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arXivRetrieval-augmented generation

UNREAL: Unifying Retrieval and Long-Context with a Single Model

UNREAL unifies retrieval and long-context evidence selection via frozen LLM representations with minimal parameters, outperforming state-of-the-art retrievers and improving long-context accuracy substantially.

Edan Kinderman, Elad Hoffer, Yochai Blau, Brian Chmiel and 3 more

Published Oct 6, 2026 · ▲ 15 on Hugging Face

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arXivRetrieval-augmented generation

AGO AI Quality Gate: Evidence-First Release Decisions for Retrieval-Augmented Generation

AGO is an evidence-first quality gate for RAG that treats judge errors and missing data as explicit outcomes, using stratified beta-binomial gates and mandatory meta-evaluation to reduce unsafe promotion to 22.2%-35.1% versus 29.3%-41.8% for naive gates.

Giulio Zeloni, Enrico Lo Conte, Salvatore Rionero, Giuseppe Santoro and 2 more

Published Oct 1, 2026 · 0 citations · ▲ 13 on Hugging Face

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AI panel: 16 of 20 reviewers recommend it
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arXivJohns HopkinsRetrieval-augmented generation

Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification

Automatic taxonomies reveal retrieval and verifier reasoning failures persist across scaled medical retrieve-then-verify systems, showing fundamental open-ended evaluation limits.

Heyuan Huang, Jirui Dai, Alexandra DeLucia, Sonal Joshi and 4 more

Published Sep 24, 2026 · 0 citations · ▲ 12 on Hugging Face

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
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71%Highly rated
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U VirginiaLinkedInRetrieval-augmented generation

CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture

CoRAG dynamically selects textual or graph retrieval and blends results for global hybrid knowledge access, outperforming local hybrid RAG on QA benchmarks.

Zaiyi Zheng, Song Wang, Zihan Chen, Yaochen Zhu and 4 more

Published 2025 · 0 citations

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AI panel: 6 of 20 reviewers recommend it
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NeurIPS 2026Retrieval-augmented generation

How Much Evidence Should Retrieval-Augmented In-Context Learning Use Under Distribution Shift?

Chen Wang

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ITMOD ONE / ITMORetrieval-augmented generation

Machine Learning-Driven RAG System Design

Anastasia Orlova, Nina Gubina, Aleksei Dmitrenko, Arsen Sarkisyan and 6 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Universidade Federal do CearáUniversidade de São PauloAvraRetrieval-augmented generation

LP-RAG: Learning to Retrieve with Link Predictors

Erik Jhones Freitas do Nascimento, Jorge Franco, Amauri Souza

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Beijing Institute of TechnologyRetrieval-augmented generation

When Does Graph Retrieval Become Answer-Supporting Evidence? A Diagnostic Audit of GraphRAG

Chenghua Duan, Zhuo Wang, boheng liu, Ziyu Li and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Chinese University of Hong KDepartment of Computer Science aRetrieval-augmented generation

LEAP: Library-driven Evolutionary Abstraction Paradigm for Large Language Models

Fanbin Lu, Chi-Wing Fu, Jiaya Jia

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Electronic Science and TechnolRetrieval-augmented generation

SAGE: Semantic Ambiguity Guided Capacity Expansion for Retrieval-Augmented Generation

Xunlei Chen, Jinyu Guo, Yi Gong, Qirui Ye and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026South China University of TechnoRetrieval-augmented generation

Geometric Gain Graph: Zero-Token Graph Construction for Multi-Hop RAG

Zeliang Li, Xiaofen Xing, Wenyu Tao, Kailing Guo and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The University of Electro-CommunRetrieval-augmented generation

TRIM: A Theory of Retrieval with Incremental Memory -- Defect, Benefit, and Critical Horizon

Hiroyuki Kasai

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026HanyangRetrieval-augmented generation

ShiftRAG: Bypassing the Textual Bottleneck via Decoupled Learning and Continuous Soft Tokens

Youmin Ko, Jihong Jeong, Hyunjoon Kim

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ZhejiangHunanIndependent ResearcherNational University of SingaporeRetrieval-augmented generation

RealDev-QA: Trajectory-Level Diagnosis for Developer RAG Under Real-World Noises

Tianling Lan, Yao Junxiao, Tingyang Chen, Cibo Yu and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Harbin Institute of TechnologySingapore ManagementRetrieval-augmented generation

Not All Gap Correction Helps: A Geometric View of External Information in LLM Inference

Dingzirui Wang, Xuanliang Zhang, Keyan Xu, Qingfu Zhu and 2 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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67%Highly rated
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NeurIPS 2026SUN YAT-SEN UNIVERSITYTsinghuaU Science and Technology of ChinNorthwestern PolytechnicalRetrieval-augmented generation

Constraint Retrieval Is Not Constraint Enforcement in Large Language Models

Yongkang Yang, Xiankun Lin, Lixin Liu, Ziyan Liu and 4 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Huawei Technologies Ltd.Huawei TechnologiesEurecomEURECOMHuawei Technologies France SASURetrieval-augmented generation

Do LLMs Bind Episodes? Probing Cross-Episode Parametric Retrieval Through Shared Cues

Francesco Mantovani, Alberto Eusebio, Alexis Huet, Giulio Franzese and 3 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Shanghai JiaotongShanghai Jiao TongThe University of Electro-CommunShenzhen Univ of Advanced TechnoRetrieval-augmented generation

TRIDENT: Post-Selection Evidence Accountability for Token-Efficient RAG

Wutong Zhang, Jiong Lou, Richard Liu, Sizhe Zhang and 5 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Renmin University of ChinaClaremont McKenna CollegeRetrieval-augmented generation

OpticalRAG: Pixel-Space Compression for Token-Efficient Retrieval-Augmented Generation

Senhao Liu, Yuheng Zhang, Chunyu Wei, Yueguo Chen and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Nanyang TechnologicalU Science and Technology of ChinSUN YAT-SEN UNIVERSITYRetrieval-augmented generation

InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories

InduceKV stores attention-ready KV memory entries with fixed memory budgets to adapt multimodal LLMs continually, outperforming PEFT, replay, and prompt-retrieval baselines.

Qianyu Chen, Ziteng Feng, Canran Xiao, Runxuan Tang

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
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