论文检索

Dongyang Zhen, Niping Duan, Huan Zhou, Qingbin Cui

GPS trajectories encode rich behavioral information about how people move, organize activities, and form daily routines. Recent advances in large language models (LLMs) raise a natural question: can such models infer and summarize travel behavior directly from mobility traces? This paper introduces TravelBehaviorQA, a large-scale benchmark dataset that reframes trajectory analysis as a language-based behavioral understanding task. The dataset links raw GPS trajectories with human-grounded question-answering (QA) pairs that capture travel intensity, temporal structure, activity patterns, mode usage, and behavioral routines. Unlike prior mobility datasets focused on prediction or classification, TravelBehaviorQA emphasizes semantic interpretation through a unified mix of deterministic and open-ended questions. In this benchmark, we construct over 143k QA instances spanning users and years, and evaluate a broad range of state-of-the-art LLMs under controlled settings. Our results reveal substantial gaps between factual extraction and genuine behavioral reasoning, showing that model scale alone is insufficient and that trajectory representation is a primary bottleneck. TravelBehaviorQA exposes critical limitations of current models and establishes a rigorous benchmark for advancing language-based understanding of human mobility behavior.

Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin

Current multimodal fake news detectors predominantly function as opaque classifiers, offering limited deductive transparency and little insight into how conflicting evidence is reconciled. To address this limitation, we propose Dialectical Structured Reasoning (DSR), a framework modeling fake news detection as an explicit dialectical process over multimodal social context. DSR instantiates two opposing agents: a Verifier, which constructs evidence paths supporting semantic consistency, and a Debunker, which actively explores exposing logical or factual contradictions. Then a differentiable Judge agent adjudicates between these competing perspectives by integrating local evidence with global parametric knowledge. Experiments on three benchmarks demonstrate that DSR achieves state-of-the-art performance while producing transparent, dialectically grounded explanations that closely mirror human reasoning process.

Shiyu Tian, Shuyue Xing, Zhuoxin Han, Caixia Yuan, Xiaojie Wang

Integrating knowledge graphs (KGs) with large language models (LLMs) enhances factual accuracy and interpretability in question answering. However, existing agent-based methods rely on static memory mechanisms that fail to address the combinatorial explosion of search spaces in multi-hop reasoning and lack continuous learning capabilities. To overcome these limitations, we propose EvoMemKG, an agent framework with a dynamic, evolvable memory mechanism specifically designed for KG reasoning. EvoMemKG features a dual-layer memory architecture: (1) a working memory that losslessly compresses retrieved triplets through clustering to manage exploration states, effectively linearizing the exponential state space expansion; and (2) an experience memory that abstracts historical reasoning paths into reusable, generalized strategies, enabling cross-task knowledge transfer and self-evolution. We further introduce a double-loop workflow that orchestrates the LLM, memory layers, and KG environment to enable end-to-end autonomous reasoning. Extensive evaluations on three KGQA datasets across two KGs demonstrate that EvoMemKG achieves state-of-the-art performance without requiring additional training or specialized tools. Notably, it achieves improvements of up to 20% over the strong baseline on complex multi-hop queries, validating the effectiveness of our dynamic memory approach.

Zhixiao Qi, Feng Huang, Yunqi Zhang, Shijie Zhang, Qingqing Sun, Yongfeng Huang, Minghu Jiang, Shuai Chen, Tianyi Zhang

Large language models (LLMs) often hallucinate in question answering (QA) tasks due to a lack of factual knowledge. While integrating knowledge graphs (KGs) with LLMs has alleviated this issue, existing methods suffer from poor generalization or low reasoning efficiency, and critically, they overlook the learning and reuse of reasoning paths from past experiences. To address these challenges, we introduce Thought-Action Graph (TAG), a structured repository of reasoning experiences. TAG decomposes successful LLM-KG interaction trajectories into fine-grained semantic operators, which are stored in TAG constructed by the thought layer and action layer. Building upon TAG, we propose a novel KGQA paradigm \textemdash TAG-Reasoning (TAGR). TAGR first retrieves and assembles reasoning blueprints from TAG, and then guides LLM to efficiently execute on KG according to them. Through this approach, TAGR transforms the computationally expensive online exploration process of LLMs into an offline process of TAG retrieval and assembly. Experimental results on multiple KGQA benchmarks demonstrate that TAGR significantly outperforms state-of-the-art methods across key metrics, while drastically reducing the number of LLM calls and generated tokens. This work opens new avenues for building continual learning, efficient, and faithful KGQA systems.

Qi Cao, Takeshi Kojima, Andrew Gambardella, Helinyi Peng, Yutaka Matsuo, Yusuke Iwasawa

Large language models (LLMs) demonstrate remarkable performance across diverse tasks, but they often generate responses that appear plausible while being factually incorrect. This problem is compounded by the lack of explicit uncertainty estimates, which makes it difficult for users to judge the reliability of model outputs. Existing uncertainty quantification methods typically rely on indirect signals, such as entropy across sampled generations. These signals can be difficult to interpret and do not fully leverage the model’s ability to assess its own uncertainty. We propose a simple and effective self-assessment method for uncertainty quantification in LLMs. Our approach groups sampled generations into semantically distinct clusters, converts them into answer options in a structured multiple-choice question, and uses the probability assigned by the LLM to each option as a confidence estimate. Experiments across multiple models and datasets show that our method consistently outperforms baseline approaches. Notably, it achieves competitive performance with as few as two additional samples, demonstrating both its effectiveness and efficiency.

Shlok Shelat, Jay Raval, Souvik Roy, Manas Gaur

Large language models (LLMs) have demonstrated strong performance on formal language tasks, yet whether this reflects genuine symbolic reasoning or pattern matching on familiar constructions remains unclear. We introduce a benchmark for deterministic finite automata (DFA) construction from regular languages, comprising factual knowledge questions, seen construction problems from public sources, and two types of unseen problems: hand-crafted instances with multiple interacting constraints and systematically generated problems via Arden’s theorem. Models achieve perfect accuracy on factual questions and 84-90% on seen tasks. However, accuracy drops sharply on unseen problems (by 30-64%), with failures stemming from systematic misinterpretation of language constraints, incorrect handling of Kleene-star semantics, and a failure to preserve global consistency. We evaluate a three-stage hint protocol that enables correction of shallow errors but does not reliably resolve globally inconsistent or structurally flawed automata. Our analysis across multiple prompting strategies (direct, Chain-of-Thought, Tree-of-Thought) reveals that errors persist regardless of prompting approach, exposing a fundamental gap between LLMs’ ability to generate syntactically plausible DFAs and their capacity for semantically correct formal reasoning.

Zhenqin Li, ShengYong Ding, Shuangyin Li

Question-Answer Generation (QAG) is essential for alleviating the cold-start problem in domain-specific large language model (LLM) post-training, where high-quality data is severely scarce.Effective training samples include rich semantic diversity and rigorous factual consistency.Thus, it is necessary to consider the inherent tension between semantic breadth and factual fidelity.However, it is extremely challenging to trade off semantic diversity against factual consistency, in that generalization across the semantic space must be achieved effectively and reliably, and factual integrity must be ensured as well.To address this issue, we propose an effective framework, namely DiFRa, that integrates continuous concept diffusion with discrete knowledge graph constraints to balance semantic diversity and factual consistency.Specifically, the proposed DiFRa models discrete concepts as a continuous latent distribution to sample embeddings that capture rich semantic variations, and constructs a refined knowledge graph as explicit factual constraints.Then, a diversity and consistency aware mechanism is designed to dynamically integrate both embeddings and the knowledge graph for QA pairs generation.Furthermore, we introduce SeFa, which harmonizes semantic entropy and consistency scores to quantify the trade-off between diversity and correctness.Extensive experiments demonstrate that DiFRa consistently outperforms the baseline models, validating its efficacy in reconciling the tension to generate semantically diverse and factually consistent QA pairs. The source code is publicly available.

Runsong Jia, Zhen Fang, Mengjia Wu, Jie Lu, Yi Zhang

Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detection methods mainly depend on internal signals like uncertainty and self-consistency checks, using the model’s pre-trained knowledge to identify unreliable outputs. However, pre-trained knowledge may become outdated and has coverage limitations, especially for specialized or recent information. To address these limitations, retrieval-augmented generation (RAG) has emerged as a promising solution by retrieving relevant evidence at inference time, grounding outputs beyond the model’s parametric knowledge. In this paper, we target a critical and practical learning problem RAG-based hallucination detection (RHD), where RAG is employed to enhance hallucination detection by addressing information updating challenges. To address RHD, we propose a novel method Evidence-Aligned Entity Verification (EAEV), which detects entity-level hallucinations by leveraging RAG to align generated entities with retrieved evidence contexts. Specifically, EAEV evaluates entity-evidence alignment through three complementary dimensions and introduces counterfactual stability analysis to ensure robust alignments under evidence perturbations. Experiments across multiple RAG benchmarks demonstrate that EAEV achieves consistent improvements over existing methods with strong generalization capabilities.

Manit Baser, Alperen Yildiz, Dinil Mon Divakaran, Mohan Gurusamy

The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by modifying a model’s factual associations, they often produce unpredictable ripple effects, which are unintended behavioral changes that propagate even to the hidden space. In this work, we introduce CLaRE, a lightweight representation-level technique to identify where these ripple effects may occur. Unlike prior gradient-based methods, CLaRE quantifies entanglement between facts using forward activations from a single intermediate layer, avoiding costly backward passes. To enable systematic study, we prepare and analyse a corpus of 11,427 facts drawn from three existing datasets. Using CLaRE, we compute large-scale entanglement graphs of this corpus for multiple models, capturing how local edits propagate through representational space. These graphs enable stronger preservation sets for model editing, audit trails, efficient red-teaming, and scalable post-edit evaluation. In comparison to baselines, CLaRE achieves an average of 62.2% improvement in Spearman correlation with ripple effects while being 2.74× faster, and using 2.85× less peak GPU memory. Besides, CLaRE requires only a fraction of the storage needed by the baselines to compute and preserve fact representations. Our entanglement graphs and corpus are available at https://github.com/manitbaser/CLaRE.

Quulgan Minggad, Xiao Zinan, Yuan Sun

While Large Language Models (LLMs) have achieved impressive linguistic fluency in low-resource languages, their capacity to process deep cultural nuances remains insufficiently quantified. This paper introduces MonCulture-Eval, a benchmark designed to assess the cultural intelligence of LLMs in the Mongolian context across two writing systems (Traditional and Cyrillic) and three regional sub-cultures (Alxa, Ordos, and Horqin). Curated entirely from primary, non-digitized archives to prevent data contamination, the benchmark employs a three-layer cognitive hierarchy—Factual, Situational, and Values—supplemented by specialized tasks including Riddles, Taboos, and Proverbs. Evaluation of frontier models reveals a severe "Script Gap" and a systematic "Etic Bias," where models sanitize spiritual rituals into secular functional norms.

Abid Ali, Diego Molla, Usman Naseem

Multimodal Large Language Models (MLLMs) have facilitated Multimodal Summarization with Multimodal Output (MSMO), wherein systems generate concise textual summaries accompanied by salient visuals from multimodal sources. However, current MSMO evaluation remains fragmented: text quality, image-text alignment, and visual diversity are typically assessed in isolation using unimodal metrics, making it difficult to capture whether the modalities jointly support a faithful and useful summary. To address this gap, we introduce MM-Eval, a unified evaluation framework that integrates assessments of textual quality, cross-modal alignment, and visual diversity. MM-Eval comprises three components: (1) text quality, measured using OpenFActScore for factual consistency and G-Eval for coherence, fluency, and relevance; (2) image-text relevance, evaluated via an MLLM-as-a-judge approach; and (3) image-set diversity, quantified using Truncated CLIP Entropy. We calibrate -Eval through a learned aggregation model trained on the mLLM-EVAL news benchmark, aligning component contributions with human preferences. Our analysis reveals a text-dominant hierarchy in this setting, where factual consistency acts as a critical determinant of perceived overall quality, while visual relevance and diversity provide complementary signals. MM-Eval improves over heuristic aggregation baselines and provides an interpretable, reference-weak framework for comparative evaluation of multimodal summaries.

Mohammad Mahdi Abootorabi, Omid Ghahroodi, Anas Madkoor, Marzia Nouri, Doratossadat Dastgheib, Ehsaneddin Asgari

Despite the rapid progress of Vision-Language Models (VLMs), the field lacks benchmarks that rigorously diagnose their true reasoning abilities and chart meaningful progress toward human-like multimodal intelligence. Most existing evaluations focus on piecemeal or disconnected tasks, obscuring critical cognitive weaknesses and providing little insight for targeted improvement.To address this gap, we introduce BloomBench, part of the Almieyar benchmarking series, the first cognitively human-grounded, bilingual (English–Arabic) multimodal benchmark for VLMs. Grounded in Bloom’s Taxonomy, BloomBench systematically evaluates six levels of cognition (Remember, Understand, Apply, Analyze, Evaluate, Create) through carefully designed image–question–answer tasks. Built with a semi-automated pipeline and validated through a stratified hybrid quality assurance protocol, it ensures scalability, cultural inclusivity, and linguistic fidelity. Leveraging this framework, we conduct a comprehensive study of state-of-the-art VLMs to diagnose their cognitive profiles. Our analysis reveals a sharp cognitive asymmetry: while state-of-the-art models achieve strong performance ceilings in semantic understanding, they struggle substantially with factual recall and creative synthesis. This demonstrates that current general multimodal proficiency masks deeper limitations in specific cognitive layers.Furthermore, our study highlights a critical performance gap between Arabic and English, exposing limitations in current cross-lingual multimodal reasoning. These findings establish a foundation for developing more cognitively aligned and inclusive VLMs.The benchmark framework and dataset is available at: https://github.com/qcri/Almieyar-Oryx-BloomBench.

Hung Pham Van, Nguyen Manh Hieu, Khang Pham Tran Tuan, Nam Le Hai, Linh Ngo Van, Nguyen Thi Ngoc Diep, Trung Le

Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context. We introduce MemORAI (Memory Organization and Retrieval via Adaptive Graph Intelligence), a framework that integrates three innovations: selective memory filtering with dual-layer compression to retain user-persona-relevant content, a provenance-enriched multi-relational graph tracking factual origins at the turn level, and query-adaptive subgraph retrieval with Dynamic Weighted PageRank that applies query-conditioned edge weighting. Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-the-art performance in memory retrieval and personalized response generation, demonstrating that selective storage, enriched representation, and adaptive retrieval are essential for coherent, personalized LLM agents.

Zihan Liu, Jianhui li, Zexin Wang, Fei Sun, Jingjing LI, Zheyuan Li, Ke Xiang, Hang Cui, Houhua Gong, Changhua Pei 等

Evidence-intensive analytical reports are expected to be fact-dense, quantitatively correct, and supported by figures. Yet one-shot long-form generation with large language models (LLMs) frequently produces fluent but under-supported drafts: core facts are missed, numbers drift, and key visuals are absent, making the report hard to trust. We propose EviReport, an evidence-tracked report-writing workflow that improves reliability by (i) organizing corpus evidence into compact, traceable units and retrieves query-relevant subgraphs into retrieval-ready packages (ii) leveraging a reasoning-focused LLM sketches a high-level plan for full coverage, then a chat-based LLM sharpens it into a detailed hierarchical outline with explicit scope and ordering (iii) rive generation with a facts-first iterative loop: extracting verifiable facts, composing strictly from those facts, then triggering gap-aware append queries to fill missing evidence To evaluate both correctness and completeness, we introduce EviReportBench, a benchmark instantiated on data-rich indicator reports that measures factual accuracy (claim verification), factual coverage (quiz-based evaluation), and visual evidence integration (image recall). Across 8 topics, experiments show that EviReport consistently outperforms strong baselines in factual coverage (2.16\times), factual accuracy (+8.9 points), and visual evidence integration (+34 points), approaching the quality of expert-written reports across multiple dimensions.

Jianpeng Hu, Yanzeng Li, Jialun Zhong, Lei Zou, Wenfa Qi

The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress. It can effectively reduce factuality hallucinations, but faithfulness hallucinations still exist. Previous methods for detecting faithfulness hallucinations either neglect to capture the models’ internal reasoning processes or handle those features coarsely, making it difficult for discriminators to learn. This paper proposes a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination. Specifically, we first extend the layer-wise relevance propagation algorithm from the token level to the semantic level, constructing an internal reasoning graph based on attribution vectors. This provides a more faithful semantic-level representation of dependency. Furthermore, we design a general framework based on a small pre-trained language model to utilize the dependencies in LLM’s reasoning for training and hallucination detection, which can dynamically adjust the pass rate of correct samples through a threshold. Experimental results demonstrate that our method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k. Implementation available here: https://anonymous.4open.science/r/SIRG-1022.

Yu Chen, Peng Chen, Ziwei Zheng, Bang Wang

Despite progress in LLM summarization, factual hallucinations persist, motivating Attributed Summary Generation (ASG), which requires sentence-level citations. However, existing prompt-based approaches face severe challenges such as positional preference, poor citation quality and sensitivity to uninformative documents. In view of these limitations, we propose \mathbf{RAAC}, a framework of \mathbf{R}eflective \mathbf{A}gents with \mathbf{A}daptive \mathbf{C}ollaboration for attributed summarization. RAAC performs iterative summarization via reflective agents’ collaboration, where a post reflection module evaluates the consistency between the summary and the input documents, based on which it critiques the summary and uses the resulting feedback to recalibrate the inputs to the next adaptive iteration. The agents’ collaboration involves two components: \mathsf{TextAgent} and \mathsf{CitationAgent}. Experimental results on the ALCE benchmark demonstrate that our framework outperforms existing baselines in both factual correctness and citation quality.

Liting Huang, Zhihao Zhang, Shoujin Wang

While Multimodal Large Language Models (MLLMs) demonstrate strong reasoning capabilities, they lack domain-specific expertise to effectively perform chemical tasks. For example, existing MLLMs struggle with both the lower-level task of molecular structure recognition and the higher-level task of chemical spectral data elucidation. When faced with complex molecular structures and multimodal chemical data (including spectral images and texts), they often fail to provide reliable inference, resulting in poor performance. Moreover, there are no benchmark datasets for evaluating multi-step multimodal reasoning capacities in the chemistry domain. To this end, we establish CheMM-Bench, a comprehensive benchmark dataset with 48,500 reasoning steps across four chemical tasks (SmilesQA, IupacQA, MwQA, SpectraQA) for evaluating visual reasoning in both molecular structure recognition and spectral analysis. On top of this, we present CheMM-R1, a state-of-the-art chemistry-specific MLLM trained with CheMMGRPO, a novel adaptation of Group Relative Policy Optimisation tailored for chemical reasoning. CheMMGRPO employs domain-specific reward functions to assess chemical validity, structural accuracy, format compliance, and factual correctness. CheMM-R1 surpasses leading proprietary models (GPT-o3, Gemini-2.5-Pro, Claude-3.5-Sonnet, and Grok-2) across all CheMM-Bench tasks. The evaluation code and model are publicly available.

Mattia Ottoborgo, Daniele Rege Cambrin, Paolo Garza

Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural coherence, as well as the correct composition of ingredients. Standard training procedures are primarily based on cross-entropy and focus solely on fluency. Building on RECIPE-NLG, we investigate the use of several composite objectives and present a new topological loss that represents ingredient lists as point clouds in embedding space, minimizing the divergence between predicted and gold ingredients. Using both standard NLG metrics and recipe-specific metrics, we find that our loss significantly improves ingredient- and action-level metrics. Meanwhile, the Dice loss excels in time/temperature precision, and the mixed loss yields competitive trade-offs with synergistic gains in quantity and time. A human preference analysis supports our finding, showing our model is preferred in 62% of the cases.

Muhammad Arslan Manzoor, Dilshod Azizov, Daniil Orel, Umer Siddique, Zain Muhammad Mujahid, Yufang Hou, Preslav Nakov

News outlets shape public opinion on a scale, which makes automated detection of political bias and factuality essential. Yet, the field still lacks unified resources, comprehensive evaluations in diverse approaches, and systematic analyzes of the representations and fusion strategies that matter the most, especially under label sparsity and dataset diversity. In addition, there is little empirical work that reports broad observation driven findings about what consistently works, what fails, and why. We address these gaps with four contributions: (i) MBFC-2025, a large-scale label set that covers ~2,600 outlets from Media Bias/Fact Check (MBFC); (ii) multi-view representations for ACL-2020 ~900 outlets and MBFC-2025, spanning Alexa graphs, hyperlink graphs, LLM-derived graphs, articles, and Wikipedia descriptions; (iii) systematic evaluation and analysis of embedding views and fusion strategies, including an RL-based fusion variant; and (iv) extensive experiments that achieve state-of-the-art results on ACL-2020 and establish strong benchmarks on MBFC-2025.

Zhigan Zhang, Jie Sui

The rapid spread of fake news on digital platforms presents significant societal challenges, demanding detection methods capable of addressing intricate manipulation strategies. Existing methods predominantly rely on monolithic verification approaches, which fail to decompose the complex blend of factual inaccuracies, logical fallacies, and propaganda techniques in modern misinformation. To address this gap, we propose PARD, a Protocol-Adaptive Role-Specific multi-agent framework that decomposes verification into factual, logical, and contextual dimensions. PARD dynamically selects the optimal interaction protocol from a library that includes Round-Robin Discussion, Point-Counterpoint Dialogue, and Cross-Examination to tailor the reasoning process for more effective detection. We evaluate PARD with LIAR-RAW+, an extended version of the LIAR-RAW dataset enriched with fine-grained factual, logical, and contextual annotations. Experimental results demonstrate that PARD consistently outperforms baseline methods in both predictive accuracy and explanatory quality, supported by its efficient dynamic governance mechanism