SCMRAG: Self-Corrective Multihop Retrieval Augmented Generation System for LLM Agents
Bibliographic record
Abstract
Existing Retrieval-Augmented Generation (RAG) systems primarily depend on static knowledge vectorstores which combine semantic similarity algorithms with reranking. This often leads to outdated information and retrieval errors. In this paper, we propose SCMRAG, a Self-Corrective Multihop Retrieval Augmented Generation system for LLM agents. We introduce an LLM-assisted dynamic knowledge graph creation step to enhance information retrieval and mitigate hallucinations. Unlike traditional RAG systems, SCMRAG includes a self-corrective agent driven mechanism that autonomously identifies and retrieves missing information from external web sources. Furthermore, SCMRAG's internal reasoning agent determines whether the knowledge graph provides sufficient information or if a corrective step is needed. It further improves retrieval accuracy and efficiency. We benchmark the effectiveness of SCMRAG on five datasets - MultiHop-RAG, ARC AI2, PopQA, PubHealth, and WikiBio; showing significant improvements in retrieval precision and hallucination reduction across diverse tasks. Our results highlight SCMRAG's potential to redefine how LLM agents interact with knowledge bases, offering a more adaptable and reliable solution for a wide range of applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".