Vision-Language Models (VLMs) have demonstrated remarkable performance in document understanding tasks; however, VLMs also suffer from hallucinations inherited from LLMs. While prior work has focused on reasoning-stage hallucinations, the role of visual perception remains underexplored. In this work, we define perceptual hallucination as the phenomenon where VLMs generate information as if perceived, despite absent or damaged visual evidence. To analyze this, we construct DocHallu, a benchmark of 2,671 original–damaged image pairs across three tasks, available at https://huggingface.co/datasets/IB99/DocHallu. Experiments reveal that perceptual hallucination occurs across all models, with higher rates for numerical content than textual content. Activation patching analysis suggests that hallucinations are strongly associated with errors introduced in the vision encoder, which can subsequently propagate and become amplified through the text decoding process. We also demonstrate that LLM-based post-hoc filtering can reduce hallucination exposure by 36% on average, with reductions of up to 88%. This work extends VLM hallucination research by defining, analyzing, and verifying perceptual hallucination in document understanding.
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Recently, embedding resources, including models, benchmarks, and datasets, have been widely released to support a variety of languages. However, the Dutch language remains underrepresented, typically comprising only a small fraction of the published multilingual resources. To address this gap and encourage the further development of Dutch embeddings, we introduce new resources for their evaluation and generation. First, we introduce the Massive Text Embedding Benchmark for Dutch (MTEB-NL), which includes both existing Dutch datasets and newly created ones, covering a wide range of tasks. Second, we provide a training dataset compiled from available Dutch retrieval datasets, complemented with synthetic data generated by large language models to expand task coverage beyond retrieval. Finally, we release a series of E5-NL compact yet efficient embedding models that demonstrate strong performance across multiple tasks. We make our resources publicly available through the Hugging Face Hub and the MTEB package.
Adversarial attacks have attracted growing attention across domains, including natural language processing (NLP). Character-level adversarial attacks preserve semantics, but they have received less attention because the discrete operations they use are costly and inefficient. Challenging these beliefs, we introduce two adaptively learnable matrices that transform discrete choices into continuous representations, enabling automatic one-shot multi-position, multi-character insertion. To optimize the two learnable matrices, we propose OSCR-Attack, an end-to-end framework based on gradient-based optimization, with a conflict resolution strategy that maps the optimized continuous distributions back into discrete insertion operations. Extensive experiments on three benchmarks with three open-source large language models (LLMs) show that OSCR-Attack improves attack success rate (ASR) by up to 21.45% points and accelerates the attack by up to 3.66 times compared to recent baselines.
Masked diffusion language models (MDLMs) enable efficient parallel decoding but are limited by a monotonic unmasking policy, where committed tokens cannot be revised. While remasking-based methods mitigate early errors, they mainly intervene during generation. In this work, we study post-hoc refinement of a completed draft and find that naive correction often fails because of contextual lock-in, a phenomenon in which local error patterns become self-reinforcing. To address this, we propose PURE (Post-hoc Unlocking and REfinement), a training-free inference algorithm for two-phase decoding. PURE profiles confidence dynamics during drafting to identify unstable regions via an instability score (\Delta_i), then unlocks them through deterministic window masking and stochastic leftward relaxation. On reasoning benchmarks, PURE substantially improves accuracy when applied to LLaDA-8B-Instruct, including a gain of +12.9 points over the baseline on GSM8K. These gains require only a small refinement budget, yielding a favorable compute-quality trade-off for discrete diffusion decoding.
EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
PDF ↗Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recent lightweight approaches reduce indexing costs by leveraging Named Entity Recognition (NER), they rely strictly on structural co-occurrence, failing to capture latent semantic connections between disjoint entities. To address this, we propose EHRAG, a lightweight RAG framework that constructs a hypergraph capturing both structure and semantic level relationships, employing a hybrid structural-semantic retrieval mechanism. Specifically, EHRAG constructs structural hyperedges based on sentence-level co-occurrence with lightweight entity extraction and semantic hyperedges by clustering entity text embeddings, ensuring the hypergraph encompasses both structural and semantic information. For retrieval, EHRAG performs a structure-semantic hybrid diffusion with topic-aware scoring and personalized pagerank (PPR) refinement to identify the top-k relevant documents. Experiments on four datasets show that EHRAG outperforms state-of-the-art baselines while maintaining linear indexing complexity and zero token consumption for construction. Code is available at https://github.com/yfsong00/EHRAG.
From “Thinking” to “Justifying”: Aligning High-Stakes Explainability with Professional Communication Standards
PDF ↗Explainable AI (XAI) in high-stakes domains should help stakeholders trust and verify system outputs. Yet Chain-of-Thought methods reason before concluding, and logical gaps or hallucinations can yield conclusions that do not reliably align with their rationale. Thus, we propose “Result → Justify”, which constrains the output communication to present a conclusion before its structured justification. We introduce SEF (Structured Explainability Framework), operationalizing professional conventions (e.g., CREAC, BLUF) via six metrics for structure and grounding. Experiments across four tasks in three domains validate this approach: all six metrics correlate with correctness (r=0.20–0.42; p<0.001), and SEF achieves 83.9% accuracy (+5.3 over CoT). These results suggest structured justification can improve verifiability and may also improve reliability. Code is available at https://github.com/cqian03/SEF.
Large Reasoning Models (LRMs) often suffer from overthinking, a phenomenon in which redundant reasoning steps are generated after a correct solution has already been reached. Existing early reasoning exit methods primarily rely on output-level heuristics or trained probing models to skip redundant reasoning steps, thereby mitigating overthinking. However, these approaches typically require additional rollout computation or externally labeled datasets. In this paper, we propose NEAT, a Neuron-based Early reAsoning exiT framework that monitors neuron-level activation dynamics to enable training-free early exits, without introducing any additional test-time computation. NEAT identifies exit-associated neurons and tracks their activation patterns during reasoning to dynamically trigger early exit or suppress reflection, thereby reducing unnecessary reasoning while preserving solution quality. Experiments on four reasoning benchmarks across six models with different scales and architectures show that, for each model, NEAT achieves an average token reduction of 22% to 28% when averaged over the four benchmarks, while maintaining accuracy.
EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks
PDF ↗Autonomous GUI agents are inherently vulnerable to Environmental Injection Attacks (EIAs). However, existing red-teaming methods face a trade-off between requiring target-specific knowledge and incurring prohibitive computational costs. More fundamentally, a key question remains: \textit{what factors determine attack success?} To answer this, we first analyze two dimensions: visual appearance (e.g., position, size, color) and semantic content. We find that semantic content dominates, while visual variations have negligible impact. Leveraging this insight, we introduce EVA, a framework that evolves payloads exclusively on the semantic dimension via a discovery-deployment pipeline. Experiments demonstrate that EVA significantly outperforms baselines, achieving 59% to 85% average Attack Success Rate (ASR) while evolving benign seeds into successful attacks within 1.18 to 1.71 iterations. This rapid convergence suggests a dense semantic attack space within the model’s latent space. Whenever an input falls into this space, the agent becomes inherently vulnerable, exposing a fundamental alignment flaw in current multimodal representations.
While Large Language Models (LLMs) have demonstrated significant potential in Tool-Integrated Reasoning (TIR), existing training paradigms face significant limitations: Zero-RL suffers from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. To address these challenges, we propose E3-TIR (Enhanced Experience Exploitation), a warm-up paradigm for the early stages of agent training. Specifically, we formulate training as the dynamic integration of three experience types: Expert Prefixes, Expert Guided, and Self-Exploration. By executing diverse branching exploration around expert “anchors’’ and employing a mix policy optimization mechanism, we effectively mitigate distribution shifts and resolve optimization conflicts arising from shared prefixes. Our method dynamically adapts the model’s knowledge boundaries, effectively balancing exploration diversity with training efficiency. Experimental results demonstrate that E3-TIR achieves a 6% performance improvement over traditional paradigms on tool-use tasks, while requiring less than 10% of the synthetic data. Furthermore, in terms of ROI—a comprehensive metric integrating performance, data cost, and training efficiency—we achieve a 1.46 gain compared to baselines.
Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowledge reasoning across diverse fields. However, despite significant progress, current scientific reasoning models still struggle with generalization across domains and often fall short of multimodal perception. Multimodal Large Language Models (MLLMs), which integrate text, images, and other modalities, present an exciting opportunity to overcome these limitations and enhance scientific reasoning. Therefore, **this position paper argues that MLLMs can significantly advance scientific reasoning across disciplines such as mathematics, physics, chemistry, and biology**. We highlight the current state of MLLM applications in scientific reasoning, noting their ability to integrate and reason over diverse data types. However, challenges such as multimodal alignment, data diversity, and reasoning depth remain obstacles to achieving their full potential. To address these challenges, we propose actionable suggestions in the near future. Overall, our work offers a novel perspective on MLLM integration with scientific reasoning, providing the LLM community with valuable insights for achieving Artificial General Intelligence (AGI).
Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection
PDF ↗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
Bias in Large Language Models (LLMs) poses significant risks to trustworthiness, manifesting primarily as stereotypical biases (e.g., gender or racial stereotypes) and structural biases (e.g., lexical overlap or position preferences). However, prior paradigms typically address these in isolation, often mitigating one at the expense of exacerbating the other. To address this, we conduct a systematic exploration of these reasoning failures and identify a primary inducement: the latent spurious feature correlations within the input that drive these erroneous reasoning shortcuts. Driven by these findings, we introduce Causal-Contrastive Preference Optimization (C2PO), a unified alignment framework designed to tackle these specific failures by simultaneously discovering and suppressing these correlations directly within the optimization process. Specifically, C2PO leverages causal counterfactual signals to isolate bias-inducing features from valid reasoning paths, and employs a fairness-sensitive preference update mechanism to dynamically evaluate logit-level contributions and suppress shortcut features. Extensive experiments across multiple benchmarks covering stereotypical bias (BBQ, Unqover), structural bias (MNLI, HANS, Chatbot, MT-Bench), out-of-domain fairness (StereoSet, WinoBias), and general utility (MMLU, GSM8K) demonstrate that C2PO effectively mitigates stereotypical and structural biases while preserving robust general reasoning capabilities.
MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis
PDF ↗Multimodal Sentiment Analysis aims to integrate information from various modalities to make complementary predictions. However, it often struggles with irrelevant or misleading visual and auditory information. Most existing approaches treat entire modality as an independent unit for feature enhancement or denoising, which often suppresses redundant noise at the cost of weakening critical information. To address this challenge, we propose MoLAN, a unified ModaLity-aware noise dynAmic editiNg framework. Specifically, MoLAN performs modality-aware block partitioning by dividing the features of each modality into multiple blocks. Each block is then dynamically assigned a distinct denoising strength based on its noise level and semantic relevance, enabling fine-grained noise suppression while preserving essential multimodal information. Notably, MoLAN is a unified and flexible framework that can be seamlessly integrated into a wide range of multimodal models. Building upon this framework, we further introduce MoLAN+, a new multimodal sentiment analysis approach. Experiments across five models and four datasets demonstrate the broad effectiveness of the MoLAN framework. Extensive evaluations show that MoLAN+ achieves the state-of-the-art performance.
CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations
PDF ↗LLM-empowered agent simulations are increasingly used to study social emergence, yet the micro-to-macro causal mechanisms behind macro outcomes often remain unclear. This is challenging because emergence arises from intertwined agent interactions and meso-level feedback and nonlinearity, making generative mechanisms hard to disentangle. To this end, we introduce CAMO, an automated Causal discovery framework from Micro behaviors to Macro Emergence in LLM agent simulations. CAMO converts mechanistic hypotheses into computable factors grounded in simulation records and learns a compact causal representation centered on an emergent target . CAMO outputs a computable Markov boundary and a minimal upstream explanatory subgraph, yielding interpretable causal chains and actionable intervention levers. It also uses simulator-internal counterfactual probing to orient ambiguous edges and revise hypotheses when evidence contradicts the current view. Experiments across four emergent settings demonstrate the promise of CAMO.[The code is available at an anonymous link: <https://anonymous.4open.science/r/CAMO-0E6C/>.]
Neural machine translation for extremely low-resource languages faces compounding challenges: scarce parallel data, orthographic inconsistency, and absence of quality metadata for principled training. We present Kumatigi, a quality-annotated French-Bambara corpus combining systematic curation with data augmentation strategies tailored to Bambara. We provide 67k quality-scored pairs that enable targeted data filtering and address pervasive orthographic normalization issues in existing resources. Our dual-dataset generation framework strategically exploits round-trip translation, producing synthetic pairs for fluency reinforcement alongside back-translated pairs that preserve authentic vocabulary for coverage expansion. We further introduce linguistically-motivated augmentation techniques addressing Bambara’s orthographic variability, improving model robustness for real-world text. Experiments with LoRA-based fine-tuning demonstrate consistent improvements across automatic metrics, with our full system achieving up to +3–4 BLEU over strong baselines. Data generation and augmentation strategies contribute +1-2 BLEU beyond high-quality parallel data alone. Human evaluation by native speakers confirms these automatic improvements align with substantial gains in translation adequacy and fluency, with our best model approaching human reference translation quality. Our methodology provides a reproducible framework applicable to other under-resourced languages facing similar data challenges.
As Multimodal Large Language Models (MLLMs) mature, GUI agents are evolving from static interactions to complex navigation. While Reinforcement Learning (RL) has emerged as a promising paradigm for training MLLM agents on dynamic GUI tasks, its effective application faces a dilemma.Standard Offline RL often relies on static step-level data, neglecting global trajectory semantics such as task completion and execution quality. Conversely, Online RL captures the long-term dynamics but suffers from high interaction costs and potential environmental instability. To bridge this gap, we propose SOLAR-RL (Semi Online Long-horizon RL). Instead of relying solely on expensive online interactions, our framework integrates global trajectory insights directly into the offline learning process. Specifically, we reconstruct diverse rollout candidates from static data, detect the first failure point using per-step validity signals, and retroactively assign dense step-level rewards with target-aligned shaping to reflect trajectory-level execution quality—effectively simulating online feedback without interaction costs.Extensive experiments demonstrate that SOLAR-RL significantly improves long-horizon task completion rates and robustness compared to strong baselines, offering a sample-efficient solution for autonomous GUI navigation.
SParK-Eval: Evaluating Structure-Aware Knowledge Acquisition in LLMs for Domain Adaptation to Industrial Records
PDF ↗Large Language Models (LLMs) often underperform in domain adaptation for industrial settings, where available corpora are limited and structurally diverse. These corpora frequently include non-natural formats such as tables, entity lists, or bullet-point instructions that hinder effective learning. To understand and improve domain-adaptive pretraining on such data, we introduce SParK-Eval (Structure-aware Parametric Knowledge Evaluation), a framework that constructs question–answer pairs from pretraining data and annotates each with its input structure (e.g., natural sentence, table, list). This enables fine-grained analysis of how input structure affects parametric knowledge acquisition during DAPT. Additionally, we propose a prompt-based input normalization method that converts diverse inputs into coherent natural sentences, providing a reference for isolating structural effects. Our experiments show that LLMs acquire substantially more knowledge from natural sentences than from their structurally non-standard counterparts. These findings underscore the importance of structure-aware evaluation in diagnosing learning challenges and guiding effective domain adaptation strategies.
The indistinguishability of large language model (LLM) output from human-authored content poses significant challenges, raising concerns about potential misuse of AI-generated text and its influence on future model training. Watermarking algorithms offer a viable solution by embedding detectable signatures into generated text. However, existing watermarking methods often involve trade-offs among attack robustness, generation quality, and additional overhead such as specialized frameworks or complex integrations. We propose a lightweight, topic-guided watermarking scheme for LLMs that partitions the vocabulary into topic-aligned token subsets. Given an input prompt, the scheme selects a relevant topic-specific token list, effectively "green-listing" semantically aligned tokens to embed robust marks while preserving fluency and coherence. Experimental results across multiple LLMs and state-of-the-art benchmarks demonstrate that our method achieves text quality comparable to industry-leading systems and simultaneously improves watermark robustness against paraphrasing and lexical perturbation attacks, with minimal performance overhead. Our approach avoids reliance on additional mechanisms beyond standard text generation pipelines, enabling straightforward adoption and suggesting a practical path toward globally consistent watermarking of AI-generated content.
Large language models (LLMs) have achieved striking successes on many benchmarks, yet recent studies continue to expose fundamental weaknesses. In this paper, we introduce Concept, a simple word-guessing board game, as a benchmark for probing abductive reasoning. Our results show that this game, easily solved by humans (with a success rate of over 90%), is still very challenging for state-of-the-art LLMs (no model exceeds 40% success rate). Specifically, we observe that LLMs struggle with interpreting other players’ strategic intents, and with correcting initial hypotheses given sequential information updates. In addition, we extend the evaluation across multiple languages, and find that the LLM performance drops further in lower-resource languages (Dutch, French, and Spanish) compared to English.
Disentangling Continued Pre-Training: Attention-Driven Routing and Semantic Hub Preservation in Language Adaptation
PDF ↗Continued Pre-Training (CPT) enables Large Language Models (LLMs) to acquire second-language capabilities, yet the underlying mechanisms remain poorly understood. In this work, we investigate how CPT adapts model representations across diverse language families and scripts, model sizes, and architectures. We find that second-language abilities emerge through a selective adaptation mechanism: task-solving capabilities are preserved in “semantic hub”, while interface layers retarget to shifted token distributions. Layer-swapping experiments demonstrate that semantic understanding can be surgically transferred between base and CPT models with minimal loss (e.g., swapping 50% of model parameters reduces performance by only 0.3%). Furthermore, we establish that attention components route language adaptation: larger parameter changes than feedforward networks, correlate more strongly with language-specific neurons, and their surgical replacement substantially degrades performance. Overall, our work provides a mechanistic understanding of CPT, guiding future work on efficient strategies for language adaptation.