Retrieval-Augmented ICL

ICL: In-Context Learning

Retrieval-augmented in-context learning studies how to select and present examples or other evidence so a pretrained model can perform a task without changing its weights. Methods range from nearest-neighbor search with text or multimodal embeddings to task-aware retrievers trained with model feedback, structured context selection, and iterative agents that identify information gaps. These approaches are used in settings such as question answering, classification, reasoning, medical image interpretation, and robotics, with benchmarks often comparing retrieval against random or fixed demonstrations and against fine-tuning. A central open tension is that similarity does not always predict usefulness: retrieval must balance task relevance, context budget, evidence format, inference cost, and risks such as privacy leakage or negative transfer.

Latest papers

45 papers8 papers in the last four weeks, down 27% on the four weeks before. 0.0% of all new papers.

From Noisy Retrieval to Decision Evidence: Silver Evidence Demonstrations for In-Context Learning
Oct 23, 2026Masoud Monajatipoor, Fazlolah Mohaghegh, Hamid Reza Hassanzadeh +2Retrieval-Augmented ICL In-Context Learning
Optum AI
Beyond the Vote: When Do LLMs Add Value to Retrieval-Based Tabular Classification?
Oct 21, 2026Ziquan Wang, Yuxuan Nie, Jian Ma +1Tabular ICL Retrieval-Augmented ICL
Hangzhou International Innovation Institute, Beihang University, Hangzhou, China · Peking University, Beijing, China
RAG-RST: Retrieved Exemplars as In-Context Discourse Schema for RST Parsing
Oct 15, 2026Mohammadreza Sediqin, Shlomo Engelson ArgamonRetrieval-Augmented ICL
Illinois Institute of Technology New York, USA · Touro University New York, USA
Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution
Oct 14, 2026Yunze Liu, Jing Wang, Shenrui Wu +16Vision-Language-Action Models ICL for Robotics
NVIDIA · UCB · MIT +2
ActICL: Post-Training for Agentic Multimodal In-Context Learning
Oct 14, 2026Yanshu Li, Ruixiang Tang, Junyi Jessy Li +1Multimodal ICL Retrieval-Augmented ICL
University of Texas at Austin · 2Rutgers University
Retrieval-Based In-Context Learning: A Domain Adaptation Framework
Oct 5, 2026Yilun Zhu, Naihao Deng, Yingcong Li +2Distribution Shift Domain Adaptation
University of Michigan. · New Jersey Institute of Technology. · Northwestern University.
When Does Retrieval Help? A Study of In-Context Adaptation in Vision-Language-Action Models
Oct 4, 2026Zixuan Liu, Joris Köster, Zizhan Zheng +1Vision-Language-Action Models Retrieval-Augmented ICL
Department of Computer Science, Tulane University · Department of Computer Science, Aalto University · Department of Industrial Engineering and Management, Aalto University
RETRACE: From Entangled Repair Histories to Reusable Experience for CI Repair
Oct 3, 2026Rabeya Khatun Muna, Muhammad Ahasanuzzaman, Nakhla Rafi +3Automated Program Repair Retrieval-Augmented ICL
Software Performance, Analysis, and Reliability (SPEAR) Lab, Concordia University · Concordia University
You Only Edit Once: Incentivizing In-Context Capability of LLMs via Local Demonstration Refinement
Sep 27, 2026Jiarong Wen, Qi Wang, Yun Qu +8LLM Inference Efficiency In-Context Example Selection
Department of Automation, Tsinghua University
PULSE: Identifying Demonstration-Utility Features with Sparse Autoencoders
Sep 26, 2026Chenduo Hao, Chuanbao Gao, Pinjun Zeng +4In-Context Example Selection In-Context Learning
Southeast University · University of California, Los Angeles · School of Architecture, The University of Texas at Austin
URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER
Sep 21, 2026Jingyu Wang, Shijie Wu, Fusheng JinNamed Entity Recognition In-Context Learning
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
Representation-guided in-context learning for medical image interpretation with multimodal large language models
Sep 21, 2026Minda Zhao, Fangyu Hu, Yan Luo +8Medical Image Analysis Multimodal Large Language Models
Harvard AI and Robotics Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA · Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA · Department of Ophthalmology, National Taiwan University Hospital, Taipei City, Taiwan +6
Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol States
Sep 20, 2026Yifan Wang, Dejing DouLLM Context Compression Memory-Augmented Language Models
NexusLumenLabs · Fudan University
Where Should a Document Live: Context, Representations, or Parameters?
Sep 15, 2026Nathanaël Carraz Rakotonirina, Momchil Hardalov, Gonzalo Iglesias +1Memory-Augmented Language Models Knowledge Augmentation for Language Models
Amazon AGI
Option-Aware Retrieval and Task-Specific VLM Adaptation for Medical VQA
Sep 14, 2026Tristan Kirscher, Niklas C. Koser, Soren PirkVLM Adaptation Multiple-Choice Question Answering
ICube, Institut Strauss · CAU
Beyond Surface Imitation: Contrastive Modeling for Reasoning Path Alignment in Multimodal In-Context Learning
Sep 9, 2026Mingbo Yang, Wenqiang Wang, Zhaolu Kang +4Contrastive Learning Multimodal ICL
Sun Yat-Sen University Shenzhen, Guangdong, China · Peking University Beijing, Beijing, China · Sun Yat-Sen University Shenzhen, Guangdong, China Pengcheng Laboratory +1
MUCnoHARM@GermEval Shared Task 2026: Retrieval-based In-Context Learning for Defamatory Offences, and Where It Falls Short
Sep 9, 2026Kristin Gnadt, Maximilian Meidinger, Matthias AßenmacherLegal NLP In-Context Retrieval
Central Office for Information Technology in the Security Sector (ZITiS), Munich, Germany · Department of Statistics, LMU Munich, Germany · Munich Center for Machine Learning (MCML), Germany
AAS-RAIL: Improving Information Extraction for Asset Administration Shells through Retrieval-Augmented In-Context Learning
Sep 7, 2026Janek Groß, Jens HeidrichDocument Information Extraction LLM Information Extraction
University of Applied Sciences Mainz, Mainz, Rhineland-Palatinate, Germany
LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Models
Sep 5, 2026Mingqi Yang, Zidong Guo, Jihui Yang +1In-Context Learning Retrieval-Augmented ICL
Department of Electronic Business, South China University of Technology, Guangzhou, Guangdong 510006, China · Meta Superintelligence Lab
Understanding the Surprising Generalization Properties of Tabular Foundation Models
Aug 18, 2026Nour Shaheen, Junwei Ma, Alex Labach +3Tabular Foundation Models Tabular ICL
Polytechnique Montréal · Mila – Quebec AI Institute · Chandar Research Lab +6
Localized TabICLv2: Scaling Tabular In-Context Learning through k-NN
Aug 17, 2026Beimnet Bekele GutaTabular ICL Tabular Classification
University of Cambridge, Cambridge, United Kingdom.
Boosting Knowledge-based Visual Question Answering with Structured Context Reasoning
Aug 17, 2026Qiyou Liu, Yong Zhang, Jianjie Luo +2Knowledge-Based VQA VLM Reasoning
School of Computer Science, Guangdong University of Technology, Guangzhou, China · School of Artificial Intelligence, Shenzhen University, Shenzhen, China · Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima, Japan
Align-RAG: Alignment Is All You Need for TSFM In-Context Learning
Aug 6, 2026Mohammad Asadi, Soheil Hor, Bardiya Akhbari +6Zero-Shot Time Series Forecasting Retrieval-Augmented ICL
1Stanford University 2Amazon
Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning
Jul 30, 2026Xinyu Luo, Hui Liu, Yihua Shao +3In-Context Example Selection In-Context Retrieval
City University of Hong Kong, China · Institute of Automation Chinese Academy of Sciences, China
RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
Jul 29, 2026Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha +2Human Activity Recognition Cost-Aware Inference
University of Sydney, Australia · Curtin University, Australia · Colorado State University, USA
In-Context Learning for Wound Classification with Small Multimodal Language Models
Jul 21, 2026George Martvel, Oskar Gustafsson, John Pavia +1Multimodal ICL Multimodal Large Language Models
1Jönköping University, Jönköping, Sweden · 2Halmstad University, Halmstad, Sweden · 3Mölnlycke Health Care +1
GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
Jun 10, 2026Garvita Allabadi, Matteo Sodano, Roberto Estevão +4Multimodal ICL Multimodal Large Language Models
University of Illinois Urbana Champaign · University of Bonn · Microsoft
LLM-Based Synthetic Ground Truth Generation for Audio-Based Emotion Classification via In-Context Learning
Jun 10, 2026Qing Huang, Pooja Pol, Jianing ZhangLLM-Assisted Annotation Emotion Recognition in Conversations
School of Business, Technical University of Applied Sciences Augsburg, Friedberger Str. 4, 86161 Augsburg, Germany · Data Science und Autonome Systeme Technologietransferzentrum (TTZ), An der Schmiede 19, 86899 Landsberg am Lech, Germany
RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models
Jun 1, 2026Guangjin Pan, Hui Chen, Hei Victor Cheng +1Wireless Communications In-Context Learning
Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden · Department of Electrical and Computer Engineering, Aarhus University, Denmark
Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning
May 31, 2026Yiming Liao, Zeno Franco, Jose Eduardo Lizarraga Mazaba +1LLM Self-Correction LLM Hallucination Mitigation
University of Maryland, Baltimore County Baltimore, Maryland, USA · Concordia University Wisconsin · Medical College of Wisconsin Milwaukee, Wisconsin USA

Oct 23, 2026J/K move · Enter open · S save

From Noisy Retrieval to Decision Evidence: Silver Evidence Demonstrations for In-Context Learning

Masoud Monajatipoor, Fazlolah Mohaghegh, Hamid Reza Hassanzadeh, Akash Uttamrao Bhoite+1

Optum AI

Retrieval-Augmented ICL In-Context Learning Retrieval-Augmented Classification RAG Robustness

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RAG and retrieval-augmented few-shot ICL can struggle when decision-relevant evidence is buried in long, noisy context. Retrieved passages and demonstrations may contain redundant or distracting information. Although expert-labeled development examples may identify the text supporting each decision, these annotations are typically used only as answer-side rationales rather than as supervision for filtering noisy retrieval. We propose silver-evidence demonstrations, which replace raw retrieved passages in ICL examples with generated, target-conditioned evidence summaries. For each labeled development example, an LLM generates silver evidence without access to the expert label or gold evidence; this evidence is then paired with the expert label and gold evidence where available. At test time, a single structured call generates silver evidence from retrieved passages and then predicts the final label and decision evidence. Across EI 2.0, SciFact, synthetic policy retrieval, and HICRIC/IMR data, our full method improves over equally sized gold-only ICL (label-only for policy) on three of four datasets and improves evidence F1 on all annotated datasets. It also achieves higher mean label accuracy than InstructRAG-style ICL on all four datasets. These results suggest that replacing demonstration-side retrieved passages with compact, target-conditioned silver evidence is particularly useful for decisions based on long and noisy text, where it reduces distracting context while preserving decision-relevant signals and expert supervision.