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  • 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.

  • Emergent Communication in Agents

    Open-source PyTorch framework for multi-agent systems to learn and analyze emergent communication protocols in cooperative reinforcement learning tasks.
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    What is Emergent Communication in Agents?
    Emergent Communication in Agents is an open-source PyTorch framework designed for researchers exploring how multi-agent systems develop their own communication protocols. The library offers flexible implementations of cooperative reinforcement learning tasks, including referential games, combination games, and object identification challenges. Users define speaker and listener agent architectures, specify message channel properties like vocabulary size and sequence length, and select training strategies such as policy gradients or supervised learning. The framework includes end-to-end scripts for running experiments, analyzing communication efficiency, and visualizing emergent languages. Its modular design allows easy extension with new game environments or custom loss functions. Researchers can reproduce published studies, benchmark new algorithms, and probe compositionality and semantics of emergent agent languages.
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