Tootfinder

Cohort Retrieval using Dense Passage Retrieval
Pranav Jadhav
https://arxiv.org/abs/2507.01049 https://arxiv.org/pdf/2507.01049…

Cohort Retrieval using Dense Passage Retrieval
Patient cohort retrieval is a pivotal task in medical research and clinical practice, enabling the identification of specific patient groups from extensive electronic health records (EHRs). In this work, we address the challenge of cohort retrieval in the echocardiography domain by applying Dense Passage Retrieval (DPR), a prominent methodology in semantic search. We propose a systematic approach to transform an echocardiographic EHR dataset of unstructured nature into a Query-Passage dataset, …

MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
Wei Chow, Yuan Gao, Linfeng Li, Xian Wang, Qi Xu, Hang Song, Lingdong Kong, Ran Zhou, Yi Zeng, Yidong Cai, Botian Jiang, Shilin Xu, Jiajun Zhang, Minghui Qiu, Xiangtai Li, Tianshu Yang, Siliang Tang, Juncheng Li
https://arxiv.org/abs/2506.03144

MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular retrieval conditions, often failing to fully exploit the expressive capacity of visual information as evidenced by maintained performance when images are replaced with captions. However, practical retrieval scenarios frequently involve interleaved multi-condition queries with multiple images. Hence, this paper introduc…

Agentic large language models improve retrieval-based radiology question answering
Sebastian Wind, Jeta Sopa, Daniel Truhn, Mahshad Lotfinia, Tri-Thien Nguyen, Keno Bressem, Lisa Adams, Mirabela Rusu, Harald K\"ostler, Gerhard Wellein, Andreas Maier, Soroosh Tayebi Arasteh
https://arxiv.org/abs/2508.00743

Agentic large language models improve retrieval-based radiology question answering
Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-augmented generation (RAG) systems for radiology question answering (QA) typically rely on single-step retrieval, limiting their ability to handle complex clinical reasoning tasks. Here we propose an agentic RAG framework enabling LLMs to autonomously decompose radiology questions, iteratively retrieve targeted clinical …

For many of the days of my career, the most important task I’ve had was to say “no”.
It’s really more like “No, not that way, but let’s find a solution.”
Saying “yes, here’s some code to do the thing you asked, even though you should DEFINITELY not be doing that thing because it’s dangerous for you and your users” is how someone (or some machine) who is not [yet] good at this job acts.

Data Retrieval with Importance Weights for Few-Shot Imitation Learning
Amber Xie, Rahul Chand, Dorsa Sadigh, Joey Hejna
https://arxiv.org/abs/2509.01657 https://

Data Retrieval with Importance Weights for Few-Shot Imitation Learning
While large-scale robot datasets have propelled recent progress in imitation learning, learning from smaller task specific datasets remains critical for deployment in new environments and unseen tasks. One such approach to few-shot imitation learning is retrieval-based imitation learning, which extracts relevant samples from large, widely available prior datasets to augment a limited demonstration dataset. To determine the relevant data from prior datasets, retrieval-based approaches most commo…

SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval
Wenchao Gu, Zongyi Lyu, Yanlin Wang, Hongyu Zhang, Cuiyun Gao, Michael R. Lyu
https://arxiv.org/abs/2508.00546

SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval
Code retrieval aims to provide users with desired code snippets based on users' natural language queries. With the development of deep learning technologies, adopting pre-trained models for this task has become mainstream. Considering the retrieval efficiency, most of the previous approaches adopt a dual-encoder for this task, which encodes the description and code snippet into representation vectors, respectively. However, the model structure of the dual-encoder tends to limit the model's perf…

Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries
Shubham Kumar Nigam, Tanmay Dubey, Noel Shallum, Arnab Bhattacharya
https://arxiv.org/abs/2508.00679

Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries
Legal precedent retrieval is a cornerstone of the common law system, governed by the principle of stare decisis, which demands consistency in judicial decisions. However, the growing complexity and volume of legal documents challenge traditional retrieval methods. TraceRetriever mirrors real-world legal search by operating with limited case information, extracting only rhetorically significant segments instead of requiring complete documents. Our pipeline integrates BM25, Vector Database, and C…

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
Yicong Zhao, Shisong Chen, Jiacheng Zhang, Zhixu Li
https://arxiv.org/abs/2509.02330 htt…

ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation
Recent advances in large language models (LLMs) have demonstrated impressive capabilities in code-related tasks, such as code generation and automated program repair. Despite their promising performance, most existing approaches for code repair suffer from high training costs or computationally expensive inference. Retrieval-augmented generation (RAG), with its efficient in-context learning paradigm, offers a more scalable alternative. However, conventional retrieval strategies, which are often…

This https://arxiv.org/abs/2506.00261 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

GPR: Empowering Generation with Graph-Pretrained Retriever
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. GPR aligns natural language questions with relevant subgraphs through LLM-guided graph augmentation and employs a structure-aware…

CMRAG: Co-modality-based document retrieval and visual question answering
Wang Chen, Guanqiang Qi, Weikang Li, Yang Li
https://arxiv.org/abs/2509.02123 https://

CMRAG: Co-modality-based document retrieval and visual question answering
Retrieval-Augmented Generation (RAG) has become a core paradigm in document question answering tasks. However, existing methods have limitations when dealing with multimodal documents: one category of methods relies on layout analysis and text extraction, which can only utilize explicit text information and struggle to capture images or unstructured content; the other category treats document segmentation as visual input and directly passes it to visual language models (VLMs) for processing, ye…