A Survey of Reasoning-Intensive Retrieval: Progress and Challenges (2026.acl-long)
Copied to clipboard
| Challenge: | Reasoning-Intensive Retrieval (RIR) targets retrieval settings where relevance is mediated by latent inferential links between a query and supporting evidence, rather than semantic similarity. |
| Approach: | They propose a taxonomy that categorizes methods based on where and how reasoning is integrated into the retrieval pipeline. |
| Outcome: | The proposed method framework provides a detailed analysis of the current landscape and its trade-offs and practical applications. |
Similar Papers
Reinforced Query Reasoners for Reasoning-intensive Retrieval Tasks (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for retrieval of information excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks. |
| Approach: | They propose a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval. |
| Outcome: | The proposed model outperforms existing models on a BRIGHT benchmark with BM25 retrievers. |
Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
Copied to clipboard
| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
RaDeR: Reasoning-aware Dense Retrieval Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | RaDeR retrievers outperform strong baselines in reasoning tasks . large language models (LLMs) have impressive reasoning capabilities on a wide range of tasks - however, they face challenges when reasoning is needed for relevance prediction. |
| Approach: | They propose a set of reasoning-based dense retrieval models trained with data derived from mathematical problem solving using large language models. |
| Outcome: | The proposed model outperforms baselines on the BRIGHT and RAR-b benchmarks and achieves comparable or superior performance while using only 2.5% of the training data used by the concurrent work ReasonIR. |
Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems (2026.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation benchmarks for retrievers are narrow and evaluate them in isolation . existing evaluation benchmarking frameworks focus on evaluating retrievers in isolation, obscuring their value in real-world applications. |
| Approach: | They propose an evaluation framework that evaluates retrievers in agentic search systems . they provide expert-annotated reasoning aspects, positive documents, a reference response and evaluation rubrics . |
| Outcome: | The proposed framework assesses retrievers in agentic search systems. |
A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)
Copied to clipboard
Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu
| Challenge: | a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences. |
| Approach: | They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference . |
| Outcome: | The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks. |
Topology-of-Question-Decomposition: Enhancing Large Language Models with Information Retrieval for Knowledge-Intensive Tasks (2025.coling-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are constrained to chaining immediate reasoning steps and relying solely on parametric knowledge. |
| Approach: | They propose a framework that activates retrieval only when necessary to improve answer accuracy. |
| Outcome: | Experiments show that the proposed framework improves performance in knowledge-intensive tasks. |
ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for enhancing dense retrieval with query augmentation ignore the alignment between generation and ranking objectives. |
| Approach: | They propose a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever. |
| Outcome: | Experimental results show that ExpandR outperforms strong baselines, achieving more than 5% improvement in retrieval performance. |
LimRank: Less is More for Reasoning-Intensive Information Reranking (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to rerank information require large-scale fine-tuning, which is computationally expensive. |
| Approach: | They propose an open-source pipeline for generating diverse, challenging, and realistic reranking examples. |
| Outcome: | The proposed model performs competitively on two benchmarks, while being trained on less than 5% of the data typically used in prior work. |
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
Copied to clipboard
| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
| Outcome: | The proposed model outperforms previous approaches by a significant margin in QA tasks over text. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
Copied to clipboard
Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |