{"publication_id":"5c993ba1-5ebb-4a12-b4dc-a4fe2418a927","traces":[{"claim_id":"claim_1","claim":"Does retrieval augmented generation show a consistent direction-bearing association in the selected source bundle, and where do null/mixed or context-only receipts bound the claim?","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records","doi":"10.64898/2026.01.24.26344477","url":"https://doi.org/10.64898/2026.01.24.26344477","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_4","study":"Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework","doi":"10.1109/acdsa67686.2026.11467963","url":"https://doi.org/10.1109/acdsa67686.2026.11467963","support_kind":"candidate_source_row","population":"rag recall tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_5","study":"Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning","doi":"10.30871/jaic.v10i1.11738","url":"https://doi.org/10.30871/jaic.v10i1.11738","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"}]},{"claim_id":"claim_2","claim":"3 of 5 selected receipts are direction-bearing for the selected source contexts; 0 receipt(s) are null/mixed and 2 are context/model only. This is a bounded source-literature signal, not a pooled effect.","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records","doi":"10.64898/2026.01.24.26344477","url":"https://doi.org/10.64898/2026.01.24.26344477","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_4","study":"Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework","doi":"10.1109/acdsa67686.2026.11467963","url":"https://doi.org/10.1109/acdsa67686.2026.11467963","support_kind":"candidate_source_row","population":"rag recall tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_5","study":"Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning","doi":"10.30871/jaic.v10i1.11738","url":"https://doi.org/10.30871/jaic.v10i1.11738","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"}]},{"claim_id":"claim_3","claim":"This receipt-backed scoping note has one bounded signal: retrieval augmented generation shows policy/exposure estimates plus separate descriptive evidence across this 5-source primary bundle (2026-2026). Evidence role grouping: direction-bearing receipts: 3; null/mixed metric-scope caveat receipts: 0; context/antecedent/model receipts: 2 excluded from effect support. The source facts cover 4 population/setting context(s) and 3 policy/exposure/practice context(s), so this is a scoping signal about where settings/designs diverge, without establishing a causal, policy-prescriptive, market-generalized, or pooled econometric claim. Population/setting counts are context descriptors only; they are not weighting, pooling, or aggregation evidence. The listed estimates remain source-specific across metrics and settings; they are not pooled or averaged. This is a separated policy/setting map, not a unified pooled economics claim. Named setting scope includes combined, rag F1 tasks, rag accuracy tasks, and rag recall tasks. Within-vs-across outcome rule: direction-bearing rows are only compared within the selected source contexts; unrelated receipt families are not treated as one outcome. Concrete contrast: directional association: Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation: Critically, CoRAG proves most robust in hybrid documentation settings, achieving statistically significant...; descriptive/modeling: A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings: The baseline LLM demonstrated strong performance across multiple metrics, including accuracy (0.1900) and....","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records","doi":"10.64898/2026.01.24.26344477","url":"https://doi.org/10.64898/2026.01.24.26344477","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_4","study":"Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework","doi":"10.1109/acdsa67686.2026.11467963","url":"https://doi.org/10.1109/acdsa67686.2026.11467963","support_kind":"candidate_source_row","population":"rag recall tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_5","study":"Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning","doi":"10.30871/jaic.v10i1.11738","url":"https://doi.org/10.30871/jaic.v10i1.11738","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"}]},{"claim_id":"claim_4","claim":"Role definitions: direction-bearing rows carry metric-specific effect or association text; null/mixed rows carry rejected or non-convergent metric evidence; context/model rows rank, model, or contextualize adjacent constructs. Interpretation: keep these rows separate; do not pool them or treat antecedent/modeling rows as the same estimand.","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model 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below keep direction-bearing, null/mixed metric-scope caveat, and context-only receipts separate.","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model framework for 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The selected receipts group because each carries a fact-level extraction for retrieval augmented generation; they separate by context (other source context) and metric, so they are not interchangeable evidence for one pooled claim.","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records","doi":"10.64898/2026.01.24.26344477","url":"https://doi.org/10.64898/2026.01.24.26344477","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_4","study":"Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework","doi":"10.1109/acdsa67686.2026.11467963","url":"https://doi.org/10.1109/acdsa67686.2026.11467963","support_kind":"candidate_source_row","population":"rag recall tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_5","study":"Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning","doi":"10.30871/jaic.v10i1.11738","url":"https://doi.org/10.30871/jaic.v10i1.11738","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"}]},{"claim_id":"claim_12","claim":"The signal is purely descriptive of source-level direction and scope; it cannot support a causal, policy-prescriptive, or pooled elasticity inference, and pooling across these designs would be inappropriate.","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records","doi":"10.64898/2026.01.24.26344477","url":"https://doi.org/10.64898/2026.01.24.26344477","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_4","study":"Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework","doi":"10.1109/acdsa67686.2026.11467963","url":"https://doi.org/10.1109/acdsa67686.2026.11467963","support_kind":"candidate_source_row","population":"rag recall tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_5","study":"Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning","doi":"10.30871/jaic.v10i1.11738","url":"https://doi.org/10.30871/jaic.v10i1.11738","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"}]},{"claim_id":"claim_13","claim":"Effect-support accounting: 2 of 5 receipt(s) is context/modeling-only and contributes no effect estimate; 3 receipt(s) are direction-bearing and 0 receipt(s) are null/mixed metric-scope caveats.","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records","doi":"10.64898/2026.01.24.26344477","url":"https://doi.org/10.64898/2026.01.24.26344477","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_4","study":"Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework","doi":"10.1109/acdsa67686.2026.11467963","url":"https://doi.org/10.1109/acdsa67686.2026.11467963","support_kind":"candidate_source_row","population":"rag recall tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_5","study":"Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning","doi":"10.30871/jaic.v10i1.11738","url":"https://doi.org/10.30871/jaic.v10i1.11738","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"}]},{"claim_id":"claim_14","claim":"This scoping signal would weaken if the null/mixed metric replicates in matched designs, if direction-bearing rows fail to reproduce within their named metric family, or if context/model rows become the only topic-overlapping receipts.","candidate_sources":[{"source_id":"source_1","study":"A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings","doi":"10.65205/jcct.2026.e3516","url":"https://doi.org/10.65205/jcct.2026.e3516","support_kind":"candidate_source_row","population":"rag accuracy tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_2","study":"Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation","doi":"10.48550/arxiv.2602.07086","url":"https://doi.org/10.48550/arxiv.2602.07086","support_kind":"candidate_source_row","population":"combined","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_3","study":"A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records","doi":"10.64898/2026.01.24.26344477","url":"https://doi.org/10.64898/2026.01.24.26344477","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_4","study":"Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework","doi":"10.1109/acdsa67686.2026.11467963","url":"https://doi.org/10.1109/acdsa67686.2026.11467963","support_kind":"candidate_source_row","population":"rag recall tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"},{"source_id":"source_5","study":"Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning","doi":"10.30871/jaic.v10i1.11738","url":"https://doi.org/10.30871/jaic.v10i1.11738","support_kind":"candidate_source_row","population":"rag F1 tasks","endpoint":"not extracted","effect":"not extracted","directness":"primary"}]}]}