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Ray Serve for Specialized Analysis Tool

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Linked challenge: 'ARC-AGI' Research Agents for Complex Problem Solving

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Linked challenge

'ARC-AGI' Research Agents for Complex Problem Solving

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Original prompt text with formatting preserved for inspection.

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Create a simple specialized analysis microservice (e.g., a mathematical solver, a logical validator) using Ray Serve. This service should expose an API endpoint that takes structured input and returns a calculated result. Integrate this as a tool that your 'Problem Decomposer' or 'Critical Evaluator' agent can call via CrewAI to perform complex, deterministic calculations or validations.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

'ARC-AGI' Research Agents for Complex Problem Solving

This challenge tasks you with building a collaborative multi-agent system using CrewAI. The goal is to tackle a complex, abstract research problem, simulating an 'ARC-AGI-like' scenario where agents must demonstrate advanced reasoning, synthesis, and problem-solving capabilities to produce an expert-level research report. Your CrewAI setup will include specialized agents such as a 'Problem Decomposer' (Gemini 2.5 Pro), a 'Knowledge Synthesizer' (Gemini 2.5 Pro), and a 'Critical Evaluator' (GPT-4o). These agents will collaborate, utilizing tools like a Pinecone vector database for persistent memory and context management, and leveraging Ray Serve for efficient deployment of custom analysis tools or specialized models. The challenge emphasizes orchestrating sophisticated agent workflows to go beyond simple information retrieval and truly engage in deep, structured reasoning to arrive at novel insights and solutions, with the final output evaluated for its comprehensiveness and conceptual depth.

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