Retrieval-augmented generation

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

6,423 stories · RSS feed

arXiv Machine Learning

Aug 10

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction

arXiv:2608. 07420v1 Announce Type: new Abstract: World models are expected to support imagination over extended temporal horizons, yet most are still trained through local few-step prediction objectives and deployed by recursively rolling out their own predictions.

By Xinyi Li, Zaishuo Xia, Chenjie Hao, Yubei Chen

rag benchmarks

arXiv AI

Aug 10

QFCQT: A Chaotically Gated Quantformer Framework for Volatile Time-Series Forecasting

arXiv:2608. 07363v1 Announce Type: new Abstract: Forecasting non-stationary time series remains difficult due to long-range dependencies, local volatility bursts, structural shifts, and nonlinear oscillatory behaviors.

By Junkai Lin, Siqi Hou, Raymond Lee

llms rag benchmarks

arXiv AI

Aug 10

SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization

arXiv:2602. 12187v2 Announce Type: replace-cross Abstract: Search-Augmented Generative Engines (SAGE) have emerged as a new paradigm for information access, bridging web-scale retrieval with generative capabilities to deliver synthesized answers.

By Sunghwan Kim, Wooseok Jeong, Serin Kim, Sangam Lee, Dongha Lee

rag benchmarks

arXiv AI

Aug 10

KReF: Training-Free Retrieval for Long-Term Time-Series Forecasting and Predictive Uncertainty

arXiv:2608. 06748v1 Announce Type: cross Abstract: Probabilistic long-term time-series forecasting commonly relies on trained models.

By Yang Zhang, Rui Su

rag benchmarks safety

arXiv AI

Aug 10

SCALE: Scientific Concept Aggregation via LLMs and Embeddings for Fine-Grained Taxonomy Extension

arXiv:2608. 07254v1 Announce Type: cross Abstract: The increasing specialization of scientific research challenges existing classification systems, which provide effective representations of broad disciplines and research topics but often fail to capture the fine-grained conceptual structure of contemporary science.

By Daniele Raimondi, Feichi Lu, Oliver Grun, Mariia Eremina, Andrea Perlato

llms rag

arXiv AI

Aug 10

GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base

arXiv:2608. 06992v1 Announce Type: cross Abstract: We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM).

By Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski

llms rag

arXiv Machine Learning

Aug 10

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.

By Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\c{c}ois Plante, Golnoosh Farnadi

llms rag fine-tuning benchmarks safety

arXiv AI

Aug 10

FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filings

arXiv:2608. 07400v1 Announce Type: new Abstract: Financial question answering is typically evaluated by answer correctness, yet in SEC filings a plausible and even numerically correct answer can be grounded in the wrong evidence.

By Sasan Mansouri, Daniel Saad, Mark Wahrenburg, Manu Weissel, Fabian Woebbeking

rag nlp benchmarks

arXiv Machine Learning

Aug 10

SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction

arXiv:2608. 06441v1 Announce Type: new Abstract: Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges.

By Guofan Yu, Sitian Chen, Zhenheng Tang, Xiaowen Chu, Amelie Chi Zhou

rag benchmarks safety data-engineering

arXiv AI

Aug 10

Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction

arXiv:2411. 00028v3 Announce Type: replace-cross Abstract: Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commercial activity level, which plays an important role in understanding urban regions and supporting decision-making.

By Zhilun Zhou, Jingyang Fan, Yu Liu, Fengli Xu, Depeng Jin, Yong Li

llms rag agents

arXiv AI

Aug 10

INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimation

arXiv:2603. 21607v2 Announce Type: replace Abstract: While retrieval-augmented generation (RAG) significantly improves the factual reliability of LLMs, it does not eliminate hallucinations, so robust uncertainty quantification (UQ) remains essential.

By Alexandra Bazarova, Andrei Volodichev, Daria Kotova, Alexey Zaytsev

llms rag benchmarks safety

arXiv AI

Aug 10

WorldMark: A Plug-and-Play World Knowledge Interface for Cross-Host Language Model Watermarking

arXiv:2608. 06416v1 Announce Type: cross Abstract: Watermarking traces the provenance of text produced by large language models by embedding statistically detectable signals during decoding.

By Song Xiao, Yuqi Yuan, Yanshuo Zhang, Kejun Zhang

llms rag

arXiv Machine Learning

Aug 10

TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution

arXiv:2603. 27723v2 Announce Type: replace Abstract: Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks.

By Yinlin Zhu, Xunkai Li, Di Wu, Wang Luo, Miao Hu, Guocong Quan

rag multimodal benchmarks safety

arXiv AI

Aug 10

Flowing Through States: Neural ODE Regularization for Reinforcement Learning

arXiv:2608. 06595v1 Announce Type: cross Abstract: Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states.

By Mohamed Ghanem, Bernd Finkbeiner

rag agents reinforcement-learning benchmarks

arXiv AI

Aug 10

CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG

arXiv:2608. 07458v1 Announce Type: cross Abstract: Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved contexts for higher efficiency, while significant information redundancy and noise still remain in the coarse-grained chunks.

By Gyuwan Kim, Cheoneum Park, Tao Yang

rag nlp efficiency

arXiv AI

Aug 10

SLED: Scalable Location Encoding via Distillation

arXiv:2608. 06612v1 Announce Type: cross Abstract: The plethora of readily available geospatial data offers exciting opportunities to learn high quality representations of the planet, but the sheer size of the Earth Observations (EO), differing modalities, and different sensor types pose significant challenges in doing so.

By Kevin Lane, Zhongying Wang, Esther Rolf, Morteza Karimzadeh

rag efficiency multimodal benchmarks

arXiv AI

Aug 10

Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation

arXiv:2608. 07176v1 Announce Type: cross Abstract: Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training large Diffusion Transformers.

By Francisco Caetano, Tim J. M. Jaspers, Haiko Middeljans, Martijn R. Jong, Rixta A. H. van Eijck van Heslinga, Floor Slooter, Albert J. de Groof, Jacques J. Bergman, Peter H. N. De With, Fons van der Sommen

llms rag diffusion computer-vision benchmarks safety

arXiv Machine Learning

Aug 10

Understanding Differentiable Embeddings Through Differential and Integral Geometry

arXiv:2608. 06809v1 Announce Type: new Abstract: How can an analyst decide whether a nonlinear dimensionality reduction embedding can be trusted?

By Xinyu Zhang, Klaus Mueller

rag

arXiv AI

Aug 10

AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies

arXiv:2608. 07065v1 Announce Type: cross Abstract: Action-chunking visuomotor policies learn from demonstrations and improve temporal consistency by predicting short action sequences rather than single-step commands.

By Jinhe Tang, Weiming Zhi

rag robotics

arXiv AI

Aug 10

Post-Grokking Collapse at the Representation-Readout Interface in Muon-Trained Transformers

arXiv:2608. 07436v1 Announce Type: new Abstract: Under the standard split, Muon gets hidden matrices and AdamW embeddings/output head.

By Ali Janati, Kaoutar El Maghraoui, Andrei Kanavalau, Anass Belfatmi

llms rag safety

Get the daily briefing