{"total":14,"items":[{"citing_arxiv_id":"2605.20084","ref_index":19,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"BalanceRAG: Joint Risk Calibration for Cascaded Retrieval-Augmented Generation","primary_cat":"cs.CL","submitted_at":"2026-05-19T16:38:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BalanceRAG uses sequential graphical testing on a 2D lattice of threshold pairs to certify safe operating points that meet target risk levels in cascaded RAG while increasing coverage.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19366","ref_index":218,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science 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Queries","primary_cat":"cs.IR","submitted_at":"2026-04-21T14:43:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SG-RAG frames retrieval as subgraph matching to ensure LLMs meet every condition in factual queries and reports large gains over baselines on a new 120k-pair ERQA dataset.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17337","ref_index":31,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning","primary_cat":"cs.AI","submitted_at":"2026-04-19T09:05:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AutoSearch applies RL with a self-answering reward to adaptively determine minimal sufficient search depth in agentic RAG, reducing over-searching while maintaining answer quality on complex questions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17237","ref_index":31,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"HeadRank: Decoding-Free Passage Reranking via Preference-Aligned Attention Heads","primary_cat":"cs.IR","submitted_at":"2026-04-19T03:43:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"HeadRank lifts preference optimization into attention space via entropy-regularized head selection and distribution regularizers to sharpen discriminability for efficient listwise reranking.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2405.17428","ref_index":138,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models","primary_cat":"cs.CL","submitted_at":"2024-05-27T17:59:45+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"NV-Embed achieves first place on the MTEB leaderboard across 56 tasks by combining a latent attention layer, causal-mask removal, two-stage contrastive training, and data curation for LLM-based embedding models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2402.03216","ref_index":70,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation","primary_cat":"cs.CL","submitted_at":"2024-02-05T17:26:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"M3-Embedding is a single model for multi-lingual, 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models","primary_cat":"cs.CL","submitted_at":"2023-03-16T01:04:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ART automatically generates multi-step reasoning programs with tool integration for LLMs, yielding substantial gains over few-shot and auto-CoT prompting on BigBench and MMLU while matching hand-crafted CoT on most tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2202.08906","ref_index":54,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"ST-MoE: Designing Stable and Transferable Sparse Expert Models","primary_cat":"cs.CL","submitted_at":"2022-02-17T21:39:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ST-MoE introduces stability techniques for sparse expert models, allowing a 269B-parameter model to achieve 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