arXiv AI
Jul 22
M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation
arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.
ICICLE: Expanding Retrieval with In-Context Documents
ICICLE is an in‑context indexing framework that expands generative retrieval by supplying newly added documents as inference‑time evidence. It generates document identifiers using both parametric memory and context‑provided document‑docid pairs, employing a [COPY] routing mechanism, preference‑based calibration, and large‑context adaptation to separate context‑grounded retrieval from parametric retrieval. Experiments on MS MARCO and NQ320K demonstrate that ICICLE improves retrieval of new documents while retaining performance on previously indexed documents without retraining the model.
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arXiv AI
Aug 10
DocMemo: Dynamic Evidence Discovery via Probabilistic Memory-Guided Retrieval for Multi-Modal Document Understanding
arXiv:2608. 07067v1 Announce Type: new Abstract: Long-document understanding requires locating sparse and heterogeneous evidence across hundreds of pages, yet existing systems remain limited by static retrieval and fragile cross-round memory.
arXiv AI
Aug 18
LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents
arXiv:2608. 16185v1 Announce Type: cross Abstract: LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent.
Hugging Face Trending Papers
Aug 17
LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents
LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent. Existing retrieval-augmented approaches pre-materialize evidence via fixed chunking, embeddings, or persistent indexes: effective for lookup, yet costly, stale-prone, and committed to a granularity before the query is known.
arXiv AI
Aug 19
DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval
The paper introduces DEPT, a method that trains a single decoder-only large language model to both expand queries and encode documents for retrieval. By preserving document embeddings close to their initial cached values while allowing gradients to flow through the generator, DEPT stabilizes retrieval targets and enables efficient index reuse and online hard‑negative mining. Experiments on the BEIR benchmark with Qwen3‑4B‑Instruct‑2507 and LLaMA‑3.2‑3B‑Instruct show that DEPT outperforms training‑free, independently trained, and staged unified baselines, with ablations confirming the benefits of preservation, whitening, end‑to‑end expansion training, and online negatives.
arXiv Computation and Language
2d ago
W-RAG: Source-Aware Retrieval for Enterprise Document Generation from Heterogeneous Knowledge Bases
W-RAG is a source-aware retrieval framework designed for enterprise document generation from heterogeneous knowledge bases. It uses ontology-guided retrieval, local ranking within each knowledge base, and source-level weighting to balance evidence from diverse sources. A new dataset covering multiple document types and industry domains demonstrates that W-RAG improves document coverage and generation quality compared to standard RAG pipelines.
arXiv AI
Jun 15
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
arXiv:2504. 20734v5 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries.
arXiv AI
Jun 30
HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning
arXiv:2606. 28349v1 Announce Type: cross Abstract: Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories.
arXiv Computation and Language
2d ago
ConvergeWriter: Data-Driven Bottom-Up Article Construction
ConvergeWriter introduces a bottom‑up, data‑driven framework for long‑form document generation that first retrieves exhaustive knowledge from a source corpus and clusters it into distinct knowledge groups. These clusters then guide the creation of a hierarchical outline and the final text, ensuring the output is strictly grounded in the retrieved material and traceable to its sources. Experiments on 14B and 32B LLMs show that this approach matches or surpasses state‑of‑the‑art baselines, especially in scenarios requiring high factual fidelity and structural coherence.
arXiv AI
2d ago
MegaMem: A Retrieval Solution for Ultra-Large Context Windows
arXiv:2608.22137v1 Announce Type: new Abstract: Modern language models and agents increasingly require persistent memory for complete codebases, long interaction histories, and heterogeneous enterpri...
arXiv AI
Jun 4
MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning
arXiv:2606. 04442v1 Announce Type: cross Abstract: AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents.
arXiv AI
3d ago
EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering
arXiv:2608.21252v1 Announce Type: cross Abstract: Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationshi...