What Survives Into Context: A Diagnostic for Budget-Constrained Multi-Hop RAG and When Submodular Evidence Packing Improves It
arXiv:2607.00725v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) under a fixed reader-context budget forces a selection problem: of the evidence retrieved, only a fraction can be shown to the reader. We argue that document recall -- the standard retrieval metric -- is the wrong quantity to optimize in this regime, and we make two contributions. First, as a general contribution, we introduce answer-in-context, a diagnostic that measures whether a gold answer survives as a contiguous span in the packed reader context (not the retrieved set). It predicts answer F1 better than
The proliferation of advanced RAG systems and the increasing demand for efficient LLM deployment under budget constraints make optimizing context utilization critical.
This research introduces a novel diagnostic and a method to improve the efficiency and accuracy of retrieval-augmented generation by better managing the limited context window of large language models, directly impacting the performance and cost of AI applications.
The focus for RAG optimization shifts from raw document recall to ensuring the 'answer-in-context' for better downstream performance, with submodular evidence packing offering a concrete improvement method.
- · AI developers
- · Enterprises deploying RAG
- · Researchers in NLP
- · Inefficient RAG systems
- · Organizations with high LLM inference costs
More effective and cost-efficient deployment of RAG-based AI applications, particularly those requiring multi-hop reasoning.
Accelerated development of domain-specific AI agents and automated workflows due to improved contextual understanding.
Increased accessibility and broader adoption of advanced AI functionalities across various industries as performance-to-cost ratios improve.
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Read at arXiv cs.CL