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 28

EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation

arXiv:2607. 22655v1 Announce Type: new Abstract: Estimating origin-destination (OD) flows under disruptive events is important for disaster response and urban resilience.

By Jie Zhao, Jie Feng, Can Rong, Zhihan Hou, Peng Lu, Yong Li

llms rag diffusion

arXiv AI

Jul 28

LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers

arXiv:2607. 22804v1 Announce Type: cross Abstract: Accurate geological characterization of subsurface reservoirs from well log data is essential to support projects such as carbon capture and storage (CCS), geothermal development, and extraction of natural resources.

By Shwetha Salimath, Francesca Bugiotti, Sylvain Wlodarczyk, Sohaib Ouzineb

llms rag

arXiv Machine Learning

Jul 28

Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory

arXiv:2607. 24237v1 Announce Type: new Abstract: Many existing clustering methods are designed based on a set-oriented definition---a cluster is a set of similar points---relying a point-to-point similarity function to find similar points.

By Kai Ming Ting, Kaifeng Zhang, Sanjay Chawla

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arXiv AI

Jul 28

Building AI That Works: ESnet's Pragmatic Approach to AI-Driven Operational Excellence

arXiv:2607. 22948v1 Announce Type: cross Abstract: The ORBIT (Operations Responses and Business Intelligence Toolkit) project was initiated to assess agentic AI for the upcoming ESnet 7 initiative and to address persistent operational pain points in the Network Operations Center (NOC) workflow.

By Bin Dong, Sukhada Gholba, Brooklin Gore, Shawn Kwang, David Mitchell, Samuel Oehlert, Garrett Stewart, Brendan White, Luke Baker, Ed Balas, Britt Gathright, Chin Guok, Jon-Paul Heron, John MacAuley, Scott Richmond, Chris Robb, Chris Tracy, Kesheng Wu

rag agents

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 AI

Jul 28

ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding

arXiv:2607. 24743v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge: models must absorb knowledge from heterogeneous 2D and 3D medical images, and evaluation protocols must align with radiologists' clinical practice and provide an accurate, fine-grained and factualness-driven assessment.

By Hangjie Yuan, Yichen Qian, Zhiwei Tang, Xianzhe Xu, Lirong Wu, Sicheng Yang, Jinwang Wang, Pengju Wang, Zhitao Zeng, Yizeng Han, Yan Xing, Shengxuan Luo, Tao Feng, Qing Xie, Weigen Yao, Yi Yang, Zuozhu Liu, Jiasheng Tang, Shaocheng Wang, Jitao Wang, Jiahong Dong, Weihua Chen, Feng Xu, Fan Wang

llms rag agents nlp multimodal benchmarks

arXiv AI

Jul 28

The Lattice Representation Hypothesis of Large Language Models

arXiv:2603. 01227v3 Announce Type: replace Abstract: We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical operations in embedding geometry.

By Bo Xiong

llms rag

arXiv AI

Jul 28

Retrieval-Augmented Generation of Ontologies from Relational Databases

arXiv:2506. 01232v2 Announce Type: replace-cross Abstract: Deriving OWL ontologies from relational database schemas supports semantic interoperability and downstream tasks such as knowledge graph population, ontology-based data access, graph-based learning, and automated reasoning.

By Nadeen Fathallah, Mojtaba Nayyeri, Athish A Yogi, Ratan Bahadur Thapa, Hans-Michael Tautenhahn, Anton Schnurpel, Steffen Staab

llms rag

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

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 Machine Learning

Jul 28

Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models

arXiv:2607. 23052v1 Announce Type: cross Abstract: Dual-encoder vision-language models (VLMs) expose a similarity interface that enables zero-shot retrieval but fails compositional constraints: queries like "umbrella and no person" retrieve images containing both, even when concept detection is reliable.

By Sultan Alshehri, Zhantao Yang, Han Zhang, Marios Savvides

llms rag fine-tuning multimodal

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 AI

Jul 28

OpenAIs HealthBench in Action: Evaluating an LLM-Based Medical Assistant on Realistic Clinical Queries

arXiv:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.

By Sandhanakrishnan Ravichandran, Shivesh Kumar, Rogerio Corga Da Silva, Miguel Romano, Reinhard Berkels, Michiel van der Heijden, Olivier Fail, Valentine Emmanuel Gnanapragasam

llms rag agents benchmarks

arXiv Machine Learning

Jul 28

On a linear fused Gromov-Wasserstein distance for graph structured data

arXiv:2203. 04711v2 Announce Type: replace Abstract: We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem.

By Dai Hai Nguyen, Koji Tsuda

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arXiv AI

Jul 28

Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Model

arXiv:2607. 22083v2 Announce Type: replace Abstract: We present Nanbeige4.

By Nanbeige Lab, :, Chen Yang, Chengrui Huang, Fufeng Lan, Hanhui Chen, Hao Zhou, Huatong Song, Jiaqi Cao, Jiaying Zhu, Jinlin Niu, Kai Wang, Lisheng Huang, Qiliang Liang, Ran Le, Ruixiang Feng, Shuang Sun, Tao Gu, Tao Zhang, Tianyu Luo, Yang Song, Yun Xing, Yuntao Wen, Ziyao Xu, Zongchao Chen, Zongqiang Li

llms rag agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 28

RMS@CC-MMD 2026: Multimodal Misogyny Detection via Geometric Interaction and Multi-View Consensus

arXiv:2607. 22709v1 Announce Type: cross Abstract: The proliferation of internet memes has introduced new complexities to automated content moderation, particularly in detecting misogyny.

By Md. Ajwad Hossain

rag computer-vision multimodal safety

arXiv AI

Jul 28

Controlling Embedding Spaces with Text-Conditioned Transformations

arXiv:2607. 22919v1 Announce Type: cross Abstract: Multimodal embedding spaces in models like CLIP enable powerful capabilities such as semantic similarity retrieval and cross-modal zero-shot classification.

By Joseph Fioresi, Fabian Caba Heilbron, Pankaj Nathani, Mubarak Shah, Kushal Kafle

rag diffusion multimodal benchmarks

arXiv Machine Learning

Jul 28

Risk reversal for least squares estimators under nested convex constraints

arXiv:2601. 16041v2 Announce Type: replace-cross Abstract: In constrained stochastic optimization, one expects that restricting the feasible set, provided it still contains the true parameter, should not increase the statistical risk of the corresponding projection estimator.

By Omar Al-Ghattas

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arXiv AI

Jul 28

Spectral Dynamics of Semantic Drift in Clinical Multi-Agent Language Model Networks

arXiv:2607. 22758v1 Announce Type: cross Abstract: The integration of iterative LLMs within multi-agent diagnostic frameworks requires a rigorous quantitative reevaluation of underlying communication topologies.

By Amritesh Banerjee

llms rag agents diffusion safety

arXiv AI

Jul 28

Hierarchical Grading in Large Language Models

arXiv:2607. 22757v1 Announce Type: cross Abstract: We introduce Graded Large Language Models (GLLMs), an algebraic framework that equips the representation space of a transformer with a grading and propagates the induced weighted scalar action through embeddings, self-attention, and the training objective.

By T. Shaska

llms rag

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