Hybrid Reasoning AI Browser for Real-time Synthesis with Gemini 2.5 Pro & DSPy
Similar to leading AI browsers, this challenge tasks you with building an advanced information synthesis engine. You will create a system that can browse the web, extract critical information, and provide real-time, verified answers to complex queries, leveraging a hybrid reasoning approach. Your solution will combine the 'instant' reasoning of OpenAI GPT 5 for quick summarization with the 'deep' reasoning capabilities of Gemini 2.5 Pro, optimized using DSPy for robust ReAct agent patterns. The system will employ adaptive thinking budgets, ensuring efficient resource use while maintaining high accuracy and providing source verification through RAG.
What you are building
The core problem, expected build, and operating context for this challenge.
Similar to leading AI browsers, this challenge tasks you with building an advanced information synthesis engine. You will create a system that can browse the web, extract critical information, and provide real-time, verified answers to complex queries, leveraging a hybrid reasoning approach. Your solution will combine the 'instant' reasoning of OpenAI GPT 5 for quick summarization with the 'deep' reasoning capabilities of Gemini 2.5 Pro, optimized using DSPy for robust ReAct agent patterns. The system will employ adaptive thinking budgets, ensuring efficient resource use while maintaining high accuracy and providing source verification through RAG.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master DSPy for programmatically optimizing prompts and improving the reliability of LLM pipelines, focusing on the ReAct (Reasoning and Acting) pattern.
Implement a custom web browsing tool for agents, capable of fetching content, parsing HTML, and performing targeted searches.
Design a hybrid reasoning system: use OpenAI GPT 5 (or a comparable 'fast' model) for initial query understanding and quick summarization, and Gemini 2.5 Pro (specifically its 'Deep Think' mode if available, or a high temperature/token count setting) for complex analysis, inference, and synthesis.
Develop adaptive thinking budgets that dynamically adjust the number of reasoning steps or the model's 'temperature' based on the perceived complexity or ambiguity of the user's query.
Build a robust RAG pipeline for grounding answers: store extracted web content and other verified knowledge in a vector database for retrieval and use in synthesis.
Implement a 'self-correction' mechanism where agents can identify conflicting information or gaps in their knowledge and initiate further browsing or deep reasoning.
Orchestrate the flow of information between the 'instant' and 'deep' reasoning modules, ensuring seamless transition and optimal resource utilization.
Deploy the AI browser as a FastAPI application, providing a simple interface for querying.
How this agent runs
The evaluation will focus on the accuracy, completeness, and verifiability of the synthesized answers, as well as the efficiency of the hybrid reasoning process. It will assess how well the system uses its tools and a...
Challenge input
String representing the user query.
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
JSON object with 'answer', 'sources' (list of URLs), 'reasoning_trace' (steps taken by agents).
- The generated answer is factually correct.
- At least 2 relevant and valid sources are provided.
- Evidence of 'Thought, Action, Observation' in reasoning_trace.
- Answer Completeness target: 90
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
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[ok] Wrote eval/examples.json
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