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évaluation d'IA

  • WorFBench

    WorFBench is an open-source benchmark framework evaluating LLM-based AI agents on task decomposition, planning, and multi-tool orchestration.
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    What is WorFBench?
    WorFBench is a comprehensive open-source framework designed to assess the capabilities of AI agents built on large language models. It offers a diverse suite of tasks—from itinerary planning to code generation workflows—each with clearly defined goals and evaluation metrics. Users can configure custom agent strategies, integrate external tools via standardized APIs, and run automated evaluations that record performance on decomposition, planning depth, tool invocation accuracy, and final output quality. Built‐in visualization dashboards help trace each agent’s decision path, making it easy to identify strengths and weaknesses. WorFBench’s modular design enables rapid extension with new tasks or models, fostering reproducible research and comparative studies.
    WorFBench Core Features
    • Diverse workflow-based benchmark tasks
    • Standardized evaluation metrics
    • Modular agent interface for LLMs
    • Baseline agent implementations
    • Multi-tool orchestration support
    • Result visualization dashboard
    WorFBench Pro & Cons

    The Cons

    Performance gaps remain significant even in state-of-the-art LLMs like GPT-4.
    Generalization to out-of-distribution or embodied tasks shows limited improvement.
    Complex planning tasks still pose challenges, limiting practical deployment.
    Benchmark primarily targets research and evaluation, not a turnkey AI tool.

    The Pros

    Provides a comprehensive benchmark for multi-faceted workflow generation scenarios.
    Includes a detailed evaluation protocol capable of precisely measuring workflow generation quality.
    Supports better generalization training for LLM agents.
    Demonstrates improved end-to-end task performance when workflows are incorporated.
    Enables reduction in inference time through parallel execution of workflow steps.
    Helps decrease unnecessary planning steps, enhancing agent efficiency.

  • Simple Playgrounds

    A lightweight Python library for creating customizable 2D grid environments to train and test reinforcement learning agents.
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    What is Simple Playgrounds?
    Simple Playgrounds provides a modular platform for building interactive 2D grid environments where agents can navigate mazes, interact with objects, and complete tasks. Users define environment layouts, object behaviors, and reward functions via simple YAML or Python scripts. The integrated Pygame renderer delivers real-time visualization, while a step-based API ensures seamless integration with reinforcement learning libraries like Stable Baselines3. With support for multi-agent setups, collision detection, and customizable physics parameters, Simple Playgrounds streamlines the prototyping, benchmarking, and educational demonstration of AI algorithms.
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