Budgeted Multi-Hop Retrieval Agents with Evidence-Chain Reliability and Cost-Aware Abstention
DOI:
https://doi.org/10.60087/jklst.vol5.n2.006Keywords:
multi-hop retrieval, retrieval-augmented generation, reciprocal rank fusion, dynamic stopping, calibration, selective prediction, abstention, evidence chainsAbstract
Fixed top-k retrieval spends the same context budget on every question without indicating whether a multi-document evidence chain is complete. This study evaluates a budgeted agent on MultiHop-RAG using all 609 documents and 2,556 questions. The 1,534/511/511 train/validation/test split kept each evidence group in one partition. The pipeline compared BM25, 256-dimensional latent-semantic retrieval, equal and weighted reciprocal rank fusion, supervised reranking, fixed depth, and calibrated dynamic stopping. A sparse reader, null detector, and isotonic calibration supported answer, evidence, citation, cost, and abstention evaluation. On 451 answerable test questions, reranking achieved 0.9069 Recall@10, 0.8064 nDCG@10, and 0.7428 complete-chain retrieval. The dynamic agent used a mean of 544.16 tokens and obtained 0.6184 F1, compared with 666.29 tokens and 0.6243 F1 for the null-gated Reranker-8 policy. Its paired F1 difference was -0.0059 with a 95% cluster-bootstrap interval of [-0.0246, 0.0115], while the 122.13-token reduction remained significant after Holm adjustment. Isotonic calibration reduced expected calibration error from 0.2671 to 0.0321. Selective answering operated at 0.3326 coverage and 0.2200 exact-match risk. Snippet retention and citation precision, rather than candidate generation alone, limited joint reliability. The results establish a measured quality-cost trade-off and identify where deeper retrieval still fails to improve answers.
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