{"total":10,"items":[{"citing_arxiv_id":"2605.12335","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records","primary_cat":"cs.IR","submitted_at":"2026-05-12T16:17:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"motivating this work is that retrieval provides a scalable mechanism for incorporating long-range historical context without forcing the model to encode the entire trajectory within its fixed internal state. Given the remarkable success of Retrieval-Augmented Generation [17] (RAG) techniques in Natural Language Processing (NLP) and their ability to leverage external knowledge beyond the training data of the base Large Language Model (LLM) [18, 19, 20, 21], the same concept can be applied for retrieving and integrating historical EHR data. In other words, by considering the patient's historical events as retrievable pieces of information, an EHR foundation model can dynamically condition on what matters the most for the patient's current state. This perspective shifts longitudinal modeling from a purely sequential problem into a retrieval-augmented one."},{"citing_arxiv_id":"2605.01302","ref_index":75,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation","primary_cat":"cs.CL","submitted_at":"2026-05-02T07:22:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CoRM-RAG uses a cognitive perturbation protocol to simulate biases and trains an Evidence Critic to retrieve documents that support correct decisions even under adversarial query changes.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"IEEE Transactions on Knowledge and Data Engineering(2026). [74] Yuhan Yang, Jie Zou, Guojia An, Jiwei Wei, Yang Yang, and Heng Tao Shen. 2026. Unleashing the potential of neighbors: diffusion-based latent neighbor generation for session-based recommendation. InProceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1. 1787-1796. [75] Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. 2023. Making retrieval-augmented language models robust to irrelevant context.arXiv preprint arXiv:2310.01558(2023). [76] Bo Yuan, Yulin Chen, Yin Zhang, and Wei Jiang. 2024. Hide and seek in noise labels: Noise-robust collaborative active learning with llms-powered assistance. InProceedings of the 62nd Annual Meeting of the Association for Computational"},{"citing_arxiv_id":"2604.26686","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation","primary_cat":"cs.SE","submitted_at":"2026-04-29T13:51:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EVOREC integrates locate-then-edit model editing with FA-constrained decoding to improve LLM-based service recommendation under evolution, reporting 25.9% average relative gain in Recall@5 over baselines and 22.3% over fine-tuning in dynamic scenarios.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25847","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling","primary_cat":"math.OC","submitted_at":"2026-04-28T16:53:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Agora-Opt uses decentralized debate among LLM agent teams plus a read-write memory bank to produce more accurate optimization models from text than prior LLM methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17458","ref_index":293,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval","primary_cat":"cs.AI","submitted_at":"2026-04-19T14:18:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.14222","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documents","primary_cat":"cs.IR","submitted_at":"2026-04-14T10:48:13+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Tree reasoning outperforms vector search on complex document queries but a hybrid approach balances results across tiers, with validation showing an 11.7-point gap on real finance documents.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06279","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Plasma GraphRAG: Physics-Grounded Parameter Selection for Gyrokinetic Simulations","primary_cat":"physics.plasm-ph","submitted_at":"2026-04-07T10:04:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Plasma GraphRAG automates physics-grounded parameter selection for gyrokinetic simulations via a domain-specific knowledge graph and LLMs, reporting over 10% better quality and up to 25% fewer hallucinations than standard RAG.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.09803","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Retrieval as a Decision: Training-Free Adaptive Gating for Efficient RAG","primary_cat":"cs.CL","submitted_at":"2025-11-12T23:09:52+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TARG uses uncertainty scores from a short no-context draft to gate retrieval in RAG, matching Always-RAG accuracy while cutting retrievals by 70-90% on QA benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.04338","ref_index":91,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"In-depth Analysis of Graph-based RAG in a Unified Framework","primary_cat":"cs.IR","submitted_at":"2025-03-06T11:34:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A unified framework and large-scale comparison of graph-based RAG methods on QA tasks yields new high-performing variants obtained by recombining existing components.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.09891","ref_index":63,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation","primary_cat":"cs.IR","submitted_at":"2025-02-14T03:28:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ArchRAG proposes attributed-community hierarchical indexing and LLM clustering to improve accuracy and lower token usage in graph-based retrieval-augmented generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}