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

Aug 11

Distilling CT Foundation Models into Editable Concept Bottlenecks for Lung Nodule Malignancy Prediction

arXiv:2608. 07857v1 Announce Type: cross Abstract: Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret.

By Fakrul Islam Tushar, Stephen Adamo, Geoffrey D. Rubin

rag

arXiv AI

Aug 11

Private Anytime Selective-Risk Certification for Federated Retrieval-Augmented Generation: Guarantees and Empirical Limits

arXiv:2608. 07913v1 Announce Type: cross Abstract: Selective-risk certificates promise that accepted outputs meet a declared error target.

By Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma

rag nlp safety

arXiv AI

Aug 11

UniSpace: Unified Visual Representation and Scalable Multimodal Modeling

arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.

By Jinbo Yan, Limeng Qiao, Jie Qin, Junyan He, Feize Wu, Guanglu Wan

llms rag diffusion multimodal

arXiv AI

Aug 11

Length-MAX Tokenizer for Language Models

arXiv:2511. 20849v2 Announce Type: replace-cross Abstract: We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference.

By Dong Dong, Weijie Su

llms rag nlp efficiency

arXiv AI

Aug 11

AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups

arXiv:2608. 08883v1 Announce Type: new Abstract: Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows.

By Jack Stark, Srinath Saikrishnan, Vikram Seenivasan, Bernie Boscoe, Andrew Lizarraga, Tuan Do

llms rag multimodal safety

arXiv Machine Learning

Aug 11

Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery

arXiv:2608. 08704v1 Announce Type: cross Abstract: Kernel spectral clustering with a single bandwidth can be inadequate for data exhibiting multiple characteristic pairwise-distance scales, a problem particularly prevalent in the high-dimensional regime.

By Zeqin Lin, Guangming Pan, Zhixiang Zhang, Yinbing Zhou

rag

arXiv AI

Aug 11

DRBENCHER: Can Your Agent Identify the Entity, Retrieve Its Properties and Do the Math?

arXiv:2604. 09251v3 Announce Type: replace Abstract: Deep research agents increasingly interleave web browsing with multi-step computation, yet existing benchmarks evaluate these capabilities in isolation, creating a blind spot in assessing real-world performance.

By Young-Suk Lee, Ramon Fernandez Astudillo, Radu Florian

rag agents benchmarks

arXiv Machine Learning

Aug 11

PreGress: Ranking-Native Pre-training and Prompting for Graph Node Ranking

arXiv:2608. 09016v1 Announce Type: cross Abstract: Node ranking is a fundamental problem in graph information retrieval, measuring the relative importance of nodes and supporting a wide range of applications such as influence analysis, recommendation, and graph-based retrieval augmented generation.

By Lujie Ban, Jiasheng shi, Yingli Zhou, Kaiwen Xue, Daiyin Wang, Xubin Li, Shuanghua Li, Chenhao Ma

llms rag benchmarks

arXiv AI

Aug 11

HugSelect: An Explainable Multi-Criteria Decision-Support Framework for foundation-model selection

arXiv:2608. 08069v1 Announce Type: cross Abstract: Foundation models are increasingly reused as software components, making model selection a critical software-engineering decision.

By Alireza Joonbakhsh (Shiraz University), Arda Canser Adal{\i} (Utrecht University), Slinger Jansen (Utrecht University), Farshad Khunjush (Shiraz University), Siamak Farshidi (Wageningen University,Research)

llms rag benchmarks

arXiv Machine Learning

Aug 11

Input convex neural networks as surrogates in mathematical optimisation

arXiv:2608. 09707v1 Announce Type: cross Abstract: Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research.

By Yu Liu, Jan Kronqvist, Fabricio Oliveira

rag

arXiv AI

Aug 11

Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging

arXiv:2608. 08926v1 Announce Type: new Abstract: Neuroimaging and genetic testing are two important clinical references for nervous system diseases, offering complementary diagnostic information.

By Tianli Tao, Ziyang Wang, Emma Robinson, Rachel Sparks, Le Zhang

llms rag multimodal

arXiv AI

Aug 11

RAG-Based Auto-Configuration for Industrial Fieldbus Devices

arXiv:2608. 08618v1 Announce Type: cross Abstract: Industrial device commissioning requires engineers to manually extract hundreds of protocol-specific parameters from heterogeneous PDF manuals and transcribe them into supervisory control systems, a time-intensive, error-prone workflow.

By Aadil Gani Ganie, Saad Ezzini, Naveed Farooz Marazi

llms rag robotics benchmarks

arXiv Machine Learning

Aug 11

BASIS: Breach-Aware Selective Prompt Injection Shielding with Prefill Attention Probes

arXiv:2608. 08027v1 Announce Type: cross Abstract: Prompt injection is a critical security threat in large language model (LLM) applications, where attackers hijack model behavior by embedding malicious instructions in user or external data.

By Laiqiao Qin, Tianqing Zhu, Longxiang Gao, Wanlei Zhou

llms rag

arXiv AI

Aug 11

REVEAL: A Rubric-Guided Agent for Explicit Evidence Sufficiency Verificationin Long-Video Question Answering

arXiv:2608. 08612v1 Announce Type: cross Abstract: Recently, retrieval-augmented and memory-augmented methods have emerged as two promising paradigms for long-video question answering.

By Caijun Yan, Yang Zhou, Meixing Shi, Haoran Sun, Yichen Li, Yuxiang Cai, Yankai Jiang

rag agents nlp multimodal benchmarks

arXiv AI

Aug 11

Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning

arXiv:2608. 09507v1 Announce Type: cross Abstract: Natural language user preferences provide an interpretable interface for LLM personalization.

By Yuting Liu, Wei Wu, Jianzhe Zhao, Guibing Guo

llms rag agents reinforcement-learning

arXiv AI

Aug 11

ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs

arXiv:2607. 28126v2 Announce Type: replace Abstract: Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles.

By Bingchen Liu, Yuanyuan Fang, Lei Liu, Guangyuan Dong, Xing Fu, Yuanyuan Gao, Shuyue Wei, Xin Li, Xiangtian Meng

llms rag computer-vision benchmarks

arXiv Machine Learning

Aug 11

LLM-Based Embeddings for Program Analysis and Optimization

arXiv:2608. 07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs.

By Calvin Higgins, Marco Alvarez

llms rag benchmarks

arXiv AI

Aug 11

Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States

arXiv:2608. 08024v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and useful responses but remain prone to hallucinations.

By Zakhar Mrykhin, Valentin Malykh

llms rag safety

arXiv AI

Aug 11

Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

arXiv:2603. 26798v2 Announce Type: replace-cross Abstract: Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this space is rarely inspected.

By Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein, Edgar Heinert, Annika M\"utze, Marvin Keller, Sparsh Tiwari, Georgii Mikriukov, Diedrich Wolter, Jae Hee Lee, Matthias Rottmann

llms rag multimodal safety

arXiv AI

Aug 11

To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval

arXiv:2604. 00715v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) improves language model (LM) performance by providing relevant context at test time for knowledge-intensive situations.

By Karan Singh, Michael Yu, Varun Gangal, Zhuofu Tao, Sachin Kumar, Emmy Liu, Steven Y. Feng

llms rag benchmarks