Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv AI

Jul 20

CAMMAR: Culture-Aware Matryoshka for Metaphorical Arabic Representations

arXiv:2607. 15847v1 Announce Type: cross Abstract: Metaphor in Arabic is a culturally grounded mechanism for constructing meaning, encoding cultural knowledge that shapes interpretation.

By Suzan Awinat, Alfonso Ortega del Puente

llms rag benchmarks

arXiv AI

Jul 20

AV-JEPA: Extending LeJEPA to Audio-Visual Self-Supervised Learning

arXiv:2607. 15295v1 Announce Type: cross Abstract: We present AV-JEPA, an elegant multimodal extension of LeJEPA to audio-visual self-supervised learning.

By Benjamin Robson, Santeri Mentu, Wenshuai Zhao, Arno Solin

llms rag diffusion computer-vision multimodal safety

arXiv AI

Jul 20

Precise but Uncoupled: Reviewer Precision Does Not Guarantee Critique Uptake in Multi-Agent Math Reasoning

arXiv:2607. 15388v1 Announce Type: new Abstract: Many math- and science-oriented agent systems use hierarchical designs with specialized reviewer roles, assuming that a dedicated review stage should help turn wrong candidates into correct ones.

By Chih-Hsuan Yang, Jingyan Jiang, Vikram Vasudevan, Cheng-Hau Yang, Huihuo Zheng, Le Chen, Eliu A. Huerta, Venkatram Vishwanath, Ian T. Foster, Rajeev Thakur

llms rag agents

arXiv AI

Jul 20

A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasms

arXiv:2607. 15480v1 Announce Type: new Abstract: As artificial intelligence (AI) systems increasingly impact society, ensuring their ethical and trustworthy deployment has become a global priority.

By Michael Papademas, Xenia Ziouvelou, Kostas Karpouzis, Vangelis Karkaletsis

rag safety

arXiv AI

Jul 20

Induction in Both Directions: A Mechanistic Analysis of In-Context Learning in Masked Diffusion Language Models

arXiv:2607. 15893v1 Announce Type: cross Abstract: While the internal mechanisms of autoregressive (AR) transformers have been studied extensively, much less is known about diffusion language models (DLMs), an emerging alternative that generates text by iterative denoising.

By Andy Catruna, Emilian Radoi

llms rag diffusion

arXiv Machine Learning

Jul 20

Interpretable Role-Based Clustering in Multi-Layer Financial Networks

arXiv:2507. 00600v3 Announce Type: replace-cross Abstract: Understanding the functional roles of financial institutions within interconnected markets is critical for effective supervision, systemic risk assessment, and resolution planning.

By Christian Franssen, Thao Le, Iman van Lelyveld, Bernd Heidergott

rag

arXiv AI

Jul 20

NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning

arXiv:2607. 15776v1 Announce Type: new Abstract: OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and bioinformatics.

By Hui Yang, Jiaoyan Chen, Yiping Song, Renate Schmidt, Wen Zhang

llms rag

Hugging Face Trending Papers

Jul 19

SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark Generation

High quality temporal graph benchmarks with rich semantics and ground-truth anomaly labels are essential for training graph neural networks, yet remain scarce due to privacy constraints and annotation costs. We present SAGA (Synthetic Agentic Graph Architecture), a system for generating large-scale, semantically rich temporal graphs via a four-phase pipeline.

llms rag agents benchmarks safety

Towards Data Science

Jul 19

Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval

Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.

By Kezhan Shi

llms rag

Towards Data Science

Jul 17

One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited

Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited appeared first on Towards Data Science .

By angela shi

rag

Towards Data Science

Jul 17

A Production RAG Pipeline in Action: Every Answer Typed and Cited

Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post A Production RAG Pipeline in Action: Every Answer Typed and Cited appeared first on Towards Data Science .

By angela shi

rag

arXiv AI

Jul 17

T5-CSBoost: Adversarial Perturbation Resistant LLM Fingerprinting

arXiv:2607. 14113v1 Announce Type: cross Abstract: While many AI-generated text (AIGT) detectors achieve strong performance on clean inputs, their accuracy degrades significantly under light paraphrasing, word substitutions, character edits, and distribution shifts.

By Gayan K. Kulatilleke, Mahsa Baktashmotlagh, Siamak Layeghy, Marius Portmann

llms rag benchmarks safety

arXiv AI

Jul 17

A Modern Multimodal Assistant on a 6 GB 2011 GPU: Stage-Validated, All-GPU CUDA Inference for Fermi

arXiv:2607. 14568v1 Announce Type: cross Abstract: A companion study ran a 35B mixture-of-experts model on a 2011 NVIDIA Tesla C2075 (Fermi, sm_20, 6GB) as a GPU-prefill/CPU-decode hybrid, because the 4-bit model did not fit in device memory (arXiv:2606.

By A. C. Opus, J. Q. Lu

rag multimodal benchmarks

arXiv AI

Jul 17

SceneBind: Binding What and Where Across Vision, Audio and Language

arXiv:2607. 15265v1 Announce Type: cross Abstract: We present SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across vision, audio and language.

By Mingfei Chen, Zijun Cui, Ruoke Zhang, Hyeonggon Ryu, Eli Shlizerman

rag multimodal benchmarks safety

arXiv AI

Jul 17

MathCoPilot: An Interactive System for Human-AI Symbiotic Paradigm of Mathematical Research

arXiv:2607. 14582v1 Announce Type: new Abstract: Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated proposition.

By Junjie Zhang, Jiayu Liu, Wenbin Liu, Zhenya Huang, Doudou Wang, Yan Jiang, Leiye Xu, Tao Xiong, Wen Huang, Qi Liu, Guoping Hu, Enhong Chen, Mengping Zhang, Xiangdong Ye

llms rag agents benchmarks

arXiv AI

Jul 17

Are LLM-Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent

arXiv:2607. 14541v1 Announce Type: new Abstract: Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads.

By Lingyun Yang, Yuxiao Wang, Shenghao Liang, Linfeng Yang, Daocheng Ying, Chunbo You, Rui Zhang, Luping Wang, Yinghao Yu, Guodong Yang, Liping Zhang

llms rag agents benchmarks

arXiv AI

Jul 17

ReportMedSAM: Guiding Segmentation Through Radiology Reports

arXiv:2607. 14116v1 Announce Type: cross Abstract: Free-form radiology reports contain rich clinical descriptions, yet converting them for reliable segmentation remains challenging due to the inherent variability of natural language.

By Anghong Du, Theodoros N. Arvanitis, Colin Watts, Alejandro F. Frangi, Le Zhang

rag computer-vision multimodal

arXiv AI

Jul 17

In-Place Tokenizer Expansion for Pre-trained LLMs

arXiv:2607. 15232v1 Announce Type: cross Abstract: A tokenizer fixed at the start of pre-training allocates vocabulary in proportion to the pre-training corpus, reflecting the deployment priorities at that time.

By Jimmy T. H. Smith, Tarek Dakhran, Alberto Cabrera, Simon S. Lee, Paul Pak, Aditya Tadimeti, Tim Seyde, Maxime Labonne, Alexander Amini, Mathias Lechner

llms rag nlp

arXiv AI

Jul 17

SmartRAG: Native Graph-Based RAG for Mobile Device

arXiv:2607. 14661v1 Announce Type: new Abstract: Deploying large language models (LLMs) as personal assistants on mobile devices demands privacy, low latency, and offline availability, yet the computational cost of giant models clashes with strict edge-hardware budgets.

By Zhihan Jiang, Meng Li, Shenghao Liu, Keran Li, Ruiben Zhou, Xianjun Deng, Shuai Wang, Haipeng Dai

llms rag efficiency benchmarks safety

arXiv AI

Jul 17

Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions

arXiv:2604. 09567v2 Announce Type: replace-cross Abstract: Knowledge representation formalisms are aimed to represent general conceptual information and are typically used in the construction of the knowledge base of reasoning agent.

By Zoran Majkic

rag agents robotics

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