ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget
ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget explores Generate scalable and verifiable training data for search agents using a frugal, open-source framework to improve performance on complex queries.. Commercial viability score: 7/10 in Search Agents.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisAligning Large Language Models with Searcher PreferencesViability: 6.0
- Builds On ThisEvaluating the Search Agent in a Parallel WorldViability: 5.0
- Builds On ThisRetrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning CapabilitiesViability: 5.0
- Builds On ThisCompute Allocation for Reasoning-Intensive Retrieval AgentsViability: 4.0
- Builds On ThisGISA: A Benchmark for General Information-Seeking AssistantViability: 6.0
Related Resources
- AI research agents(glossary)