SCORE-RAG: Self-Correcting Exploration-Exploitation Retrieval for Multi-hop Question Answering

Evaluating RAG approaches for Question Answering

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering

Guided Retrieval Training (GRT) is introduced, a novel method that improves the performance of a search agent by restricting the retrieval process during RL training using ground truth information, and enhances training efficiency by achieving better QA performance with fewer training steps.

MCoRe: Multi-Entry Complementary Retrieval with Reflection-Guided Iteration for Multi-Hop QA

Retrieval-augmented generation (RAG) has become a standard paradigm for knowledge-intensive question answering by grounding large language models (LLMs) in external evidence. However, open-domain multi-hop question answering (QA) remains challenging for two reasons. First, evidence dispersion across documents and non-contiguous spans means that critical bridge evidence can be weakly related to query and is easy to miss. Second, semantic-resolution mismatch complicates retrieval: coarser retrieval views offer better global coherence but may obscure the exact bridging detail, while finer-grained views highlight specific mentions but may omit the context needed to reveal the relation. In this paper, we propose MCoRe, a multi-entry complementary retrieval framework with reflection-guided iteration for multi-hop QA. To mitigate the semantic-resolution mismatch, MCoRe enables multi-entry complementary retrieval by indexing entry units at multiple semantic resolutions (entities, sentences, and summaries) with explicit links to chunk evidence, mapping all hits back to chunks, and fusing cross-resolution hits via chunk-level voting to form a compact evidence set for answer generation. To cope with evidence dispersion, MCoRe performs reflection-guided iteration: when evidence is insufficient, it identifies the missing bridge cue and issues a gap-focused follow-up query to recover it. Empirical results demonstrate the effectiveness of MCoRe, which consistently outperforms state-of-the-art baselines by 6.77 EM points and 8.79 F1 points averaged over three multi-hop QA benchmarks, with gains of up to 12.70 EM and 14.06 F1 points on 2Wiki.

SAC-RAG: Semantic Adaptive Context Compression for Retrieval-Augmented Generation

Experimental results show that SAC-RAG reduces token consumption by 38%–58% at the cost of only a 1–2 percentage point EM drop, with EM actually improving after compression for reasoning-type questions, achieving the optimal quality–efficiency trade-off in terms of token consumption.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries

Retrieval-Augmented Generation (RAG) systems are widely adopted in question answering, yet they often fail to satisfy complex multi-constraint queries, leading to constraint violations, factual inconsistencies, or hallucinations. We present Structure-Driven RAG System for Multi-Constraint Queries(MC-RAG), a structure-driven RAG system that reformulates retrieval as a subgraph matching problem over a knowledge graph. By integrating semantic and structural embeddings with path-level indexing, MC-RAG performs interpretable, structure-aware, and constraint-consistent retrieval and generation. During the demonstration, participants can input medical or encyclopedic multi-constraint queries, visualize how the system parses constraints, performs structural matching, and generates answers, thereby experiencing an end-to-end, interactive, and explainable RAG pipeline. A demo video is available at https://youtu.be/J8kahzmAnu0.

Towards Adaptive and Retriever-friendly Retrieval-augmented Generation via Reinforcement Learning

Retrieval-augmented Generation (RAG) has emerged as an effective paradigm for enhancing large language models (LLMs) with external knowledge, delivering substantial performance gains without costly parameter updates, particularly in low-resource settings. However, existing RAG systems still face several open challenges. Many approaches rely on static or heuristic retrieval strategies, limiting their ability to adapt retrieval strategies to different tasks with different reasoning requirements. Some methods build RAG systems based on independently optimized components, making joint optimization across all components infeasible. Moreover, empirical evidence shows that stronger retrievers do not necessarily lead to better end-to-end RAG performance, revealing a misalignment between retrieval quality and generation effectiveness and leaving considerable room for improvement in how retrieval is integrated into the RAG pipeline. Motivated by these challenges, we propose ARF-RAG, an Adaptive Retriever-Friendly Retrieval-Augmented Generation framework. ARF-RAG dynamically determines when to retrieve and further generates retriever-friendly queries that faithfully reflect the generator's current information needs while being optimized for the behavior of the given retriever. ARF-RAG adopts a role-unified mechanism, in which a single LLM simultaneously performs all retrieval-related and generation actions, including retrieval decision-making, query generation, and answer generation, enabling coherent optimization across all components. To effectively align the retriever and generator, we formulate RAG as a unified reinforcement learning (RL) problem. We design comprehensive reward functions that jointly account for answer correctness, retrieval effectiveness, and retrieval cost, enabling fine-grained policy optimization via proximal policy optimization (PPO). Extensive experiments. The codebase for reproducing the proposed framework is publicly available at: https://github.com/ii-research/ARF-RAG on three question-answering benchmarks demonstrate that ARF-RAG consistently outperforms strong non-RL and RL-based baselines on both simple and complex QA tasks. Further analyses show that ARF-RAG adaptively learns when to retrieve based on question complexity and generates more effective, retriever-aligned queries that resolve what to retrieve, highlighting the advantages of unified RL optimization for building robust and efficient RAG systems.