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 17

Governing Generative AI Across Financial Institutions: A Framework for Generative AI Risk Control

arXiv:2607. 04103v3 Announce Type: replace-cross Abstract: Generative artificial intelligence is moving from general-purpose experimentation toward specialized applications across banking, capital markets, insurance, payments, and wealth management.

By Dennis Mao, Alessandra Lin, Yixin Kang, Yiqing Wang

rag agents multimodal

arXiv AI

Jul 17

SmartRAG: Native Graph-Based RAG for Mobile Device

arXiv:2607. 14661v1 Announce Type: new Abstract: Deploying large language models (LLMs) as personal assistants on mobile devices demands privacy, low latency, and offline availability, yet the computational cost of giant models clashes with strict edge-hardware budgets.

By Zhihan Jiang, Meng Li, Shenghao Liu, Keran Li, Ruiben Zhou, Xianjun Deng, Shuai Wang, Haipeng Dai

llms rag efficiency benchmarks safety

arXiv Machine Learning

Jul 17

Contrastive Conformal Sets

arXiv:2603. 26261v2 Announce Type: replace Abstract: Contrastive learning produces coherent semantic feature embeddings by encouraging positive samples to cluster closely while separating negative samples.

By Yahya Alkhatib, Wee Peng Tay

rag

arXiv AI

Jul 17

In-Place Tokenizer Expansion for Pre-trained LLMs

arXiv:2607. 15232v1 Announce Type: cross Abstract: A tokenizer fixed at the start of pre-training allocates vocabulary in proportion to the pre-training corpus, reflecting the deployment priorities at that time.

By Jimmy T. H. Smith, Tarek Dakhran, Alberto Cabrera, Simon S. Lee, Paul Pak, Aditya Tadimeti, Tim Seyde, Maxime Labonne, Alexander Amini, Mathias Lechner

llms rag nlp

arXiv Machine Learning

Jul 17

AlphaWiSE: Adaptive Weight Interpolation for Continual Multimodal Representation Learning

arXiv:2607. 15094v1 Announce Type: cross Abstract: Multimodal models such as CLIP learn a shared embedding space for cross-modal retrieval, but continual adaptation to sequentially arriving data can disrupt the cross-modal alignment acquired from earlier phases.

By Sarthak Jain, Qiran Hu, Zhen Zhu, Yaoyao Liu

rag multimodal safety

arXiv Machine Learning

Jul 17

NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosis

arXiv:2607. 14314v1 Announce Type: new Abstract: Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling.

By Lincan Li, Zheng Chen, Yushun Dong

llms rag safety

arXiv Machine Learning

Jul 17

cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data

arXiv:2607. 15018v1 Announce Type: cross Abstract: High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables.

By Chun-houh Chen, Shun-Chuan Chang, Chiun-How Kao, Yi-Ju Lee, Shang-Ying Shiu, Yin-Jing Tien, ShengLi Tzeng, Han-Ming Wu

rag safety

arXiv AI

Jul 17

Semantic Audio-driven Understanding for Dynamic Humanoid Whole Body Control

arXiv:2607. 14182v1 Announce Type: cross Abstract: Recent advances in humanoid robotics and reinforcement learning have enabled the acquisition of highly expressive whole-body motion policies.

By J. M. A. Marcelo, M. Brienza, E. Bugli, L. Comito, D. Nardi, D. D. Bloisi, V. Suriani

rag agents reinforcement-learning robotics multimodal safety

arXiv AI

Jul 17

Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving

arXiv:2607. 14387v1 Announce Type: new Abstract: Validating autonomous driving systems requires diverse, regulation-compliant test scenarios.

By Yuan Gao, Wenting Miao, Mattia Piccinini, Haoyu Wang, Qunying Song, Johannes Betz

llms rag agents robotics benchmarks

arXiv Machine Learning

Jul 17

Mutable Low-Rank Sketches for Retrain-Free Recommendation

arXiv:2607. 15242v1 Announce Type: new Abstract: A common bottleneck in two-stage recommendation is embedding staleness: when a user rates a new item, their embedding remains fixed until the next retrain cycle.

By Hector J. Garcia, Nick Clayton

rag

arXiv AI

Jul 17

Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models

arXiv:2607. 14315v1 Announce Type: cross Abstract: In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score.

By Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis, Dimitrios Kotios, Vasileios Koukos, Dimosthenis Kyriazis, Jonh Soldatos

rag benchmarks

arXiv AI

Jul 17

T5-CSBoost: Adversarial Perturbation Resistant LLM Fingerprinting

arXiv:2607. 14113v1 Announce Type: cross Abstract: While many AI-generated text (AIGT) detectors achieve strong performance on clean inputs, their accuracy degrades significantly under light paraphrasing, word substitutions, character edits, and distribution shifts.

By Gayan K. Kulatilleke, Mahsa Baktashmotlagh, Siamak Layeghy, Marius Portmann

llms rag benchmarks safety

arXiv AI

Jul 17

ReportMedSAM: Guiding Segmentation Through Radiology Reports

arXiv:2607. 14116v1 Announce Type: cross Abstract: Free-form radiology reports contain rich clinical descriptions, yet converting them for reliable segmentation remains challenging due to the inherent variability of natural language.

By Anghong Du, Theodoros N. Arvanitis, Colin Watts, Alejandro F. Frangi, Le Zhang

rag computer-vision multimodal

arXiv AI

Jul 17

SceneBind: Binding What and Where Across Vision, Audio and Language

arXiv:2607. 15265v1 Announce Type: cross Abstract: We present SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across vision, audio and language.

By Mingfei Chen, Zijun Cui, Ruoke Zhang, Hyeonggon Ryu, Eli Shlizerman

rag multimodal benchmarks safety

arXiv AI

Jul 17

Are LLM-Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent

arXiv:2607. 14541v1 Announce Type: new Abstract: Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads.

By Lingyun Yang, Yuxiao Wang, Shenghao Liang, Linfeng Yang, Daocheng Ying, Chunbo You, Rui Zhang, Luping Wang, Yinghao Yu, Guodong Yang, Liping Zhang

llms rag agents benchmarks

arXiv AI

Jul 17

MathCoPilot: An Interactive System for Human-AI Symbiotic Paradigm of Mathematical Research

arXiv:2607. 14582v1 Announce Type: new Abstract: Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated proposition.

By Junjie Zhang, Jiayu Liu, Wenbin Liu, Zhenya Huang, Doudou Wang, Yan Jiang, Leiye Xu, Tao Xiong, Wen Huang, Qi Liu, Guoping Hu, Enhong Chen, Mengping Zhang, Xiangdong Ye

llms rag agents benchmarks

arXiv AI

Jul 17

Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents

arXiv:2607. 15095v1 Announce Type: cross Abstract: The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions.

By Dylan Van Mulders, Matthias Bogaert, Dirk Van den Poel

llms rag agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 17

SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation

arXiv:2607. 14494v1 Announce Type: new Abstract: Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form.

By Yiming Zhang, Koji Tsuda

llms rag agents nlp benchmarks

arXiv Machine Learning

Jul 17

Random Parameter Noise Does Not Make Exact ReLU Verification Easy

arXiv:2607. 14375v1 Announce Type: cross Abstract: We study exact verification of ReLU networks in an adversarial smoothed model.

By Mojtaba Soltanalian

rag safety

arXiv AI

Jul 17

BrainPilot: Automating Brain Discovery with Agentic Research

arXiv:2607. 15079v1 Announce Type: new Abstract: Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines.

By Haoxuan Li, Tianci Gao, Jianhe Li, Yang Fan, Runze Shi, Weiran Wang, Tianxiang Zhao, Zezhao Wu, Xiaoyang Jiang, Qihui Zhang, Jia Li, Xiao Xiao, Kai Du, Xiaoxuan Jia, Chao Xie, Lu Mi

rag agents benchmarks

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