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 13

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.

By Charles E. Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung

llms rag agents benchmarks safety

arXiv AI

Jul 13

Improving Language Agents through BREW: Bootstrapping expeRientially-learned Environmental knoWledge

arXiv:2511. 20297v2 Announce Type: replace Abstract: Large Language Model (LLM)-based agents are increasingly capable of complex, multi-step tasks such as GUI automation, tool use, and data manipulation, yet they cannot learn from experience: each new session rediscovers solutions from scratch.

By Shashank Kirtania, Param Biyani, Priyanshu Gupta, Yasharth Bajpai, Roshni Iyer, Sumit Gulwani, Gustavo Soares

llms rag agents robotics benchmarks

arXiv Machine Learning

Jul 13

Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking

arXiv:2602. 21196v2 Announce Type: replace Abstract: Efficiently processing long sequences with Transformer models usually requires splitting the computations across accelerators via context parallelism.

By Ravi Ghadia, Maksim Abraham, Sergei Vorobyov, Max Ryabinin

llms rag efficiency

arXiv AI

Jul 13

Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs

arXiv:2607. 09121v1 Announce Type: cross Abstract: In this study, we examine the opportunities brought by Large Language Models (LLMs) to various aspects of fundamental analysis of companies based on their reports as well as data and documents describing macroeconomic situation like GDP and inflation changes as well as documents filled to the U.

By Bartosz Zi\'o{\l}ko, Kacper Dobrzeniewski

llms rag

arXiv AI

Jul 13

H3Former: Hypergraph-based Semantic-Aware Aggregation via Hyperbolic Hierarchical Contrastive Loss for Fine-Grained Visual Classification

arXiv:2511. 10260v2 Announce Type: replace-cross Abstract: Fine-Grained Visual Classification (FGVC) remains a challenging task due to subtle inter-class differences and large intra-class variations.

By Yongji Zhang, Siqi Li, Kuiyang Huang, Yue Gao, Yu Jiang

rag benchmarks

arXiv AI

Jul 13

PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation

arXiv:2607. 09094v1 Announce Type: cross Abstract: Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research.

By Devanshu Verma, Vasudha Bhatnagar, Vikas Kumar, Balaji Ganesan

rag computer-vision benchmarks

arXiv AI

Jul 13

Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation

arXiv:2607. 09349v1 Announce Type: cross Abstract: Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents.

By Cedric Caruzzo, Donggeun Yoo, Tae Soo Kim

rag fine-tuning benchmarks safety

arXiv AI

Jul 13

PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal Synthesis

arXiv:2607. 09662v1 Announce Type: cross Abstract: Current electroencephalography (EEG)-based dream detection relies on power spectral density (PSD) and statistical moment features, achieving a state-of-the-art area under the receiver operating characteristic curve (AUC) of approximately 0.

By Ren Takahashi, Emre Yusuf, Jayabrata Bhaduri

rag diffusion benchmarks

arXiv AI

Jul 13

Generative Communications: Overview, Technologies, and Trends

arXiv:2607. 09183v1 Announce Type: cross Abstract: The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication paradigms.

By Wenjun Zhang, Zhiyong Chen, Tong Wu, Guo Lu, Li Song, Feng Yang, Meixia Tao

rag

arXiv Machine Learning

Jul 13

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks

arXiv:2507. 00600v2 Announce Type: replace-cross Abstract: Understanding the functional roles of financial institutions within interconnected markets is critical for effective supervision, systemic risk assessment, and resolution planning.

By Christian Franssen, Thao Le, Iman van Lelyveld, Bernd Heidergott

rag

arXiv AI

Jul 13

SLIDERS: Systematic Reviews via Automated Evidence Synthesis and Reconciliation

arXiv:2604. 22294v2 Announce Type: replace-cross Abstract: Systematic reviews -- which requires comprehensive evidence collection and synthesis from large document corpora in response to targeted research questions -- are foundational in finance, social sciences, and other technical fields.

By Harshit Joshi, Priyank Shethia, Jadelynn Dao, Monica S. Lam

llms rag agents benchmarks

arXiv Machine Learning

Jul 13

Multimodal Scenario Similarity Search for Autonomous Driving

arXiv:2607. 09428v1 Announce Type: cross Abstract: Large-scale autonomous-driving datasets contain vast numbers of recorded scenarios, creating a need for efficient retrieval methods that can identify situations similar to a given query.

By Tam\'as Matuszka, Andr\'as Tam\'asy, Bal\'azs Szol\'ar

llms rag agents robotics multimodal

arXiv Machine Learning

Jul 13

Group Invariant Spectral Embedding

arXiv:2607. 08987v1 Announce Type: new Abstract: Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional structures.

By Yeari Vigder, Paulina Hoyos, David Thong, Joakim and\'en, Joe Kileel, Amit Moscovich

rag

arXiv AI

Jul 13

Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification

arXiv:2607. 09443v1 Announce Type: cross Abstract: Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts.

By Anil Osman Tur, Tonje Knutsen Sordalen, Kim Tallaksen Halvorsen, Cigdem Beyan

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jul 13

Terminal Dimension Reduction for Time Series with Applications

arXiv:2607. 09490v1 Announce Type: cross Abstract: Terminal embeddings have emerged as a powerful tool for dimension reduction.

By Alexander Munteanu, Matteo Russo, David Saulpic, Chris Schwiegelshohn

rag

arXiv Machine Learning

Jul 13

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers

arXiv:2607. 09480v1 Announce Type: cross Abstract: The human visual system (HVS) employs foveated sampling and eye movements to achieve efficient perception, conserving both metabolic energy and computational resources.

By Ibrahim Batuhan Akkaya, Kishaan Jeeveswaran, Bahram Zonooz, Elahe Arani

llms rag computer-vision safety

arXiv AI

Jul 13

Conceptual Networks for Cross-Linguistic Idiomatic Expressions:A Feature-Based Graph Approach

arXiv:2607. 09576v1 Announce Type: cross Abstract: We present an interpretable network-based framework for representing idiomatic and figurative meaning across eight typologically diverse languages, totaling 160 conventional expressions, the large majority of which are idiomatic.

By Kiran Pala, Punam Silu, Lixun Yu

llms rag benchmarks

arXiv Machine Learning

Jul 13

Cluster and then Embed: A Modular Approach for Visualization

arXiv:2509. 03373v2 Announce Type: replace Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure.

By Elizabeth Coda, Ery Arias-Castro, Gal Mishne

rag

arXiv Machine Learning

Jul 13

Contrastive Learning on Multimodal Analysis of Electronic Health Records

arXiv:2403. 14926v3 Announce Type: replace-cross Abstract: Electronic health record (EHR) systems capture a wealth of multimodal clinical data, encompassing both structured clinical codes and unstructured clinical notes.

By Tianxi Cai, Feiqing Huang, Ryumei Nakada, Linjun Zhang, Doudou Zhou

rag multimodal safety

arXiv AI

Jul 13

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs

arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).

By Skatje Myers, Dmitriy Dligach, Timothy A. Miller, Samantha Barr, James Landefeld, Yanjun Gao, Matthew Churpek, Anoop Mayampurath, Majid Afshar

llms rag

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