Adaptive AI Search Synthesis
Inspired by Google's 'AI Overviews' and AI mode, this challenge involves creating an advanced agent system for personalized, adaptive search synthesis. Leveraging LangGraph for dynamic, graph-based workflows, the system will intelligently combine traditional search results with generative AI insights from 'AI Overviews' (simulated by LLMs). The core is to use Gemini 2.5 Pro in 'Deep Think' mode for complex reasoning, query reformulation, and synthesizing coherent, well-sourced answers to intricate user queries. This system will showcase hybrid reasoning (instant retrieval and deep generative synthesis), RAG for source attribution, and adaptive thinking budgets to optimize computational resources based on query complexity. It aims to provide comprehensive answers, even for ambiguous or multi-faceted questions, simulating a personalized, intelligent research assistant.
What you are building
The core problem, expected build, and operating context for this challenge.
Inspired by Google's 'AI Overviews' and AI mode, this challenge involves creating an advanced agent system for personalized, adaptive search synthesis. Leveraging LangGraph for dynamic, graph-based workflows, the system will intelligently combine traditional search results with generative AI insights from 'AI Overviews' (simulated by LLMs). The core is to use Gemini 2.5 Pro in 'Deep Think' mode for complex reasoning, query reformulation, and synthesizing coherent, well-sourced answers to intricate user queries. This system will showcase hybrid reasoning (instant retrieval and deep generative synthesis), RAG for source attribution, and adaptive thinking budgets to optimize computational resources based on query complexity. It aims to provide comprehensive answers, even for ambiguous or multi-faceted questions, simulating a personalized, intelligent research assistant.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for defining stateful, Directed Acyclic Graph (DAG) agent workflows, including conditional transitions and cycle detection.
Implement agents that utilize Gemini 2.5 Pro's Deep Think mode for multi-step reasoning, complex data analysis, and inferential tasks.
Design a RAG system that integrates both traditional search results and LLM-generated 'AI Overviews' for comprehensive context.
Build a hybrid reasoning pipeline: an 'Instant Agent' for quick fact retrieval and a 'Deep Thinker Agent' for synthesizing complex answers, using adaptive switching logic.
Develop strategies for dynamically adjusting reasoning 'budgets' (e.g., number of LLM calls, search depth) based on inferred query complexity or user preferences.
Orchestrate multi-turn conversations where agents can ask clarifying questions or refine their search strategy based on initial results.
Ensure robust source attribution and fact-checking mechanisms for all synthesized information.
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[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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