CursorCore: Assist Programming through Aligning Anything

Abstract: LLMs have been successfully applied to programming assistance tasks, such as code completion, code insertion, and instructional code editing. However, these applications remain insufficiently automated and struggle to effectively integrate various types of information during the programming process, including coding history, current code, and user instructions. In this work, we propose a new conversational framework that comprehensively integrates these information sources, collect data to train our models and evaluate their performance. Firstly, to thoroughly evaluate how well models align with different types of information and the quality of their outputs, we introduce a new benchmark, APEval (Assist Programming Eval), to comprehensively assess the performance of models in programming assistance tasks. Then, for data collection, we develop a data generation pipeline, Programming-Instruct, which synthesizes training data from diverse sources, such as GitHub and online judge platforms. This pipeline can automatically generate various types of messages throughout the programming process. Finally, using this pipeline, we generate 219K samples, fine-tune multiple models, and develop the CursorCore series. We show that CursorCore outperforms other models of comparable size. This framework unifies applications such as inline chat and automated editing, contributes to the advancement of coding assistants. Code, models and data are freely available at https://github.com/TechxGenus/CursorCore.

• The paper introduces CursorCore, a novel framework that integrates historical code edits, current snippets, and user instructions to enhance AI-driven programming.
• The APEval benchmark rigorously assesses model performance in program synthesis, code repair, and editing tasks using classic Pass@1 metrics.
• The Programming-Instruct pipeline generates 219K diverse samples, enabling fine-tuning of CursorCore models for adaptable and practical software development.

Overview of "CursorCore: Assist Programming through Aligning Anything"

The paper "CursorCore: Assist Programming through Aligning Anything" delivers a novel framework and model series aimed at enhancing AI-assisted programming. The authors identify a critical gap in existing LLMs: their inability to seamlessly integrate diverse types of information during the software development process. The proposed solution, CursorCore, comprises a new conversational framework, a benchmark for model evaluation, and a data collection pipeline to improve the training process.

Assistant-Conversation Framework

The Assistant-Conversation framework introduced in this paper attempts to integrate the programming process more comprehensively than existing methods. The framework incorporates various inputs, such as System instructions (S), historical edits (H), current code snippets (C), and user instructions (U), with the model outputs being the Assistant's (A) response. This comprehensive approach aims to address common shortcomings in conventional LLM applications in coding, which often overlook important contextual information from code history or rely heavily on user input.

In practical terms, the framework provides flexibility by allowing different input combinations, such as:

• Historical code edits with current and user input (H, C, U)
• Historical edits with current code (H, C)
• Current code with user instructions (C, U)
• Solely current code (C)

The design ensures that models can utilize complete coding scenarios to suggest edits, streamlining the coding process and minimizing the need for redundant operations by developers.

APEval: Benchmark for Evaluation

To assess the alignment capabilities of programming assistants, the authors propose a new benchmark named APEval. This benchmark extends existing evaluations like HumanEval by incorporating a variety of informational inputs to test the performance of models more rigorously across the aspects of program synthesis, code repair, and task-specific instructional editing. APEval consists of samples categorized into four types based on the informational input combinations. It utilizes classic Pass@1 metrics for evaluation, aiming to provide a comprehensive assessment of performance in a range of programming assistance tasks.

Programming-Instruct: Data Collection Pipeline

To address the scarcity of relevant training data reflecting real-world coding processes, the authors introduce Programming-Instruct. This pipeline generates diverse training datasets by using multiple sources, including simulated coding processes from AI models (AIprogrammer), real-world Git commit histories, and records of iterative development submissions on online coding platforms. It synthesizes data for various scenarios without requiring extensive manual annotation.

The authors generate 219K samples using this pipeline, fine-tuning multiple models to develop the CursorCore series. The intentional diversity of data ensures that the models are exposed to numerous programming contexts and instructions during training, enhancing their adaptability to different real-world scenarios.

CursorCore Model Series

The CursorCore models are fine-tuned versions of notable base LLMs, including Deepseek-Coder, Yi-Coder, and Qwen2.5-Coder. These models are compared against other state-of-the-art solutions and are found to demonstrate superior performance in programming assistance tasks within the benchmark evaluations. Their training involves using a chat template that aligns well with the varied input types defined by Assistant-Conversation, maintaining compatibility with existing chatbot functionalities.

Implications and Future Directions

The CursorCore series offers a significant advancement in AI-assisted programming by improving automation and reducing friction in coding workflows. While the paper focuses primarily on function-level programming assistance, future research might explore the translation of these frameworks to repository-level and multi-file scenarios to gain broader applicability.

Further developments could incorporate preference-based optimization techniques or explore additional application areas beyond programming, expanding the methodologies to design AI assistants across various domains.

In conclusion, the paper provides a comprehensive approach to enhancing the integration of information in programming assistance models, offering both theoretical insights and practical tools that could influence future developments in the field of AI-driven software development.