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

Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness

arXiv:2607. 24162v1 Announce Type: new Abstract: Optimizing agentic workflows, such as retrieval-augmented generation (RAG) pipelines, requires navigating a combinatorial space of discrete component choices under tight evaluation budgets.

By Yang Li, Hai Liu, Dian Shao, Yu Wang, Xiyu Chen, Sergey Volkov, Bozhi Wang, Ziyu Sun, Sihang Liu, Ye Luo, Xiaowei Zhang

rag agents

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)

rag

arXiv Machine Learning

Jul 28

LanteRn: Latent Visual Structured Reasoning

arXiv:2603. 25629v2 Announce Type: replace-cross Abstract: While language reasoning models excel in many tasks, visual reasoning remains challenging for current large multimodal models (LMMs).

By Andr\'e G. Viveiros, Nuno Gon\c{c}alves, Matthias Lindemann, Andr\'e Martins

llms rag diffusion reinforcement-learning fine-tuning multimodal benchmarks

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 Machine Learning

Jul 28

TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings

arXiv:2607. 24130v1 Announce Type: cross Abstract: Tabular data is the dominant structured-data modality, and learning table representations has become a core research direction.

By Ayeen Poostforoushan, Liane Vogel, Carsten Binnig

rag benchmarks

arXiv Machine Learning

Jul 28

Context-Adaptive Inference: A Unified Statistical and Foundation-Model View

arXiv:2607. 23304v1 Announce Type: cross Abstract: Modern predictive systems are expected to adapt their behavior to the specific situation they are facing.

By Yue Yao, Caleb N. Ellington, Jingyun Jia, Baiheng Chen, Dong Liu, Rikhil Rao, Jiaqi Wang, Samuel Wales-McGrath, Yixin Yang, Zhiyuan Li, Eric P. Xing, Ben Lengerich

llms rag

arXiv Machine Learning

Jul 28

Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer

arXiv:2605. 07407v2 Announce Type: replace Abstract: We show that information can be transferred post-hoc across independently trained health foundation models (FMs), each pretrained on ~20M minutes of wearable sensor data from ~172K participants, by aligning their data-dependent coordinate systems.

By Gajendra Katuwal, Advait Koparkar, Salar Abbaspourazad, Anshuman Mishra, Sarvesh Kirthivasan

rag multimodal safety

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 Machine Learning

Jul 28

A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction

arXiv:2605. 21916v2 Announce Type: replace-cross Abstract: Dynamic link prediction is important for modeling evolving interactions in social, communication, financial, and transportation networks.

By Nouhaila Innan, M. Murali Karthick, Simeon Kandan Sonar, Vivek Chaturvedi, Muhammad Shafique

rag 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

rag benchmarks safety

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

Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments

arXiv:2607. 23503v1 Announce Type: cross Abstract: Internet of Things (IoT) applications generate vast amounts of Correlated Time Series (CTS) data that often contain missing values and require imputation.

By Zhichen Lai, Huan Li, Dalin Zhang, Dong Gong, Lina Yao, Christian S. Jensen

rag efficiency benchmarks

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

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

llms rag benchmarks

arXiv AI

Jul 28

Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models

arXiv:2607. 23976v1 Announce Type: cross Abstract: Appending a two-word confirmation tag to a decision question -- "Is X the better choice?

By Tapan Parikh

llms rag benchmarks

arXiv AI

Jul 28

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).

By Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang, Chih-Fan Hsu, Jeng-Lin Li

llms rag benchmarks

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

llms 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 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

llms rag

arXiv AI

Jul 28

KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering

arXiv:2607. 22652v1 Announce Type: new Abstract: Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream knowledge-intensive tasks, particularly knowledge graph question answering (KGQA).

By Yike Wu, Nan Hu, Guilin Qi, Guohui Xiao, Chen Jiang, Xinchun Zou, Yuchen Lu, Songlin Zhai, Yongrui Chen, Yuyang Zhang, Xiaoguang Li, Lifeng Shang, Jiaoyan Chen, Jeff Z. Pan

llms rag agents nlp safety

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