arXiv AI
Jun 30
CAMI: Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval
arXiv:2606. 28365v1 Announce Type: cross Abstract: RAG ingestion pipelines frequently augment search corpus index with semantic enrichment indices (e.
From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance
arXiv:2607. 24791v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for applying large language models (LLMs) to enterprise document corpora, yet naive implementations encounter hard limits as corpus scale and query complexity grow.
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arXiv AI
Jun 11
NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track
arXiv:2606. 11199v1 Announce Type: cross Abstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, where it was awarded Best Dynamic Evaluation in the text-to-text track.
arXiv AI
Jun 10
Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis
arXiv:2606. 10381v1 Announce Type: cross Abstract: Muon collider research spans accelerator physics, detector instrumentation, and high-energy phenomenology, with relevant evidence scattered across a rapidly expanding and heterogeneous body of scientific literature.
arXiv AI
Jul 14
GRASP: GRanularity-Aware Search Policy for Agentic RAG
arXiv:2607. 10463v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.
arXiv AI
Aug 5
ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG
arXiv:2608. 01269v2 Announce Type: replace-cross Abstract: Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context suited to the current query.
arXiv AI
Jul 24
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
arXiv:2607. 21324v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents.
Hugging Face Trending Papers
Jul 11
GRASP: GRanularity-Aware Search Policy for Agentic RAG
Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide when to retrieve, whether to use lexical matching or semantic similarity, and how to control context granularity to prevent irrelevant tokens from interfering with agent reasoning.
Hugging Face Trending Papers
Jul 23
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents. Yet, most prior work optimizes components in isolation rather than coordinating improvements across the pipeline.
arXiv AI
Jun 11
FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
arXiv:2605. 02411v2 Announce Type: replace Abstract: A semantic gap separates how users describe tasks from how tools are documented.
arXiv AI
Jun 16
S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents
arXiv:2606. 15367v1 Announce Type: new Abstract: Deep research agents aim to solve complex knowledge-intensive tasks through long-horizon planning, evidence gathering, reasoning, and report generation.
arXiv Machine Learning
Jul 31
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
arXiv AI
Jul 28
A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility
arXiv:2607. 24663v1 Announce Type: cross Abstract: Scientific user facilities accumulate decades of operational knowledge that no single search index covers: electronic logbooks, technical documents, internal wikis, operations chat messages, maintenance records, and live control-system data.