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 Machine Learning

Jul 28

Unsupervised Graph Representation Learning with Complementary View Alignment

arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.

By Zengyi Wo, Shiyu Zhang, Qiyao Peng, Tianpeng Li, Xuan Guo

rag benchmarks safety

arXiv Machine Learning

Jul 28

XGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss

arXiv:2607. 23149v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks provide an efficient randomized learning framework for classification.

By Yogesh Kumar, Mudasir Ganaie

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arXiv Machine Learning

Jul 28

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning

arXiv:2607. 23837v1 Announce Type: new Abstract: Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially, leading to catastrophic forgetting.

By Reza Rahimi Azghan, Gautham Krishna Gudur, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh

llms rag fine-tuning benchmarks

arXiv AI

Jul 28

AutoCluster, AutoTopicModeling, AutoTrendAnalysis: A Complete AutoML Pipeline for Predicting Emerging Trends

arXiv:2607. 22641v1 Announce Type: cross Abstract: Predicting emerging trends is vital for businesses, researchers, and policymakers; yet traditional approaches often lack scalability and adaptability.

By Ahmed Abolfadl, Marwa Mahmoud Abla, Mervat Abu-Elkheir, Maggie Mashaly

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arXiv AI

Jul 28

Language-Routed RAG and Direct Option Scoring for Multilingual Financial QA: DS@GT at FinMMEval

arXiv:2607. 22841v1 Announce Type: cross Abstract: We present DS@GT's submission to FinMMEval 2026 Task 1, a multilingual financial exam question answering benchmark spanning English, Spanish, Greek, Chinese, and Hindi.

By Justice Ayela, Kabir Sahni

llms rag nlp benchmarks

arXiv Machine Learning

Jul 28

ADVERSARIAL: And-Inverter Graph-Assisted Hardware Trojan Detection At Scale

arXiv:2607. 23882v1 Announce Type: new Abstract: Modern System-on-Chip (SoCs) often contain hundreds of millions to tens of billions of gates, making existing Hardware Trojan (HT) detection methods impractical due to their immense scale.

By Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband

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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.

By Rajat Sainju, Dariusz Jarosz, Hairong Shang, Michael Prince, Ryan M. Aydelott, Mathew J. Cherukara, Yine Sun, Michael D. Borland

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arXiv Machine Learning

Jul 28

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.

By Zeyu Zhang, Xue Li, Iacer Calixto, Paul Groth, Sebastian Schelter

llms rag fine-tuning benchmarks

arXiv AI

Jul 28

Evaluating RAG for French immigration law: a benchmark and baseline study

arXiv:2607. 24449v1 Announce Type: cross Abstract: International recruitment in France requires navigating a layered legal framework absent from existing legal AI benchmarks.

By Annia Abtout, Julien Delaunay, Monika Ewa Rakoczy

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arXiv Machine Learning

Jul 28

DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids

arXiv:2607. 22590v1 Announce Type: cross Abstract: The rapid growth of AI workloads and renewable energy resources exacerbates supply-demand imbalance in power systems, making traditional load regulation designed for efficient allocation inadequate and motivating demand response (DR) mechanisms to enable load controllability in smart grids.

By Yunhao Yao, Siyu Jing, Yang Yang, Qiang Xu, Changqi Weng, Xiang-Yang Li

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arXiv AI

Jul 28

Plain Transformers are Surprisingly Powerful Link Predictors

arXiv:2602. 01553v3 Announce Type: replace-cross Abstract: Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies.

By Quang Truong, Yu Song, Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Jiliang Tang

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arXiv AI

Jul 28

EviBack: Search-Agent Reinforcement Learning via Evidence-Constrained Teacher Backoff

arXiv:2607. 23955v1 Announce Type: new Abstract: Reinforcement learning enables Agentic RAG systems to learn multi-turn search from verifiable outcome rewards, but all- zero rollout groups provide no comparative signal and may hide useful search behavior.

By Xiao Ma, Zhiquan Hu, Yi Wei, Chenchen Zhao, Yijun Chen, Jicheng Zhao, Yuming Li Chuang Dai

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jul 28

CSV-Decode: Certifiable Sub-Vocabulary Decoding for Efficient Large Language Model Inference

arXiv:2511. 21702v2 Announce Type: replace-cross Abstract: Large language models face significant computational bottlenecks during inference due to the expensive output layer computation over large vocabularies.

By Dong Liu, Shu Wang, Yanxuan Yu, Haisheng Wang, Ben Lengerich

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arXiv AI

Jul 28

Lexical discovery in unknown environments orchestrated by Large Language Models

arXiv:2607. 22591v1 Announce Type: new Abstract: Populations of autonomous agents deployed in unknown environments (e.

By Rafael Sendra-Arranz, I\~naki Dellibarda Varela, Eduardo Rocon, \'Alvaro Guti\'errez, Manuel Cebrian

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arXiv AI

Jul 28

Escaping the Euclidean Void: Manifold-Informed Flow Matching for Sequential Recommendation

arXiv:2607. 23762v1 Announce Type: cross Abstract: Conventional recommenders capture users' preferences by optimizing observed user-item relations, whereas continuous generative recommendation additionally learns the trajectory of synthesizing a target item.

By Dengzhao Fang, Jingtong Gao, Yu Li, Xiangyu Zhao, Yi Chang

rag diffusion benchmarks

arXiv Machine Learning

Jul 28

Self-Attention Dynamics with Rotary Position Embeddings: Twisted States and Explicit Consensus Rates on the Sphere

arXiv:2607. 24502v1 Announce Type: cross Abstract: Rotary position embeddings (RoPE) modify attention scores through position-dependent rotations, but their effect on normalized token dynamics is not captured by the vanilla spherical self-attention model.

By Hao Ye (Xi'an Institute of Optics,Precision Mechanics, Chinese Academy of Sciences, University of Chinese Academy of Sciences)

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arXiv AI

Jul 28

Reason Before You Retrieve: Agentic Planning for Multi-modal RAG

arXiv:2607. 22643v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal input over a flat evidence space.

By Tianyu Yang, Shir Simon, Zhenzhen Li, Minhao Cheng, Xiangliang Zhang

rag agents fine-tuning multimodal

arXiv Machine Learning

Jul 28

Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement

arXiv:2607. 22770v1 Announce Type: new Abstract: Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex overlapping symptoms among diseases.

By Siyuan Du, Mengxi Chen, Xinyang Jiang, Zilong Wang, Jiangchao Yao, Dongsheng Li, Ya Zhang, Lili Qiu, Yanfeng Wang

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arXiv AI

Jul 28

TriShieldRAG: A Three-Ring Defense-in-Depth Framework Against Knowledge Corruption in Retrieval-Augmented Generation

arXiv:2607. 23838v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) lets a large language model answer questions using documents retrieved from an external knowledge base at query time.

By Susil Kumar Mohanty, Rohit Patel, Kosuru Yuvaraj, Jeenal Chaudhary, Disha Singhania

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arXiv AI

Jul 28

An empirical investigation into the properties of standard word embeddings

arXiv:2607. 23675v1 Announce Type: cross Abstract: The embedding of word sequences into continuous vector spaces has been one of the most important developments in Natural Language Processing in the recent past.

By Salomon Kabongo

rag nlp multimodal

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