Introduces V-RAGBench benchmark and CARVE method that selects per-chunk retrieval configurations via parallel retrievers and adaptive reranking, outperforming eight VideoRAG baselines.
DAT: Dynamic alpha tuning for hybrid retrieval in retrieval-augmented generation
6 Pith papers cite this work. Polarity classification is still indexing.
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SAGE applies a von Mises-Fisher density estimator with an adaptive threshold to route memory updates, achieving best-in-class token-F1 on LoCoMo while reducing API cost 3.4x and latency 2.5x on GPT-4o-mini.
Empirical comparison across 14 retrievers on the BRIGHT benchmark shows reasoning-specialized models can match strong accuracy with competitive speed while many large LLM bi-encoders add latency for small gains and confidence scores remain poorly calibrated.
A framework automates multi-agent system creation via LLM planning and two-stage agent recommendation, claiming higher recall than prior methods.
InSemRAG combines dynamic intent-aware hybrid retrieval and semantics-preserving chunk repair in an iterative loop, yielding 2.65 F1 gain on HotPotQA and 1.5 accuracy gain on FEVER with 4.32x lower latency than Multi-Hop RAG via SLMs.
H-RAG uses hierarchical parent-child document segmentation with hybrid retrieval and parent-level aggregation to achieve 0.4271 nDCG@5 on retrieval and 0.3241 harmonic mean on generation in a multi-turn RAG shared task.
citing papers explorer
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Rethinking RAG in Long Videos: What to Retrieve and How to Use It?
Introduces V-RAGBench benchmark and CARVE method that selects per-chunk retrieval configurations via parallel retrievers and adaptive reranking, outperforming eight VideoRAG baselines.
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SAGE: A Novelty Gate for Efficient Memory Evolution in Agentic LLMs
SAGE applies a von Mises-Fisher density estimator with an adaptive threshold to route memory updates, achieving best-in-class token-F1 on LoCoMo while reducing API cost 3.4x and latency 2.5x on GPT-4o-mini.
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Are LLM-Based Retrievers Worth Their Cost? An Empirical Study of Efficiency, Robustness, and Reasoning Overhead
Empirical comparison across 14 retrievers on the BRIGHT benchmark shows reasoning-specialized models can match strong accuracy with competitive speed while many large LLM bi-encoders add latency for small gains and confidence scores remain poorly calibrated.
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From Intent to Execution: Composing Agentic Workflows with Agent Recommendation
A framework automates multi-agent system creation via LLM planning and two-stage agent recommendation, claiming higher recall than prior methods.
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Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking
InSemRAG combines dynamic intent-aware hybrid retrieval and semantics-preserving chunk repair in an iterative loop, yielding 2.65 F1 gain on HotPotQA and 1.5 accuracy gain on FEVER with 4.32x lower latency than Multi-Hop RAG via SLMs.
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H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations
H-RAG uses hierarchical parent-child document segmentation with hybrid retrieval and parent-level aggregation to achieve 0.4271 nDCG@5 on retrieval and 0.3241 harmonic mean on generation in a multi-turn RAG shared task.