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 8

Modality Relevance is not Modality Utility: Post-hoc Selective Modality Escalation for Cost-Aware Multimodal RAG

arXiv:2607. 05438v1 Announce Type: cross Abstract: Multimodal retrieval-augmented generation (RAG) grounds a generator in evidence drawn from heterogeneous modalities -- text, tables, and images.

By Xue Li, Yiming Gai

llms rag multimodal

arXiv Machine Learning

Jul 8

Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES

arXiv:2607. 05691v1 Announce Type: cross Abstract: Every chemical language model reading SMILES begins with a tokenizer, yet the field has inherited byte-pair encoding (BPE) from natural language with little scrutiny.

By Hunter Heidenreich

llms rag computer-vision nlp

arXiv AI

Jul 8

PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates

arXiv:2607. 05483v1 Announce Type: cross Abstract: Agentic workflows often operate over shared, structured state.

By Zhaoyu Bai, Jiaqi Cai

llms rag agents benchmarks

arXiv AI

Jul 8

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology

arXiv:2605. 18419v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology.

By Franciskus Xaverius Erick, Johanna Paula M\"uller, Bernhard Kainz

llms rag fine-tuning efficiency multimodal safety

arXiv Machine Learning

Jul 8

Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

arXiv:2607. 05635v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities.

By Vrushank Ahire, Yogesh Kumar, M. A. Ganaie

rag benchmarks

arXiv AI

Jul 8

Provable learning separation for predicting time-evolution of quantum many-body systems

arXiv:2607. 06472v1 Announce Type: cross Abstract: Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantum machine learning (QML) tasks that exhibit learning separations?

By Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert, Vedran Dunjko, Sofiene Jerbi

rag

arXiv AI

Jul 8

Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?

arXiv:2508. 01109v3 Announce Type: replace Abstract: We investigate whether socioeconomic indicators, like household wealth, leave recoverable informational imprints in both satellite imagery (capturing features like buildings and roads) and Internet-sourced text (reflecting historical, cultural, and narratives of neighborhoods).

By Satiyabooshan Murugaboopathy, Connor T. Jerzak, Adel Daoud

llms rag agents multimodal safety

arXiv AI

Jul 8

Learning The Minimum Action Distance

arXiv:2506. 09276v4 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent.

By Lorenzo Steccanella, Joshua B. Evans, \"Ozg\"ur \c{S}im\c{s}ek, Anders Jonsson

rag agents reinforcement-learning

arXiv Machine Learning

Jul 8

Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles

arXiv:2607. 06484v1 Announce Type: cross Abstract: Poisoning attacks against public datasets lead to major concerns, such as (i) misclassification of perceived objects when the poisoned data is used for training and (ii) embedding of backdoors that may eventually be triggered later on, when specific conditions in the system apply over the learned models.

By Marwan Lazrag, Badis Hammi, Lorena Gonzalez-Manzano, Joaquin Garcia-Alfaro

rag agents diffusion safety

arXiv Machine Learning

Jul 8

Multimodal Analytics of Cybersecurity Crisis Preparation Exercises: What Predicts Success?

arXiv:2603. 28553v2 Announce Type: replace-cross Abstract: Instructional alignment, the match between intended cognition and enacted activity, is central to effective instruction but hard to operationalize at scale.

By Conrad Borchers, Valdemar \v{S}v\'abensk\'y, Sandesh K. Kafle, Kevin K. Tang, Jan Vykopal

rag multimodal safety

arXiv Machine Learning

Jul 8

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings

arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.

By Qiwei Han, Chi Zhou, Ruobing Wang, Zheng Ma

rag diffusion efficiency multimodal benchmarks safety

arXiv AI

Jul 8

Universal Algorithm-Implicit Learning

arXiv:2602. 14761v2 Announce Type: replace-cross Abstract: Current meta-learning methods are constrained to narrow task distributions with fixed feature and label spaces, limiting applicability.

By Stefano Woerner, Seong Joon Oh, Christian F. Baumgartner

llms rag nlp multimodal benchmarks

arXiv Machine Learning

Jul 8

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks

arXiv:2607. 05489v1 Announce Type: cross Abstract: The AInstein architecture introduced an unsupervised neural method for solving the Riemannian Einstein equations on arbitrary manifolds.

By Tancredi Schettini Gherardini, Edward Hirst, Alexander George Stapleton

rag

arXiv AI

Jul 8

Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs

arXiv:2607. 06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English.

By Andrea Alfarano, Andrea Bacciu, Saab Mansour, Amin Mantrach, Marcello Federico

llms rag

arXiv Machine Learning

Jul 8

Determinantal point process sampling for bioacoustic active learning

arXiv:2607. 06063v1 Announce Type: cross Abstract: Eco-acoustic monitoring generates vast volumes of audio data, making active learning a promising approach for reducing annotation effort while efficiently training reliable biodiversity classifiers.

By Hugo Magaldi, Gabriel Dubus

rag

arXiv Machine Learning

Jul 8

Channel-wise Retrieval for Multivariate Time Series Forecasting

arXiv:2604. 05543v2 Announce Type: replace Abstract: Multivariate time series forecasting often struggles to capture long-range dependencies due to fixed lookback windows.

By Junhyeok Kang, Jun Seo, Soyeon Park, Sangjun Han, Seohui Bae, Hyeokjun Choe, Soonyoung Lee

rag benchmarks

arXiv AI

Jul 8

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

arXiv:2512. 23236v4 Announce Type: replace-cross Abstract: Making deep learning recommendation model (DLRM) training and inference fast and efficient is important.

By Gang Liao, Hongsen Qin, Ying Wang, Alicia Golden, Michael Kuchnik, Yavuz Yetim, Jia Jiunn Ang, Chunli Fu, Yihan He, Samuel Hsia, Zewei Jiang, Dianshi Li, Uladzimir Pashkevich, Varna Puvvada, Feng Shi, Matt Steiner, Ruichao Xiao, Liyuan Li, Nathan Yan, Xiayu Yu, Zhou Fang, Roman Levenstein, Kunming Ho, Haishan Zhu, Alec Hammond, Richard Li, Ajit Mathews, Kaustubh Gondkar, Abdul Zainul-Abedin, Ketan Singh, Hongtao Yu, Wenyuan Chi, Barney Huang, Sean Zhang, Noah Weller, Zach Marine, Wyatt Cook, Carole-Jean Wu, Gaoxiang Liu

rag agents

arXiv Machine Learning

Jul 8

Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding

arXiv:2607. 05936v1 Announce Type: cross Abstract: Integration of web APIs is a cornerstone of modern software systems, yet writing correct web API invocation code remains challenging due to complex and evolving API specifications.

By Daniel Maninger, Leon Chemnitz, Jannis Brugger, Tushar Lamba, Amir Molzam Sharifloo, Mira Mezini

llms rag safety

arXiv AI

Jul 8

A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems

arXiv:2607. 06479v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physical laws directly into the learning process.

By Sonal Ankush Chibire, Jenn-Terng Gau, Bo Zhang

rag

Hugging Face Trending Papers

Jul 8

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning

Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in isolation. When problems arrive sequentially, accumulating reusable experience across them can further improve performance.

llms rag

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