ConCise: Training-Free Conclusion-Chain State Compression for Cost-Efficient Multi-Step RAG Services
arXiv:2606.28361v1 Announce Type: cross Abstract: Multi-step retrieval-augmented generation (RAG) has been widely deployed as LLM-powered web services for complex question answering, where iterative retrieval-reasoning rounds deliver strong multi-hop accuracy. However, this paradigm causes historical documents and reasoning traces to accumulate across rounds, inflating cumulative input tokens approximately as $O(N^2)$ with progressively increasing noise density. In API-based service architectures, such growth directly amplifies per-request billing cost, network payload, and response latency. E
The increasing deployment of multi-step RAG in LLM-powered services highlights the growing challenge of managing accumulating input tokens and associated costs, making this research timely.
This development addresses a critical cost and efficiency bottleneck in advanced AI services, directly impacting the scalability and economic viability of complex AI applications.
The proposed 'training-free conclusion-chain state compression' method offers a way to significantly reduce operational costs and improve performance for multi-step RAG, making these services more accessible and efficient.
- · AI service providers
- · LLM application developers
- · Cloud infrastructure providers
- · Inefficient RAG architectures
- · High-cost LLM API users
Reduced operational costs and improved latency for complex AI applications using multi-step RAG.
Accelerated deployment and broader adoption of sophisticated AI agentic systems due to lower operational barriers.
Increased competition among AI service providers as cost efficiencies enable more advanced offerings at better price points.
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Read at arXiv cs.AI