2025-01-03

Generative AI for Assisting Software Developers

HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation

Relevance: This paper introduces a new benchmark for evaluating LLMs on self-invoking code generation, a task directly relevant to assisting software developers. The benchmark focuses on the ability of LLMs to solve a base problem and then utilize its solution to solve a more complex related problem, mirroring real-world software development scenarios. The findings highlight the limitations of current LLMs in this area and suggest future research directions for improving code reasoning capabilities, thus directly impacting the development of generative AI tools for software developers.

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Training Software Engineering Agents and Verifiers with SWE-Gym

Relevance: This paper introduces SWE-Gym, an environment for training software engineering agents. The use of real-world Python tasks and the focus on improving code resolution rates directly addresses the application of generative AI in software development. The creation of such an environment facilitates the development and evaluation of AI systems that can assist developers with tasks such as bug detection, code completion, and refactoring, thereby advancing the field of Generative AI for software engineering.

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AI Agents

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis

Relevance: This paper directly addresses the challenges in training GUI agents by proposing a novel data synthesis pipeline. OS-Genesis focuses on creating high-quality trajectory data for training AI agents that interact with graphical user interfaces. The innovative reverse task synthesis approach, along with the use of a trajectory reward model, significantly improves agent performance, advancing the capabilities of AI agents in real-world digital environments.

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OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System

Relevance: This paper presents OneKE, a system using multiple agents to extract knowledge from various sources. The design of OneKE with different agents performing specific roles demonstrates a practical approach to building complex AI agents capable of handling diverse tasks. The focus on schema configuration and error correction highlights the importance of robust design and adaptability in AI agents.

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Xmodel-2 Technical Report

Relevance: This paper introduces Xmodel-2, a large language model designed for reasoning tasks and agent-based tasks. The architecture’s emphasis on efficient training and its ability to handle complex reasoning are key features relevant to building effective AI agents. The open-source nature of the model and code contributes to the broader AI agent research community.

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Prompt Engineering Techniques

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Relevance: This paper directly addresses prompt engineering by focusing on the issue of ‘overthinking’ in LLMs. It introduces novel efficiency metrics and proposes strategies to mitigate this issue, ultimately leading to more efficient prompt crafting and improved resource allocation. The research contributes to a better understanding of how to optimize prompts for specific tasks and models.

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Human-in-the-loop Machine Learning

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Techniques for Explaining AI Behavior

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