Despite extensive research on a wide range of question answering (QA) systems, most existing work focuses on answer containment—i.e., assuming that answers can be directly extracted and/or generated from documents in the corpus. However, some questions require inference, i.e., deriving answers that are not explicitly stated but can be inferred from the available information. We introduce Inferential QA–a new task that challenges models to infer answers from answer-supporting passages which provide only clues. To study this problem, we construct Quit (QUestions requiring Inference from Texts) dataset, comprising 7,401 questions and 2.4M passages built from high-convergence human- and machine-authored hints, labeled across three relevance levels using LLM-based answerability and human verification. Through comprehensive evaluation of retrievers, rerankers, and LLM-based readers, we show that methods effective on traditional QA tasks struggle in inferential QA: retrievers underperform, rerankers offer limited gains, and fine-tuning provides inconsistent improvements. Even reasoning-oriented LLMs fail to outperform smaller general-purpose models. These findings reveal that current QA pipelines are not yet ready for inference-based reasoning. Inferential QA thus establishes a new class of QA tasks that move towards understanding and reasoning from indirect textual evidence.
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Deep Research systems have revolutionized how LLMs solve complex questions through iterative reasoning and evidence gathering. However, current systems remain fundamentally constrained to textual web data, overlooking the vast knowledge embedded in multimodal documents: scientific papers, technical reports, and financial documents where critical information exists in figures, tables, charts, and equations. Processing such documents demands sophisticated parsing to preserve visual semantics, intelligent chunking to maintain structural coherence, and adaptive retrieval across modalities, which are capabilities absent in existing systems. In response, we present Doc-Researcher, a unified system that bridges this gap through three integrated components: (i) deep multimodal parsing that preserves layout structure and visual semantics while creating multi-granular representations from chunk to document level, (ii) systematic retrieval architecture supporting text-only, vision-only, and hybrid paradigms with dynamic granularity selection, and (iii) iterative multi-agent workflows that decompose complex queries, progressively accumulate evidence, and synthesize comprehensive answers across documents and modalities. To enable rigorous evaluation, we introduce M4DocBench, the first benchmark for Multi-modal, Multi-hop, Multi-document, and Multi-turn deep research. Featuring 158 expert-annotated questions with complete evidence chains across 304 documents, M4DocBench tests capabilities that existing benchmarks cannot assess. Experiments demonstrate that Doc-Researcher achieves 50.6% accuracy, 3.4× better than state-of-the-art baselines, validating that effective document research requires not just better retrieval, but fundamentally deep parsing that preserve multimodal integrity and support iterative research. Our work establishes a new paradigm for conducting deep research on multimodal document collections.
Retrieval-augmented generation (RAG) has facilitated large language models (LLMs) by grounding facts in external knowledge. While prior RAG strategies can achieve dynamic control by detecting sentence-wise hallucination, thereby reducing unnecessary computation, they ignore the entity, statement, and their combinations. In this work, we analyze adaptive RAG methods through probing uncertainty to explore real-time entity- and statement-level verification, finding that such information can serve as training-free signals for hallucination detection, and the entropy probes can also guide principled document ranking. Inspired by these insights, we propose SeaRAG, a training-free, adaptive RAG framework that dynamically detects and mitigates hallucinations by probing LLMs at both entity and statement aspects, ranking retrieved passages via entropy-based uncertainty reduction, and regenerating evidence-grounded responses in real-time. Experiments across various Question-Answering benchmarks and multiple LLMs demonstrate consistent accuracy improvements and reduced retrieval rates. SeaRAG with self-correction outperforms Always Retrieve by up to 11.1% on TriviaQA while cutting retrieval frequency from 45.2%, offering an efficient and real-time hallucination control.
Large Language Models (LLMs) increasingly serve as research assistants, yet their reliability in scholarly tasks remains under-evaluated. In this work, we introduce PaperAsk, a benchmark that systematically evaluates LLMs across four key research tasks: citation retrieval, content extraction, paper discovery, and claim verification. We evaluate GPT-4o, GPT-5, and Gemini-2.5-Flash under realistic usage conditions, using web interfaces where search operations are opaque to the user. Through controlled experiments, we find consistent reliability failures: citation retrieval fails in 48–98% of multi-reference queries, section-specific content extraction fails in 72–91% of cases, and topical paper discovery yields F1 scores below 0.32, missing over 60% of relevant literature. Further human analysis attributes these failures to the uncontrolled expansion of retrieved context and the tendency of LLMs to prioritize semantically relevant text over task instructions. Across basic tasks, the LLMs display distinct failure behaviors: ChatGPT often withholds responses rather than risk errors, whereas Gemini produces fluent but fabricated answers. To address these issues, we develop lightweight reliability classifiers trained on PaperAsk data to identify unreliable outputs. PaperAsk provides a reproducible and diagnostic framework for advancing the reliability evaluation of LLM-based scholarly assistance systems. The benchmark is publicly available at https://github.com/wuyoscar/PaperAsk.
Multi–turn intent understanding is central to task–oriented chatbots, yet real deployments face tight token budgets and noisy contexts, and most retrieval pipelines emphasize relevance while overlooking set–level diversity and confounds such as ''more context'' or exemplar order. We ask whether retrieval diversity, rather than longer prompts, systematically improves LLM intent understanding under fixed budgets. We present a diversity–aware retrieval framework that selects in–context exemplars to balance intent coverage and linguistic variety, and integrates this selection with standard LLM decoders; the evaluation enforces budget–matched prompts and randomized positions, and includes sensitivity analyses over exemplar count, diversity strength, and backbone size. On MultiWOZ 2.4 and SGD, the approach achieves strong gains in Joint Goal Accuracy (JGA) under equal token budgets, surpassing strong LLM/DST baselines, with consistent improvements across K= 4 ~ 7 and moderate latency. Overall, the study isolates and validates the impact of content diversity in retrieval and offers a simple, deployable selection principle for building accurate, budget–constrained multi–turn intent systems.
Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and accuracy of Large Language Model (LLM) generations. However, this reliance on external data introduces new attack surfaces. Attackers can inject poisoned texts into databases to manipulate LLMs into producing harmful target responses for attacker-chosen queries. Existing research primarily focuses on attacking conventional RAG systems. However, such methods are ineffective against GraphRAG. This robustness derives from the KG abstraction of GraphRAG, which reorganizes injected text into a graph before retrieval, thereby enabling the LLM to reason based on the restructured context instead of raw poisoned passages. To expose latent security vulnerabilities in GraphRAG, we propose Knowledge Evolution Poison (KEPo), a novel poisoning attack method specifically designed for GraphRAG. For each target query, KEPo first generates a toxic event containing poisoned knowledge based on the target answer. By fabricating event backgrounds and forging knowledge evolution paths from original facts to the toxic event, it then poisons the KG and misleads the LLM into treating the poisoned knowledge as the final result. In multi-target attack scenarios, KEPo further connects multiple attack corpora, enabling their poisoned knowledge to mutually reinforce while expanding the scale of poisoned communities, thereby amplifying attack effectiveness. Experimental results across multiple datasets demonstrate that KEPo achieves state-of-the-art attack success rates for both single-target and multi-target attacks, significantly outperforming previous methods.
Conversational search allows multi-turn user-system interactions to support complex information seeking. It is challenging due to frequent topic shifts and ambiguous intentions in conversation. Although existing methods attempt to optimize retrieval performance through query rewriting or session encoding, they still face crucial challenges, including (1) Noisy context: the diverse types of noises in the conversation context prevent models from achieving reliable and consistent contextual understanding; (2) Poor interpretability: the lack of transparency in how results are generated undermines user trust, hindering the practical application of conversational search systems. In this paper, we propose LogiCGR, a novel framework that utilizes curriculum learning and group relative policy optimization (GRPO) to perform logic-enhanced retrieval, improving the robustness and interpretability of conversational search. Specifically, LogiCGR equips large language models (LLMs) with logical denoising and generative retrieval, integrating them seamlessly through an adaptive framework. Additionally, we introduce a lightweight module that works with generative retrieval for self-dual-path retrieval, thus delivering complementary performance gains. Extensive experiments and intuitive case studies demonstrate that our proposed LogiCGR outperforms state-of-the-art baselines in both retrieval performance and interpretability. The code and data are available at https://github.com/GenIRAG/LogiCGR.
Recent advances in enabling Large Language Models (LLMs) to use external tools have significantly extended their functional capabilities beyond internal knowledge. However, most existing approaches rely on retrieval-based mechanisms to select suitable tools from massive repositories, which often struggle to align user queries with tool documentation and exhibit limited generalization. To address these issues, we propose ToolBox-RL. The code is available at https://github.com/S-cavy/ToolBox-RL., a novel reinforcement learning framework that unifies query rewriting, intent understanding, and large-scale tool retrieval into an end-to-end optimization process. ToolBox-RL introduces a query rewriting stage to better capture user intent and ensure semantic alignment with tool descriptions, while reinforcement learning encourages autonomous discovery of generalized tool-use strategies through combined cold-start and policy optimization training. Our experiments demonstrate that ToolBox-RL not only achieves the best tool call accuracy on both white-box and black-box tools but also exhibits strong generalization capabilities on out-of-domain dataset. Ablation studies show that ToolBox-RL can adapt to different retrieval methods and maintains good performance even when incorporating a large number of distractor tools.
Knowledge Graph Question Answering (KGQA) leverages struc- tured knowledge graphs for reliable reasoning. Graph-based Retrieval- Augmented Generation (RAG) addresses the incompleteness and hallucination issues of Large Language Models (LLMs) by retrieving query-relevant subgraphs. However, existing approaches rely on single-intent semantic retrieval, compressing queries into single representations and optimizing each query independently. This leads to narrow triple selection that omits complementary information. While multi-intent retrieval diversification addresses this limitation, it faces critical challenges: (1) semantic diversification does not guarantee reasoning performance, and (2) co-selection frequency across intents does not ensure reasoning benefit. We propose Topic-Adaptive Retrieval Diversification (TARD), based on end-to-end optimization via generation feedback. TARD adaptively extracts multiple topic-based intents through neural topic modeling and employs a Gumbel-Softmax differentiable sampling to enable joint optimization. Supervised fine-tuning aligns the topic-adaptive multi-intent selector and triple scorer with reasoning performance to achieve beneficial consensus patterns. Adaptive direct preference optimization trains the generator to utilize relevant consensus while ignoring uninformative patterns. Experiments on WebQSP and CWQ show that TARD outperforms state-of-the-art baselines. Our code and data are publicly available at https://github.com/leedongcheon/TARD.
Cross-modal retrieval is a fundamental task in multimedia understanding, aimed at querying samples with similar semantics in one modality (e.g., text) using another modality (e.g., image). Existing methods merely focus on point-to-point comparisons between individual samples, while overlooking the widely present many-to-many structural relationships in real-world scenarios. However, the many-to-many relationships formed by multiple samples sharing similar semantics are crucial for effectively achieving semantic alignment and accurately constructing shared semantic representations. To address this, we propose a novel hypergraph-based cross-modal retrieval approach, which explicitly establishes many-to-many associations between multiple samples using a label-driven hypergraph construction mechanism, combined with differentiated hyperedge weighting. Additionally, to avoid the limitation of information interaction direction imposed by traditional unidirectional cross-attention mechanisms, we design a bidirectional cross-attention structure, with image and text as separate query sources, to achieve symmetric semantic enhancement between modalities. The resulting joint image-text representations are then mapped as hypergraph vertices, further enhancing the model's ability to align cross-modal semantics. Since constructing a global hypergraph on a large-scale sample set would incur high computational cost, we introduce global label co-occurrence frequency to supervise the batch-level hypergraph construction, enhancing the local graph's ability to capture global semantics. Experimental results show that our model outperforms existing state-of-the-art methods on three benchmark cross-modal retrieval datasets.
The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The quality of generated content heavily relies on the usefulness of the retrieved information and the capacity of LLMs' internal information processing mechanism to incorporate it in answer generation. It is generally assumed that the retrieved information is relevant to the question. However, the retrieved information may have a variable degree of relevance and usefulness, depending on the question and the document collection. It is important to take into account the relevance of the retrieved information in answer generation. In this paper, we propose OpenDecoder, a new approach that leverages explicit evaluation of the retrieved information as quality indicator features for generation. We aim to build a RAG model that is more robust to varying levels of noisy context. Three types of explicit evaluation information are considered: relevance score, ranking score, and QPP (query performance prediction) score. The experimental results on five benchmark datasets demonstrate the effectiveness and better robustness of OpenDecoder by outperforming various baseline methods. Importantly, this paradigm is flexible to be integrated with the post-training of LLMs for any purposes and incorporated with any type of external indicators.
Retrieval-augmented generation (RAG) has become a key paradigm for knowledge-intensive question answering. However, existing multi-hop RAG systems remain inefficient, as they alternate between retrieval and reasoning at each step, resulting in repeated LLM calls, high token consumption, and unstable entity grounding across hops. We propose CompactRAG, a simple yet effective framework that decouples offline corpus restructuring from online reasoning. In the offline stage, an LLM reads the corpus once and converts it into an atomic QA knowledge base, which represents knowledge as minimal, fine-grained question--answer pairs. In the online stage, complex queries are decomposed and carefully rewritten to preserve entity consistency, and are resolved through dense retrieval followed by RoBERTa-based answer extraction. Notably, during inference, the LLM is invoked only twice in total—once for sub-question decomposition and once for final answer synthesis—regardless of the number of reasoning hops. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that CompactRAG achieves competitive accuracy while substantially reducing token consumption compared to iterative RAG baselines, highlighting a cost-efficient and practical approach to multi-hop reasoning over large knowledge corpora. The implementation is available at https://github.com/How-Young-X/CompactRAG.
Domain-adaptive hashing enhances discriminative hash representations by transferring knowledge from a label-rich source domain to a label-scarce target domain. It has attracted significant attention due to its ability to enable efficient cross-domain retrieval without requiring target domain labels. However, existing methods generally assume that source domain labels are completely accurate. In practice, labels obtained via web crawling or crowdsourcing often contain varying degrees of noise, which hampers semantic alignment and aggravates domain shift. To tackle these issues, we propose a novel method termed Evidential Learning and Progressive Alignment (ELPA) for domain-adaptive hashing. This method comprises two key modules: the Uncertainty-aware Noise Separation (UNS) and the Progressive Cross-domain Alignment (PCA). In the UNS, we exploit the belief and uncertainty masses obtained from the evidential learning model and utilize the posterior probabilities of a Gaussian Mixture Model to effectively distinguish clean samples from noisy ones. In PCA, we introduce a progressive partial optimal transport mechanism that prioritizes pseudo-label generation for well-aligned target samples, thereby gradually achieving class-level and global-level cross-domain alignment. Extensive experiments across multiple benchmark datasets with various noise ratios demonstrate that ELPA consistently surpasses existing state-of-the-art methods, exhibiting superior robustness and generalization capability.
Large reasoning models have demonstrated strong problem-solving abilities, yet real-world tasks often require external tools and long-horizon interactions. Existing agent frameworks typically follow predefined workflows, which limit autonomous and global task completion. In this paper, we introduce DeepAgent, an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution within a single, coherent reasoning process. To manage long-horizon interactions, we introduce an autonomous memory folding mechanism that compresses past interactions into structured episodic, working, and tool memories, reducing error accumulation while preserving critical information. To teach general-purpose tool use efficiently and stably, we develop an end-to-end reinforcement learning strategy, namely ToolPO, that leverages LLM-simulated APIs and applies tool-call advantage attribution to assign fine-grained credit to the tool invocation tokens. Extensive experiments on eight benchmarks, including general tool-use tasks (ToolBench, API-Bank, TMDB, Spotify, ToolHop) and downstream applications (ALFWorld, WebShop, GAIA, HLE), demonstrate that DeepAgent consistently outperforms baselines across both labeled-tool and open-set tool retrieval scenarios. The code and demo are available at https://github.com/RUC-NLPIR/DeepAgent.
The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowledge base. Moreover, domain documents are characterized by dense terminology and strong contextual dependencies, which exacerbate the semantic fragmentation of text chunks, thereby making it difficult to efficiently utilize their key information. To address these challenges, this paper proposes QChunker, which restructures the RAG paradigm from retrieval-augmentation to understanding-retrieval-augmentation. Firstly, QChunker models the text chunking as a composite task of text segmentation and knowledge completion to ensure the logical coherence and integrity of text chunks. Drawing inspiration from Hal Gregersen's ''Questions Are the Answer'' theory, we design a multi-agent debate framework comprising four specialized components: a question outline generator, text segmenter, integrity reviewer, and knowledge completer. This framework operates on the principle that questions serve as catalysts for profound insights. Through this pipeline, we successfully construct a high-quality dataset of 45K entries and transfer this capability to small language models. Additionally, to handle long evaluation chains and low efficiency in existing chunking evaluation methods, which overly rely on downstream QA tasks, we introduce a novel direct evaluation metric, ChunkScore. Both theoretical and experimental validations demonstrate that ChunkScore can directly and efficiently discriminate the quality of text chunks. Furthermore, during the text segmentation phase, we utilize document outlines for multi-path sampling to generate multiple candidate chunks and select the optimal solution employing ChunkScore. Extensive experimental results across four heterogeneous domains exhibit that QChunker effectively resolves aforementioned issues by providing RAG with more logically coherent and information-rich text chunks. Notably, this study also establishes a small-domain QA dataset concerning hazardous chemical safety, which fully reveals the significant value of RAG in specialized domains and the generalization capability of the QChunker framework.
Owing to their unprecedented comprehension capabilities, large language models (LLMs) have become indispensable components of modern web search engines. From a technical perspective, this integration represents retrieval-augmented generation (RAG), which enhances LLMs by grounding them in external knowledge base. A prevalent technical approach in this context is graph-based RAG (G-RAG). However, current G-RAG methodologies frequently underutilize graph topology, predominantly focusing on low-order structures or pre-computed static communities. This limitation affects their effectiveness in addressing dynamic and complex queries. Thus, we propose DA-RAG, which leverages attributed community search (ACS) to dynamically extract relevant subgraphs based on the queried question. DA-RAG captures high-order graph structures, allowing for the retrieval of self-complementary knowledge. Furthermore, DA-RAG is equipped with a chunk-layer oriented graph index, which facilitates efficient multi-granularity retrieval while significantly reducing both computational and economic costs. We evaluate DA-RAG on multiple datasets, demonstrating that it outperforms existing RAG methods by up to 40% in head-to-head comparisons across four metrics while reducing index construction time and token overhead by up to 37% and 41%, respectively.
Proximity graphs are the state-of-the-art solution for approximate nearest neighbor (ANN) search, supporting applications such as Web search and retrieval-augmented generation (RAG). Sustaining long-term performance requires adaptive pruning as data and query workloads evolve. However, existing approaches are largely static and uniform. Adaptive pruning faces three key challenges: temporal drift in data and query distributions, spatial heterogeneity across graph regions, and costly feedback due to graph-level evaluations. We present BANCO, a bandit-based framework for adaptive proximity graph pruning. BANCO unifies diverse pruning strategies within a common decision space and optimizes them via a drift-aware batched bandit algorithm. It addresses temporal drift through drift-aware updates, captures spatial heterogeneity using contextual features for region-specific pruning, and reduces evaluation costs through batched feedback aggregation. We establish a dynamic regret bound with sublinear loss and polynomial computational complexity. Extensive experiments on four real-world datasets demonstrate that BANCO helps maintain long-term ANN search efficiency and accuracy under evolving data and workloads.
Large Language Models (LLMs) have demonstrated strong capabilities in web search and reasoning. However, their dependence on static training corpora makes them prone to factual errors and knowledge gaps. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external knowledge sources, especially structured Knowledge Graphs (KGs), which provide explicit semantics and efficient retrieval. Existing KG-based RAG approaches, however, generally assume that anchor entities are accessible to initiate graph traversal, which limits their robustness in open-world settings where accurate linking between the user query and the KG entity is unreliable. To overcome this limitation, we propose AnchorRAG, a novel multi-agent collaboration framework for open-world RAG without the predefined anchor entities. Specifically, a predictor agent dynamically identifies candidate anchor entities by aligning user query terms with KG nodes and initializes independent retriever agents to conduct parallel multi-hop explorations from each candidate. Then a supervisor agent formulates the iterative retrieval strategy for these retriever agents and synthesizes the resulting knowledge paths to generate the final answer. This multi-agent collaboration framework improves retrieval robustness and mitigates the impact of ambiguous or erroneous anchors. Extensive experiments on four public benchmarks demonstrate that AnchorRAG significantly outperforms existing baselines and establishes new state-of-the-art results on the real-world reasoning tasks.