arXiv:2602.01672v2 Announce Type: replace Abstract: Search-augmented reasoning agents interleave multi-step reasoning with external retrieval, but uncontrolled retrieval can introduce redundant evidence, saturate the context, and destabilize reinforcement learning (RL). Existing outcome-based RL methods provide only sparse terminal rewards, offering limited guidance for intermediate information-acquisition decisions. We propose DeepControl, an adaptive information-control framework based on information utility, a state-dependent estimate of the marginal value of retrieved evidence. The framewo
The increasing complexity and resource demands of large language models necessitate more efficient and controlled information retrieval mechanisms to improve performance and stability.
This research advances the core intelligence of AI agents by addressing a critical challenge in effective retrieval-augmented generation, leading to more robust and capable autonomous systems.
The ability to dynamically control information flow will make search-augmented LLMs more efficient, less prone to 'context saturation,' and more adaptable in multi-step reasoning tasks.
- · AI developers
- · Enterprises deploying advanced AI agents
- · Research institutions
- · SaaS platforms leveraging LLMs
- · Inefficient LLM architectures
- · Systems heavily reliant on brute-force retrieval
Search-augmented LLMs become demonstrably more powerful and reliable for complex problem-solving.
This leads to an acceleration in the development and deployment of sophisticated AI agents across various industries.
The enhanced agency of these systems could further automate white-collar tasks, impacting labor markets and organizational structures.
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Read at arXiv cs.CL