P2R builds LLM-generated structured expertise profiles and uses coarse-to-fine hybrid retrieval plus rubric-scoring committees to match papers to reviewers, outperforming paper-to-paper baselines on NeurIPS, SIGIR, and SciRepEval.
A comprehensive survey on vector database: Storage and retrieval technique, challenge
8 Pith papers cite this work. Polarity classification is still indexing.
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Benchmark study shows DCO methods for vector similarity search are not reliable silver bullets due to high sensitivity to data properties and hardware, making them unsuitable for production deployment.
FINER-SQL boosts 3B-parameter small language models to 67.73% and 85% execution accuracy on BIRD and Spider benchmarks via dense memory and atomic rewards in group relative policy optimization, matching larger LLMs at lower latency.
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.
RAGRoute introduces a neural router for federated RAG that dynamically selects relevant sources, reducing communication by up to 80.65% and latency by 52.50% while preserving accuracy on three benchmarks.
Presents a training-free personalization toolkit for LVLMs that extracts features via vision foundation models, applies RAG for instance retrieval, and uses visual prompting for multi-concept adaptation on images and videos, claiming SOTA results on a new real-world benchmark.
Introduces a scalable AI skill framework for autonomous microkinetics discovery that automates workflows and evaluates surrogate reliability.
A RAG-based virtual assistant was developed and evaluated to deliver accurate, context-specific responses for students navigating university project regulations.
citing papers explorer
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Towards Explorative IRBL: Combining Semantic Retrieval with LLM-driven Iterative Code Exploration
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.
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Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing
RAGRoute introduces a neural router for federated RAG that dynamically selects relevant sources, reducing communication by up to 80.65% and latency by 52.50% while preserving accuracy on three benchmarks.
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Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Presents a training-free personalization toolkit for LVLMs that extracts features via vision foundation models, applies RAG for instance retrieval, and uses visual prompting for multi-concept adaptation on images and videos, claiming SOTA results on a new real-world benchmark.