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ARXIV:2602.05728 · RAG OPTIMIZATION · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2602.05728RAG OPTIMIZATIONSUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
CompactRAG revolutionizes multi-hop question answering by reducing LLM calls and token overhead, offering a cost-efficient solution for knowledge-intensive reasoning.
Opportunity summary
Pain CompactRAG revolutionizes multi-hop question answering by reducing LLM calls and token overhead, offering a cost-efficient solution for knowledge-intensive reasoning.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence failed
CompactRAG revolutionizes multi-hop question answering by reducing LLM calls and token overhead, offering a cost-efficient solution for knowledge-intensive reasoning. However, existing multi-hop RAG systems remain inefficient, as they alternate between retrieval and reasoning at…
Retrieval-augmented generation (RAG) has become a key paradigm for knowledge-intensive question answering. However, existing multi-hop RAG systems remain inefficient, as they alternate between retrieval and reasoning at each step, resulting in repeated LLM calls,…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that CompactRAG achieves competitive accuracy while substantially reducing token consumption compared to iterative RAG baselines, highlighting a…
RAG Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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CompactRAG revolutionizes multi-hop question answering by reducing LLM calls and token overhead, offering a cost-efficient solution for knowledge-intensive reasoning.
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10.48550/arXiv.2602.05728CompactRAG revolutionizes multi-hop question answering by reducing LLM calls and token overhead, offering a cost-efficient solution for knowledge-intensive reasoning.
Abstract
Retrieval-augmented generation (RAG) has become a key paradigm for knowledge-intensive question answering. However, existing multi-hop RAG systems remain inefficient, as they alternate between retrieval and reasoning at each step, resulting in repeated LLM calls, high token consumption, and unstable entity grounding across hops. We propose CompactRAG, a simple yet effective framework that decouples offline corpus restructuring from online reasoning. In the offline stage, an LLM reads the corpus once and converts it into an atomic QA knowledge base, which represents knowledge as minimal, fine-grained question-answer pairs. In the online stage, complex queries are decomposed and carefully rewritten to preserve entity consistency, and are resolved through dense retrieval followed by RoBERTa-based answer extraction. Notably, during inference, the LLM is invoked only twice in total - once for sub-question decomposition and once for final answer synthesis - regardless of the number of reasoning hops. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that CompactRAG achieves competitive accuracy while substantially reducing token consumption compared to iterative RAG baselines, highlighting a cost-efficient and practical approach to multi-hop reasoning over large knowledge corpora. The implementation is available at GitHub.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
failed0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
CompactRAG revolutionizes multi-hop question answering by reducing LLM calls and token overhead, offering a cost-efficient solution for knowledge-intensive reasoning. However, existing multi-hop RAG systems remain inefficient, as they alternate between retrieval and reasoning at...
METHOD
Retrieval-augmented generation (RAG) has become a key paradigm for knowledge-intensive question answering. However, existing multi-hop RAG systems remain inefficient, as they alternate between retrieval and reasoning at each step, resulting in repeated LLM calls, high token cons...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that CompactRAG achieves competitive accuracy while substantially reducing token consumption compared to iterative RAG baselines, highligh...
WHY NOW
RAG Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10.
Notably, during inference, the LLM is invoked only twice in total - once for sub-question decomposition and once for final answer synthesis - regardless of the number of reasoning hops.
Explicitly stated in the abstract with clear numeric specification
partial
Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that CompactRAG achieves competitive accuracy while substantially reducing token consumption compared to iterative RAG baselines
Directly stated in abstract with specific dataset names and comparative language
partial
CompactRAG achieves competitive accuracy while substantially reducing token consumption compared to iterative RAG baselines
Explicitly stated in abstract with clear comparative claim
partial
In the offline stage, an LLM reads the corpus once and converts it into an atomic QA knowledge base, which represents knowledge as minimal, fine-grained question-answer pairs.
Directly described in abstract with specific technical details
partial
resolved through dense retrieval followed by RoBERTa-based answer extraction
Strongly implied in abstract and analysis, though not explicitly naming RoBERTa in abstract
partial
its efficiency depends on the quality of the initial corpus transformation, and the offline processing can be computationally intensive upfront
Directly stated in analysis section with clear limitations
partial
highlighting a cost-efficient and practical approach to multi-hop reasoning over large knowledge corpora
Directly stated in abstract but includes subjective evaluation terms
partial
the success of sub-question decomposition accuracy could vary depending on the complexity of input questions
Directly stated in analysis section as a caveat
partial
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Concepts
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CompactRAG revolutionizes multi-hop question answering by reducing LLM calls and token overhead, offering a cost-efficient solution for knowledge-intensive reasoning.
Segment
RAG Optimization
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
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Technical feasibility
partial
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DEFENSIBILITY
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