Retrieval-augmented generation

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

6,643 stories · RSS feed

arXiv AI

Jun 16

HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation

arXiv:2606. 15331v1 Announce Type: cross Abstract: Generative recommendation models that formulate the task as sequence generation overcome the objective fragmentation problem of traditional cascade architectures, yet existing approaches still suffer from flat semantic representations lacking hierarchical structure for multi-step reasoning and an externally constructed chain-of-thought (CoT) that requires expensive annotations and remains disconnected from the generation objective.

By Shuqi Zhao, Jingsong Su, Xiang Liu, Xingzhi Yao, Yiming Qiu, Huimu Wang, Liang Lin, Pengbo Mo, Mingming Li, Jiao Dai, Jizhong Han, Songlin Hu

rag efficiency safety

arXiv AI

Jun 16

DeepRoot: A KG-Coordinated Multi-Agent System for Therapeutic Reasoning over Historical Medical Texts

arXiv:2606. 15931v1 Announce Type: cross Abstract: Historical medical archives and traditional medicines hold immense potential for drug discovery and remain a primary source for current drug development.

By Zijian Carl Ma, Sean J. Wang, Sijbren Kramer, Li Erran Li

llms rag agents

arXiv AI

Jun 16

JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics

arXiv:2606. 14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling.

By Guillaume Letellier (LPCC), Antonin Vacheret (LPCC), Fr\'ed\'eric Jurie

llms rag nlp benchmarks

arXiv AI

Jun 16

Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks

arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.

By Mo Shakiba, Rana Rokni, Mohammad Mohammadi, Nima Dehghani

rag

arXiv Machine Learning

Jun 16

Ricci-Filtration: Boosting Retrieval-Augmented Generation Reranker to Query-Answer Tasks by Discrete Ricci Flow

arXiv:2606. 15482v1 Announce Type: cross Abstract: Ricci flow is a curvature-guided diffusion process that deforms space by shrinking regions of high positive curvature and expanding those with negative curvature.

By Tian Qin, Wei-Min Huang

rag diffusion benchmarks

arXiv AI

Jun 16

Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering

arXiv:2606. 16494v1 Announce Type: cross Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base.

By Jieyuan Liu, Jianyang Gu, Shijie Chen, Jefferson Chen, Zhen Wang

llms rag nlp multimodal benchmarks safety

arXiv AI

Jun 16

RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking

arXiv:2604. 20623v2 Announce Type: replace-cross Abstract: Traditional change detection identifies where changes occur, but does not explain what changed in natural language.

By Roie Kazoom, Yotam Gigi, George Leifman, Tomer Shekel, Genady Beryozkin

rag computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 16

Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

arXiv:2605. 09697v3 Announce Type: replace-cross Abstract: In many real-world computer vision applications, including medical imaging and industrial inspection, binary classification tasks are characterized by a severe scarcity of positive samples.

By Radhika Amar Desai, Modigari Narendra

rag computer-vision data-engineering

arXiv AI

Jun 16

G-Loss: Graph-Guided Fine-Tuning of Language Models

arXiv:2604. 25853v3 Announce Type: replace-cross Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure.

By Aditya Sharma, Vinti Agarwal, Rajesh Kumar

llms rag nlp fine-tuning benchmarks

arXiv AI

Jun 16

Near--Real-Time Conflict-Related Fire Detection in Sudan Using Unsupervised Deep Learning

arXiv:2512. 07925v4 Announce Type: replace-cross Abstract: Ongoing armed conflict in Sudan highlights the need for rapid monitoring of conflict-related fire-affected areas.

By Kuldip Singh Atwal, Dieter Pfoser, Daniel Rothbart

rag

arXiv AI

Jun 16

A Unified Definition of Hallucination: It's The World Model, Stupid!

arXiv:2512. 21577v3 Announce Type: replace-cross Abstract: Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs.

By Emmy Liu, Varun Gangal, Chelsea Zou, Michael Yu, Xiaoqi Huang, Alex Chang, Zhuofu Tao, Karan Singh, Sachin Kumar, Steven Y. Feng

llms rag benchmarks safety

arXiv AI

Jun 16

Model Graph Inductive Learning for Knowledge Graph Completion

arXiv:2606. 16509v1 Announce Type: new Abstract: Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations.

By Mohommad Esmaei Khani, Mahdieh Hasheminejad, Ali Taherkhani, Hossein Hajiabolhassan

rag benchmarks

arXiv AI

Jun 16

E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory

arXiv:2601. 21714v5 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requires maintaining rigorous logical integrity over extended horizons.

By Kaixiang Wang, Yidan Lin, Jiong Lou, Zhaojiacheng Zhou, Bunyod Suvonov, Jie Li

llms rag agents benchmarks

arXiv Machine Learning

Jun 16

Self-Supervised Learning as Discrete Communication

arXiv:2602. 09764v2 Announce Type: replace-cross Abstract: Most self-supervised learning (SSL) methods learn continuous visual representations by aligning different views of the same input, offering limited control over how information is structured across representation dimensions.

By Kawtar Zaher, Ilyass Moummad, Olivier Buisson, Alexis Joly

rag computer-vision

arXiv AI

Jun 16

Privacy-Preserving Text Sanitization for Distributed Agents Collaboration via Disentangled Representations

arXiv:2606. 15335v1 Announce Type: cross Abstract: When distributed agents exchange text across organizational boundaries, privacy leakage arises not only from explicit identifiers but also from distributional signatures such as formatting conventions, vocabulary choices, and syntactic patterns.

By Xuan Liu, Hefeng Zhou, Sicheng Chen, Chao Yang, Xingcheng Xu, Jingjing Qu, Jiong Lou, Jie LI, Xia Hu

rag agents benchmarks safety

arXiv Machine Learning

Jun 16

Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalization

arXiv:2510. 14217v2 Announce Type: replace Abstract: The spectral properties of feature embeddings offer critical insights into model generalization and representation quality.

By Asma Jamali, Tin Sum Cheng, Rodrigo A. Vargas-Hern\'andez

llms rag benchmarks

arXiv AI

Jun 16

Phys-JEPA: Physics-Informed Latent World Models for Multivariate Time-Series Forecasting

arXiv:2606. 16076v1 Announce Type: cross Abstract: Multivariate forecasting in physical systems requires models that predict coupled temporal variables while preserving meaningful state evolution.

By Weizhi Nie, Weichao Liu, Honglin Guo, Yuting Su

rag

arXiv Machine Learning

Jun 16

PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation

arXiv:2606. 15452v1 Announce Type: new Abstract: Rare events in time series are critical to model but hard to learn due to data scarcity.

By Emre Yusuf, Ren Takahashi, Jayabrata Bhaduri

rag diffusion multimodal benchmarks safety

arXiv AI

Jun 16

DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning

arXiv:2606. 15778v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining.

By Ali Sarabadani, Mahtab Tajvidiyan

llms rag fine-tuning

arXiv Machine Learning

Jun 16

Contrastive Learning for Seismic Horizon Tracking with Domain-Specific Priors

arXiv:2606. 16271v1 Announce Type: cross Abstract: Unsupervised 3D seismic horizon tracking faces a key limitation: signal-based propagators provide accurate trace-level alignment but often fail near faults, whereas texture-driven deep models are more robust to discontinuities, typically at the cost of labeled data requirements and reduced trace-level precision.

By Alexandre Thouvenot, Lionel Boillot, Vincent Gripon

rag safety

Get the daily briefing