arXiv:2605.03344v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as math and code generation. We challenge this assumption by showing that the limitation lies not in RAG itself, but in the choice of corpus. Instead of retrieving documents, we propose retrieving thinking traces, i.e., intermediate thinking trajectories generated during problem solving attempts. We show that thinking traces are already a strong retrieval source, a
The continuous advancements and limitations of current RAG models necessitate innovative approaches to improve reasoning capabilities in AI, making this research timely.
This research redefines RAG's potential by expanding its application to complex reasoning tasks, which could significantly enhance advanced AI systems.
RAG's utility extends beyond knowledge-intensive tasks to include reasoning-intensive problems like math and code generation, by utilizing internal 'thinking traces' rather than just external documents.
- · AI developers
- · Companies using RAG for complex tasks
- · AI infrastructure providers
- · AI models without advanced reasoning capabilities
- · Traditional RAG implementations
Retrieval Augmented Generation (RAG) models will demonstrate improved performance on logical reasoning and problem-solving tasks.
This improvement could lead to more robust and autonomous AI agents capable of handling a wider array of complex, multi-step problems.
Enhanced reasoning in AI might accelerate progress in fields like scientific discovery, advanced programming, and automated decision-making, potentially impacting white-collar workflows significantly.
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