Exploring Information Seeking Agent Consolidation

📰 ArXiv cs.AI

Learn to consolidate information-seeking agents into a single foundation model for scalable cross-domain deployment

advanced Published 25 Jun 2026

Action Steps

Build a foundation agentic model using data-level mixing paradigm
Train the model on a diverse dataset to enable cross-domain deployment
Compare the performance of the consolidated model with specialized systems
Apply the consolidated model to various knowledge-intensive tasks
Evaluate the scalability of the consolidated model in real-world scenarios

Who Needs to Know This

AI researchers and engineers can benefit from this study to develop more efficient and scalable information-seeking agents, while product managers can apply the findings to improve knowledge-intensive task systems

Key Insight

💡 Consolidating information-seeking agents into a single foundation model can enable scalable and cross-domain deployment

Key Takeaways

Learn to consolidate information-seeking agents into a single foundation model for scalable cross-domain deployment

Full Article

Title: Exploring Information Seeking Agent Consolidation

Abstract:
arXiv:2602.00585v2 Announce Type: replace Abstract: Information-seeking agents have emerged as a powerful paradigm for knowledge-intensive tasks, yet today's systems remain specialized for the open web, documents, or local knowledge bases, hindering scalable and cross-domain deployment. We present the first systematic empirical study of consolidating these information-seeking agents into a single foundation agentic model. We compare two paradigms -- \emph{data-level mixing}, which trains a unifi

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