Retrieval Or DROP Benchmark Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 129 papers for Retrieval Or DROP Benchmark Papers. Dominant protocol signals include automatic metrics, simulation environments, human evaluation, with frequent benchmark focus on Retrieval, DROP and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Top Papers On This Benchmark

• SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables

  Sungho Park, Jueun Kim, Wook-Shin Han · Feb 26, 2026

  Automatic Metrics

  Yet existing benchmarks are small, manually curated - and therefore error-prone - and contain shallow questions that seldom demand more than two hops or invoke aggregations, grouping, or other advanced analytical operations expressible in n

• AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

  Yutong Wang, Siyuan Xiong, Xuebo Liu, Wenkang Zhou, Liang Ding · Feb 26, 2026

  Automatic Metrics Multi Agent

  While Multi-Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading impact of erroneous information generated by individual participants.

• MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations

  Sara Rosenthal, Yannis Katsis, Vraj Shah, Lihong He, Lucian Popa · Feb 26, 2026

  Automatic Metrics

  We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augmented generation, a popular use of large language models.

• MoDora: Tree-Based Semi-Structured Document Analysis System

  Bangrui Xu, Qihang Yao, Zirui Tang, Xuanhe Zhou, Yeye He · Feb 26, 2026

  Automatic Metrics

  Semi-structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and often irregular layouts.

• TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought

  Jianmin Li, Ying Chang, Su-Kit Tang, Yujia Liu, Yanwen Wang · Feb 26, 2026

  Automatic Metrics

  Additionally, TCM-DiffRAG outperformed directly supervised fine-tuned (SFT) LLMs and other benchmark RAG methods.

• Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators

  Zhengyang Su, Isay Katsman, Yueqi Wang, Ruining He, Lukasz Heldt · Feb 26, 2026

  Automatic Metrics

  In addition, evaluation on academic benchmarks demonstrates that STATIC can considerably improve cold-start performance for generative retrieval.

• Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA

  Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun · Feb 26, 2026

  Automatic Metrics

  We propose a reinforced co-adaptation framework that jointly optimizes retrieval and generation through two components: (1) Graph-aware Retrieval (GraphRAG), which models entity-relation structure over a high-citation knowledge subgraph for

• Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance

  Weida Liang, Yiyou Sun, Shuyuan Nan, Chuang Li, Dawn Song · Feb 26, 2026

  Automatic Metrics

  Through a controlled analysis of paired human-written and model-generated solutions, we identify a systematic dissociation between usage and executability: human- and model-derived strategies differ in structured, domain-dependent ways, lea

• Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training

  Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li · Feb 26, 2026

  Automatic Metrics Long Horizon

  Agentic RAG addresses this by enabling LLMs to dynamically decide when and what to retrieve, but current RL-based training methods suffer from sparse outcome rewards that discard intermediate signals and low sample efficiency where failed s

• Bridging Latent Reasoning and Target-Language Generation via Retrieval-Transition Heads

  Shaswat Patel, Vishvesh Trivedi, Yue Han, Yihuai Hong, Eunsol Choi · Feb 25, 2026

  Automatic Metrics

  Across four multilingual benchmarks (MMLU-ProX, MGSM, MLQA, and XQuaD) and two model families (Qwen-2.5 and Llama-3.1), we demonstrate that masking RTH induces bigger performance drop than masking Retrieval Heads (RH).

• DySCO: Dynamic Attention-Scaling Decoding for Long-Context LMs

  Xi Ye, Wuwei Zhang, Fangcong Yin, Howard Yen, Danqi Chen · Feb 25, 2026

  Automatic Metrics

  Across multiple instruction-tuned and reasoning models, DySCO consistently improves performance on challenging long-context reasoning benchmarks, yielding relative gains of up to 25% on MRCR and LongBenchV2 at 128K context length with modes

• Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

  Christian Nickel, Laura Schrewe, Florian Mai, Lucie Flek · Feb 25, 2026

  Automatic Metrics

  Theory of Mind (ToM) refers to an agent's ability to model the internal states of others.

• Personalized Graph-Empowered Large Language Model for Proactive Information Access

  Chia Cheng Chang, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen · Feb 25, 2026

  Automatic Metrics

  Since individuals may struggle to recall all life details and often confuse events, establishing a system to assist users in recalling forgotten experiences is essential.

• FewMMBench: A Benchmark for Multimodal Few-Shot Learning

  Mustafa Dogan, Ilker Kesen, Iacer Calixto, Aykut Erdem, Erkut Erdem · Feb 25, 2026

  Automatic Metrics

  In this paper, we introduce FewMMBench, a comprehensive benchmark designed to evaluate MLLMs under few-shot conditions, with a focus on In-Context Learning (ICL) and Chain-of-Thought (CoT) prompting.

• Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem

  Heejin Jo · Feb 25, 2026

  Automatic Metrics

  Large language models consistently fail the "car wash problem," a viral reasoning benchmark requiring implicit physical constraint inference.

• Mitigating Structural Noise in Low-Resource S2TT: An Optimized Cascaded Nepali-English Pipeline with Punctuation Restoration

  Tangsang Chongbang, Pranesh Pyara Shrestha, Amrit Sarki, Anku Jaiswal · Feb 25, 2026

  Automatic Metrics

  We first establish highly proficient ASR and NMT components: a Wav2Vec2-XLS-R-300m model achieved a state-of-the-art 2.72% CER on OpenSLR-54, and a multi-stage fine-tuned MarianMT model reached a 28.32 BLEU score on the FLORES-200 benchmark

• Structurally Aligned Subtask-Level Memory for Software Engineering Agents

  Kangning Shen, Jingyuan Zhang, Chenxi Sun, Wencong Zeng, Yang Yue · Feb 25, 2026

  Automatic Metrics Long Horizon

  Large Language Models (LLMs) have demonstrated significant potential as autonomous software engineering (SWE) agents.

• Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access

  Touseef Hasan, Laila Cure, Souvika Sarkar · Feb 25, 2026

  Simulation Env

  We conduct a pilot evaluation study using community-sourced queries to examine system behavior in realistic scenarios.

• Revisiting RAG Retrievers: An Information Theoretic Benchmark

  Wenqing Zheng, Dmitri Kalaev, Noah Fatsi, Daniel Barcklow, Owen Reinert · Feb 25, 2026

  Automatic Metrics

  Existing benchmarks primarily compare entire RAG pipelines or introduce new datasets, providing little guidance on selecting or combining retrievers themselves.

• Revisiting Text Ranking in Deep Research

  Chuan Meng, Litu Ou, Sean MacAvaney, Jeff Dalton · Feb 25, 2026

  Automatic Metrics

  To tackle it, most prior work equips large language model (LLM)-based agents with opaque web search APIs, enabling agents to iteratively issue search queries, retrieve external evidence, and reason over it.

• Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG

  Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy, Chiara Picardi, Amit Giloni · Feb 24, 2026

  Automatic Metrics

  Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation components.

• Beyond Subtokens: A Rich Character Embedding for Low-resource and Morphologically Complex Languages

  Felix Schneider, Maria Gogolev, Sven Sickert, Joachim Denzler · Feb 24, 2026

  Automatic Metrics

  Tokenization and sub-tokenization based models like word2vec, BERT and the GPTs are the state-of-the-art in natural language processing.

• Multi-Vector Index Compression in Any Modality

  Hanxiang Qin, Alexander Martin, Rohan Jha, Chunsheng Zuo, Reno Kriz · Feb 24, 2026

  Automatic Metrics

  We study efficient multi-vector retrieval for late interaction in any modality.

• A Benchmark for Deep Information Synthesis

  Debjit Paul, Daniel Murphy, Milan Gritta, Ronald Cardenas, Victor Prokhorov · Feb 24, 2026

  Human EvalAutomatic Metrics Tool Use

  Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis.

• HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG

  Yuqi Huang, Ning Liao, Kai Yang, Anning Hu, Shengchao Hu · Feb 24, 2026

  Automatic Metrics

  Extensive experiments demonstrate that HELP achieves competitive performance across multiple simple and multi-hop QA benchmarks and up to a 28.8$\times$ speedup over leading Graph-based RAG baselines.

• E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications

  Jiwoo Kang, Yeon-Chang Lee · Feb 24, 2026

  Automatic Metrics

  Multimodal recommender systems (MMRSs) enhance collaborative filtering by leveraging item-side modalities, but their reliance on a fixed set of modalities and task-specific objectives limits both modality extensibility and task generalizati

• RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition

  Kun Ran, Marwah Alaofi, Danula Hettiachchi, Chenglong Ma, Khoi Nguyen Dinh Anh · Feb 24, 2026

  Automatic Metrics

  R2RAG won the Best Dynamic Evaluation award in the Open Source category, demonstrating high effectiveness with careful design and efficient use of resources.

• Online Algorithms with Unreliable Guidance

  Julien Dallot, Yuval Emek, Yuval Gil, Maciej Pacut, Stefan Schmid · Feb 24, 2026

  Automatic Metrics

  This paper introduces a new model for ML-augmented online decision making, called online algorithms with unreliable guidance (OAG).

• Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems

  Mukul Chhabra, Luigi Medrano, Arush Verma · Feb 23, 2026

  Automatic Metrics

  Enterprise Retrieval-Augmented Generation (RAG) assistants operate in multi-turn, case-based workflows such as technical support and IT operations, where evaluation must reflect operational constraints, structured identifiers (e.g., error c

• InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation

  Yu Li, Pranav Narayanan Venkit, Yada Pruksachatkun, Chien-Sheng Wu · Feb 23, 2026

  Simulation Env

  Existing evaluation approaches rely on demographic surveys, personality questionnaires, or short AI-led interviews as proxies, but lack direct assessment against what individuals actually said.

• KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

  Mohammad Amanlou, Erfan Shafiee Moghaddam, Yasaman Amou Jafari, Mahdi Noori, Farhan Farsi · Feb 23, 2026

  Automatic Metrics

  Results show that KNIGHT enables token- and cost-efficient generation from a reusable graph representation, achieves high quality across these criteria, and yields model rankings aligned with MMLU-style benchmarks, while supporting topic-sp

• How Retrieved Context Shapes Internal Representations in RAG

  Samuel Yeh, Sharon Li · Feb 23, 2026

  Automatic Metrics

  Retrieval-augmented generation (RAG) enhances large language models (LLMs) by conditioning generation on retrieved external documents, but the effect of retrieved context is often non-trivial.

• Structured Prompt Language: Declarative Context Management for LLMs

  Wen G. Gong · Feb 23, 2026

  Automatic Metrics

  SPL-flow extends SPL into resilient agentic pipelines with a three-tier provider fallback strategy (Ollama -> OpenRouter -> self-healing retry) fully transparent to the .spl script.

• Cross-lingual Matryoshka Representation Learning across Speech and Text

  Yaya Sy, Dioula Doucouré, Christophe Cerisara, Irina Illina · Feb 23, 2026

  Automatic Metrics

  We introduce large-scale data curation pipelines and new benchmarks, compare modeling strategies, and show that modality fusion within a frozen text Matryoshka model performs best.

• Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval

  Yibo Yan, Jiahao Huo, Guanbo Feng, Mingdong Ou, Yi Cao · Feb 23, 2026

  Automatic Metrics

  We begin by examining the benchmark landscape, and subsequently dive into the methodological evolution, categorizing approaches into three primary aspects: multimodal embedding models, multimodal reranker models, and the integration of Retr

• Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework

  Yibo Yan, Mingdong Ou, Yi Cao, Xin Zou, Jiahao Huo · Feb 23, 2026

  Automatic Metrics

  Visual Document Retrieval (VDR), which aims to retrieve relevant pages within vast corpora of visually-rich documents, is of significance in current multimodal retrieval applications.

• How to Train Your Deep Research Agent? Prompt, Reward, and Policy Optimization in Search-R1

  Yinuo Xu, Shuo Lu, Jianjie Cheng, Meng Wang, Qianlong Xie · Feb 23, 2026

  Automatic Metrics

  Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation.

• Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering

  Maryam Amirizaniani, Alireza Salemi, Hamed Zamani · Feb 22, 2026

  Automatic Metrics Long Horizon

  Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context.

• Retrieval Augmented Enhanced Dual Co-Attention Framework for Target Aware Multimodal Bengali Hateful Meme Detection

  Raihan Tanvir, Md. Golam Rabiul Alam · Feb 22, 2026

  Automatic Metrics

  Hateful content on social media increasingly appears as multimodal memes that combine images and text to convey harmful narratives.

• Facet-Level Persona Control by Trait-Activated Routing with Contrastive SAE for Role-Playing LLMs

  Wenqiu Tang, Zhen Wan, Takahiro Komamizu, Ichiro Ide · Feb 22, 2026

  Automatic Metrics

  Personality control in Role-Playing Agents (RPAs) is commonly achieved via training-free methods that inject persona descriptions and memory through prompts or retrieval-augmented generation, or via supervised fine-tuning (SFT) on persona-s

• VIGiA: Instructional Video Guidance via Dialogue Reasoning and Retrieval

  Diogo Glória-Silva, David Semedo, João Maglhães · Feb 22, 2026

  Automatic Metrics Long Horizon

  Our evaluation shows that VIGiA outperforms existing state-of-the-art models on all tasks in a conversational plan guidance setting, reaching over 90\% accuracy on plan-aware VQA.

• AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG

  Qijie You, Wenkai Yu, Wentao Zhang · Feb 22, 2026

  Automatic Metrics Long Horizon

  With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction.

• Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem

  Lichang Song, Ting Long, Yi Chang · Feb 21, 2026

  Automatic Metrics Multi Agent

  To overcome this limitation, we reformulate RAG as a cooperative multi-agent decision-making problem and propose Cooperative Retrieval-Augmented Generation (CoRAG), a framework in which the reranker and the generator act as peer decision-ma

• Contradiction to Consensus: Dual Perspective, Multi Source Retrieval Based Claim Verification with Source Level Disagreement using LLM

  Md Badsha Biswas, Ozlem Uzuner · Feb 21, 2026

  Automatic Metrics

  Through extensive evaluation on four benchmark datasets with five LLMs, we show that knowledge aggregation not only improves claim verification but also reveals differences in source-specific reasoning.

• Diagnosing LLM Reranker Behavior Under Fixed Evidence Pools

  Baris Arat, Emre Sefer · Feb 20, 2026

  Automatic Metrics

  Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever.

• RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering

  Deniz Qian, Hung-Ting Chen, Eunsol Choi · Feb 20, 2026

  Automatic Metrics

  Our method outperforms baselines, including agentic search approaches, achieving at least 10% relative and 3% absolute gain in complete recall percentage on a multi-answer retrieval dataset (QAMPARI).

• Subgroups of $U(d)$ Induce Natural RNN and Transformer Architectures

  Joshua Nunley · Feb 20, 2026

  Automatic Metrics

  This paper presents a direct framework for sequence models with hidden states on closed subgroups of U(d).

• Validating Political Position Predictions of Arguments

  Jordan Robinson, Angus R. Williams, Katie Atkinson, Anthony G. Cohn · Feb 20, 2026

  Human Eval

  Real-world knowledge representation often requires capturing subjective, continuous attributes -- such as political positions -- that conflict with pairwise validation, the widely accepted gold standard for human evaluation.

• Decomposing Retrieval Failures in RAG for Long-Document Financial Question Answering

  Amine Kobeissi, Philippe Langlais · Feb 20, 2026

  Automatic Metrics

  Retrieval-augmented generation is increasingly used for financial question answering over long regulatory filings, yet reliability depends on retrieving the exact context needed to justify answers in high stakes settings.

• CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications

  Victoria Blake, Mathew Miller, Jamie Novak, Sze-yuan Ooi, Blanca Gallego · Feb 20, 2026

  Automatic Metrics

  The framework was evaluated on five lexically heterogeneous clinical concepts against a manually curated benchmark and gold-standard concept sets.

• Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering

  Jash Rajesh Parekh, Wonbin Kweon, Joey Chan, Rezarta Islamaj, Robert Leaman · Feb 20, 2026

  Automatic Metrics

  Existing benchmarks do not evaluate such conditional reasoning, and retrieval-augmented or graph-based methods lack explicit mechanisms to ensure that retrieved knowledge is applicable to given context.

• Improving Neural Topic Modeling with Semantically-Grounded Soft Label Distributions

  Raymond Li, Amirhossein Abaskohi, Chuyuan Li, Gabriel Murray, Giuseppe Carenini · Feb 20, 2026

  Automatic Metrics

  Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity.

• TFL: Targeted Bit-Flip Attack on Large Language Model

  Jingkai Guo, Chaitali Chakrabarti, Deliang Fan · Feb 19, 2026

  Automatic Metrics

  Large language models (LLMs) are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks.

• QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration

  Meng Ye, Xiao Lin, Georgina Lukoczki, Graham W. Lederer, Yi Yao · Feb 19, 2026

  Automatic Metrics

  Mineral prospectivity mapping requires synthesizing heterogeneous geological knowledge, including textual deposit models and geospatial datasets, to identify regions likely to host specific mineral deposit types.

• Unmasking the Factual-Conceptual Gap in Persian Language Models

  Alireza Sakhaeirad, Ali Ma'manpoosh, Arshia Hemmat · Feb 19, 2026

  Automatic Metrics

  While emerging Persian NLP benchmarks have expanded into pragmatics and politeness, they rarely distinguish between memorized cultural facts and the ability to reason about implicit social norms.

• PEACE 2.0: Grounded Explanations and Counter-Speech for Combating Hate Expressions

  Greta Damo, Stéphane Petiot, Elena Cabrio, Serena Villata · Feb 19, 2026

  Automatic Metrics

  The increasing volume of hate speech on online platforms poses significant societal challenges.

• ABCD: All Biases Come Disguised

  Mateusz Nowak, Xavier Cadet, Peter Chin · Feb 19, 2026

  Automatic Metrics

  Multiple-choice question (MCQ) benchmarks have been a standard evaluation practice for measuring LLMs' ability to reason and answer knowledge-based questions.

• The Role of the Availability Heuristic in Multiple-Choice Answering Behaviour

  Leonidas Zotos, Hedderik van Rijn, Malvina Nissim · Feb 19, 2026

  Automatic Metrics

  When students are unsure of the correct answer to a multiple-choice question (MCQ), guessing is common practice.

• RPDR: A Round-trip Prediction-Based Data Augmentation Framework for Long-Tail Question Answering

  Yiming Zhang, Siyue Zhang, Junbo Zhao, Chen Zhao · Feb 19, 2026

  Automatic Metrics

  We evaluate RPDR on two long-tail retrieval benchmarks, PopQA and EntityQuestion, demonstrating substantial improvements over existing retrievers like BM25 and Contriver, especially on extremely long-tail categories.

• WebFAQ 2.0: A Multilingual QA Dataset with Mined Hard Negatives for Dense Retrieval

  Michael Dinzinger, Laura Caspari, Ali Salman, Irvin Topi, Jelena Mitrović · Feb 19, 2026

  Automatic Metrics

  We introduce WebFAQ 2.0, a new version of the WebFAQ dataset, containing 198 million FAQ-based natural question-answer pairs across 108 languages.

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