agent Coding

7.5OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent CollaborationThis work addresses a gap in the systematic design and optimization of LLM-based multi-agent systems, which is crucial for enhancing their performance in complex tasks.May 16, 2026LLM Agent Reasoning

6.8Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program AnalysisThis framework could improve program analysis by leveraging LLMs to incorporate dynamic information sources, potentially leading to more accurate and comprehensive analyses.May 14, 2026LLM Reasoning Coding

7.2M2A: Synergizing Mathematical and Agentic Reasoning in Large Language ModelsThis work is relevant for AI researchers and engineers interested in improving the reasoning capabilities of language models, particularly in applications requiring both mathematical and interactive reasoning.May 13, 2026LLM Agent Reasoning

7.0Reasoning Is Not Free: Robust Adaptive Cost-Efficient Routing for LLM-as-a-JudgeUnderstanding when and how to leverage reasoning in LLMs can lead to more efficient and accurate automated decision-making systems, which is crucial for applications requiring structured verification.May 13, 2026LLM Reasoning Evaluation

7.2ReaComp: Compiling LLM Reasoning into Symbolic Solvers for Efficient Program SynthesisThis work is relevant for AI researchers and engineers interested in improving the efficiency and reliability of program synthesis by reducing dependency on LLMs during inference, thus saving computational resources and potentially broadening the applicability of AI solutions.May 8, 2026LLM Reasoning Coding

7.5Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMsUnderstanding the capabilities and limitations of LLMs in deterministic tasks is crucial for researchers and engineers aiming to integrate these models into reliable computational applications.May 8, 2026LLM Evaluation Coding

7.2ProgramBench: Can Language Models Rebuild Programs From Scratch?This research addresses a significant gap in evaluating language models for software development, moving beyond simple tasks to more complex, real-world scenarios.May 7, 2026LLM Agent Evaluation

6.8AI Alignment via Incentives and CorrectionUnderstanding and improving AI alignment is crucial for ensuring that AI systems behave as intended, especially as they become more autonomous and integrated into critical applications.May 6, 2026Alignment Agent Coding

7.2IACDM: Interactive Adversarial Convergence Development Methodology -- A Structured Framework for AI-Assisted Software DevelopmentAs AI tools become more prevalent in software development, understanding and addressing their limitations is crucial for ensuring the reliability and security of AI-generated applications.May 1, 2026Coding Security Evaluation

7.2Understanding the Limits of Automated Evaluation for Code Review Bots in PracticeUnderstanding the limitations of automated code review tools is crucial for improving their design and integration into software development workflows.Apr 29, 2026Evaluation Coding

6.8An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python CodeUnderstanding the capabilities of locally deployed LLMs for bug detection is crucial for privacy-sensitive and resource-constrained environments, offering insights into their practical applicability and limitations.Apr 29, 2026LLM Evaluation Coding

6.8Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation ModelsThis work is relevant for AI engineers and researchers looking to improve the efficiency and performance of multimodal foundation models, which are increasingly used in diverse applications.Apr 27, 2026Multimodal Infra Architecture

7.2Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial FindingsAs generative AI transforms software engineering, establishing effective frameworks like Shift-Up is crucial for maintaining code quality and project sustainability.Apr 23, 2026Coding Architecture

7.2From Feelings to Metrics: Understanding and Formalizing How Users Vibe-Test LLMsUnderstanding and formalizing user evaluation practices can bridge the gap between benchmark scores and real-world applicability, making LLM assessments more relevant.Apr 18, 2026Other

7.5AgentForge: Execution-Grounded Multi-Agent LLM Framework for Autonomous Software EngineeringThis framework addresses the critical gap in verifying the correctness of code generated by large language models, making it highly relevant for developers and researchers focused on reliable software automation.Apr 17, 2026LLM Agent Coding