OpenRaiser/Intern-Atlas huggingface dataset:
Graph Integrated Transformers for Community Detection in Social Networks
Venue: arXiv cs.SI 2026
Year: 2026

Community detection is crucial for applications like targeted marketing and recommendation systems. Traditional methods rely on network structure, and embedding-based models integrate semantic information. However, there is a challenge when a model leverages local and global information from complex structures like social networks. Graph Neural Networks (GNNs) and Transformers have shown superior performance in capturing local and global relationships. In this paper, We propose Graph Integrated Transformer for Community Detection (GIT-CD), a hybrid model combining GNNs and Transformer-based attention mechanisms to enhance community detection in social networks. Specifically, the GNN module captures local graph structures, while the Transformer module models long-range dependencies. A self-optimizing clustering module refines community assignments using K-Means, silhouette loss, and KL divergence minimization. Experimental results on benchmark datasets show that GIT-CD outperforms state-of-the-art models, making it a robust approach for detecting meaningful communities in complex social networks.
MiJaBench: Revealing Minority Biases in Large Language Models via Hate Speech Jailbreaking
Venue: arXiv cs.CL 2026
Year: 2026

Current safety evaluations of large language models (LLMs) create a dangerous illusion of universality, aggregating "Identity Hate" into scalar scores that mask systemic vulnerabilities against specific populations. To expose this selective safety, we introduce MiJaBench, a bilingual (English and Portuguese) adversarial benchmark comprising 44,000 prompts across 16 minority groups. By generating 528,000 prompt-response pairs from 12 state-of-the-art LLMs, we curate MiJaBench-Align, revealing that safety alignment is not a generalized semantic capability but a demographic hierarchy: defense rates fluctuate by up to 33\% within the same model solely based on the target group. Crucially, we demonstrate that model scaling exacerbates these disparities, suggesting that current alignment techniques do not create principle of non-discrimination but reinforces memorized refusal boundaries only for specific groups, challenging the current scaling laws of security. We release all datasets and scripts to encourage research into granular demographic alignment at GitHub.
SciFig: Towards Automating Scientific Figure Generation
Venue: arXiv cs.AI 2026
Year: 2026

Creating high-quality figures and visualizations for scientific papers is a time-consuming task that requires both deep domain knowledge and professional design skills. Despite over 2.5 million scientific papers published annually, the figure generation process remains largely manual. We introduce $\textbf{SciFig}$, an end-to-end AI agent system that generates publication-ready pipeline figures directly from research paper texts. SciFig uses a hierarchical layout generation strategy, which parses research descriptions to identify component relationships, groups related elements into functional modules, and generates inter-module connections to establish visual organization. Furthermore, an iterative chain-of-thought (CoT) feedback mechanism progressively improves layouts through multiple rounds of visual analysis and reasoning. We introduce a rubric-based evaluation framework that analyzes 2,219 real scientific figures to extract evaluation rubrics and automatically generates comprehensive evaluation criteria. SciFig demonstrates remarkable performance: achieving 70.1$\%$ overall quality on dataset-level evaluation and 66.2$\%$ on paper-specific evaluation, and consistently high scores across metrics such as visual clarity, structural organization, and scientific accuracy. SciFig figure generation pipeline and our evaluation benchmark will be open-sourced.
Enhanced-FQL($λ$), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay
Venue: arXiv cs.LG 2026
Year: 2026

This paper introduces a fuzzy reinforcement learning framework, Enhanced-FQL($λ$), that integrates novel Fuzzified Eligibility Traces (FET) and Segmented Experience Replay (SER) into fuzzy Q-learning with Fuzzified Bellman Equation (FBE) for continuous control tasks. The proposed approach employs an interpretable fuzzy rule base instead of complex neural architectures, while maintaining competitive performance through two key innovations: a fuzzified Bellman equation with eligibility traces for stable multi-step credit assignment, and a memory-efficient segment-based experience replay mechanism for enhanced sample efficiency. Theoretical analysis proves the proposed method convergence under standard assumptions. Extensive evaluations in continuous control domains demonstrate that Enhanced-FQL($λ$) achieves superior sample efficiency and reduced variance compared to n-step fuzzy TD and fuzzy SARSA($λ$) baselines, while maintaining substantially lower computational complexity than deep RL alternatives such as DDPG. The framework's inherent interpretability, combined with its computational efficiency and theoretical convergence guarantees, makes it particularly suitable for safety-critical applications where transparency and resource constraints are essential.
The Overlooked Role of Graded Relevance Thresholds in Multilingual Dense Retrieval
Venue: arXiv cs.IR 2026
Year: 2026

Dense retrieval models are typically fine-tuned with contrastive learning objectives that require binary relevance judgments, even though relevance is inherently graded. We analyze how graded relevance scores and the threshold used to convert them into binary labels affect multilingual dense retrieval. Using a multilingual dataset with LLM-annotated relevance scores, we examine monolingual, multilingual mixture, and cross-lingual retrieval scenarios. Our findings show that the optimal threshold varies systematically across languages and tasks, often reflecting differences in resource level. A well-chosen threshold can improve effectiveness, reduce the amount of fine-tuning data required, and mitigate annotation noise, whereas a poorly chosen one can degrade performance. We argue that graded relevance is a valuable but underutilized signal for dense retrieval, and that threshold calibration should be treated as a principled component of the fine-tuning pipeline.
Gavel: Agent Meets Checklist for Evaluating LLMs on Long-Context Legal Summarization
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) now support contexts of up to 1M tokens, but their effectiveness on complex long-context tasks remains unclear. In this paper, we study multi-document legal case summarization, where a single case often spans many documents totaling 100K-500K tokens. We introduce Gavel-Ref, a reference-based evaluation framework with multi-value checklist evaluation over 26 items, as well as residual fact and writing-style evaluations. Using Gavel-Ref, we go beyond the single aggregate scores reported in prior work and systematically evaluate 12 frontier LLMs on 100 legal cases ranging from 32K to 512K tokens, primarily from 2025. Our results show that even the strongest model, Gemini 2.5 Pro, achieves only around 50 of $S_{\text{Gavel-Ref}}$, highlighting the difficulty of the task. Models perform well on simple checklist items (e.g., filing date) but struggle on multi-value or rare ones such as settlements and monitor reports. As LLMs continue to improve and may surpass human-written summaries -- making human references less reliable -- we develop Gavel-Agent, an efficient and autonomous agent scaffold that equips LLMs with six tools to navigate and extract checklists directly from case documents. With Qwen3, Gavel-Agent reduces token usage by 36% while resulting in only a 7% drop in $S_{\text{checklist}}$ compared to end-to-end extraction with GPT-4.1.
Decision-Aware Trust Signal Alignment for SOC Alert Triage
Venue: arXiv cs.CR 2026
Year: 2026

Detection systems that utilize machine learning are progressively implemented at Security Operations Centers (SOCs) to help an analyst to filter through high volumes of security alerts. Practically, such systems tend to reveal probabilistic results or confidence scores which are ill-calibrated and hard to read when under pressure. Qualitative and survey based studies of SOC practice done before reveal that poor alert quality and alert overload greatly augment the burden on the analyst, especially when tool outputs are not coherent with decision requirements, or signal noise. One of the most significant limitations is that model confidence is usually shown without expressing that there are asymmetric costs in decision making where false alarms are much less harmful than missed attacks. The present paper presents a decision-sensitive trust signal correspondence scheme of SOC alert triage. The framework combines confidence that has been calibrated, lightweight uncertainty cues, and cost-sensitive decision thresholds into coherent decision-support layer, instead of making changes to detection models. To enhance probabilistic consistency, the calibration is done using the known post-hoc methods and the uncertainty cues give conservative protection in situations where model certainty is low. To measure the model-independent performance of the suggested model, we apply the Logistic Regression and the Random Forest classifiers to the UNSW-NB15 intrusion detection benchmark. According to simulation findings, false negatives are greatly amplified by the presence of misaligned displays of confidence, whereas cost weighted loss decreases by orders of magnitude between models with decision aligned trust signals. Lastly, we describe a human-in-the-loop study plan that would allow empirically assessing the decision-making of the analysts with aligned and misaligned trust interfaces.
IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation
Venue: arXiv cs.LG 2026
Year: 2026

Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information. While recent text-to-image (T2I) models can generate aesthetically appealing images, their reliability in generating infographics remains unclear. Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content. We present IGENBENCH, the first benchmark for evaluating the reliability of text-to-infographic generation, comprising 600 curated test cases spanning 30 infographic types. We design an automated evaluation framework that decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types. We employ multimodal large language models (MLLMs) to verify each question, yielding question-level accuracy (Q-ACC) and infographic-level accuracy (I-ACC). We comprehensively evaluate 10 state-of-the-art T2I models on IGENBENCH. Our systematic analysis reveals key insights for future model development: (i) a three-tier performance hierarchy with the top model achieving Q-ACC of 0.90 but I-ACC of only 0.49; (ii) data-related dimensions emerging as universal bottlenecks (e.g., Data Completeness: 0.21); and (iii) the challenge of achieving end-to-end correctness across all models. We release IGENBENCH at https://igen-bench.vercel.app/.
Specific Emitter Identification via Active Learning
Venue: arXiv cs.AI 2026
Year: 2026

With the rapid growth of wireless communications, specific emitter identification (SEI) is significant for communication security. However, its model training relies heavily on the large-scale labeled data, which are costly and time-consuming to obtain. To address this challenge, we propose an SEI approach enhanced by active learning (AL), which follows a three-stage semi-supervised training scheme. In the first stage, self-supervised contrastive learning is employed with a dynamic dictionary update mechanism to extract robust representations from large amounts of the unlabeled data. In the second stage, supervised training on a small labeled dataset is performed, where the contrastive and cross-entropy losses are jointly optimized to improve the feature separability and strengthen the classification boundaries. In the third stage, an AL module selects the most valuable samples from the unlabeled data for annotation based on the uncertainty and representativeness criteria, further enhancing generalization under limited labeling budgets. Experimental results on the ADS-B and WiFi datasets demonstrate that the proposed SEI approach significantly outperforms the conventional supervised and semi-supervised methods under limited annotation conditions, achieving higher recognition accuracy with lower labeling cost.
Multiagent Reinforcement Learning with Neighbor Action Estimation
Venue: arXiv cs.RO 2026
Year: 2026

Multiagent reinforcement learning, as a prominent intelligent paradigm, enables collaborative decision-making within complex systems. However, existing approaches often rely on explicit action exchange between agents to evaluate action value functions, which is frequently impractical in real-world engineering environments due to communication constraints, latency, energy consumption, and reliability requirements. From an artificial intelligence perspective, this paper proposes an enhanced multiagent reinforcement learning framework that employs action estimation neural networks to infer agent behaviors. By integrating a lightweight action estimation module, each agent infers neighboring agents' behaviors using only locally observable information, enabling collaborative policy learning without explicit action sharing. This approach is fully compatible with standard TD3 algorithms and scalable to larger multiagent systems. At the engineering application level, this framework has been implemented and validated in dual-arm robotic manipulation tasks: two robotic arms collaboratively lift objects. Experimental results demonstrate that this approach significantly enhances the robustness and deployment feasibility of real-world robotic systems while reducing dependence on information infrastructure. Overall, this research advances the development of decentralized multiagent artificial intelligence systems while enabling AI to operate effectively in dynamic, information-constrained real-world environments.
TSSR: Two-Stage Swap-Reward-Driven Reinforcement Learning for Character-Level SMILES Generation
Venue: arXiv cs.LG 2026
Year: 2026

The design of reliable, valid, and diverse molecules is fundamental to modern drug discovery, as improved molecular generation supports efficient exploration of the chemical space for potential drug candidates and reduces the cost of early design efforts. Despite these needs, current chemical language models that generate molecules as SMILES strings are vulnerable to compounding token errors: many samples are unparseable or chemically implausible, and hard constraints meant to prevent failure can restrict exploration. To address this gap, we introduce TSSR, a Two-Stage, Swap-Reward-driven reinforcement learning (RL) framework for character-level SMILES generation. Stage one rewards local token swaps that repair syntax, promoting transitions from invalid to parseable strings. Stage two provides chemistry-aware feedback from RDKit diagnostics, rewarding reductions in valence, aromaticity, and connectivity issues. The reward decomposes into interpretable terms (swap efficiency, error reduction, distance to validity), is model agnostic, and requires no task-specific labels or hand-crafted grammars. We evaluated TSSR on the MOSES benchmark using a GRU policy trained with PPO in both pure RL (P-RL) from random initialization and fine-tuning RL (F-RL) starting from a pretrained chemical language model, assessing 10,000 generated SMILES per run. In P-RL, TSSR significantly improves syntactic validity, chemical validity, and novelty. In F-RL, TSSR preserves drug-likeness and synthesizability while increasing validity and novelty. Token-level analysis shows that syntax edits and chemistry fixes act jointly to reduce RDKit detected errors. TSSR converts a sparse terminal objective into a denser and more interpretable reward, improving both syntactic and chemical quality without reducing diversity. TSSR is dataset-agnostic and can be adapted to various reinforcement learning approaches.
Design and Development of Modular Limbs for Reconfigurable Robots on the Moon
Venue: arXiv cs.RO 2026
Year: 2026

In this paper, we present the development of 4-DOF robot limbs, which we call Moonbots, designed to connect in various configurations with each other and wheel modules, enabling adaptation to different environments and tasks. These modular components are intended primarily for robotic systems in space exploration and construction on the Moon in our Moonshot project. Such modular robots add flexibility and versatility for space missions where resources are constrained. Each module is driven by a common actuator characterized by a high torque-to-speed ratio, supporting both precise control and dynamic motion when required. This unified actuator design simplifies development and maintenance across the different module types. The paper describes the hardware implementation, the mechanical design of the modules, and the overall software architecture used to control and coordinate them. Additionally, we evaluate the control performance of the actuator under various load conditions to characterize its suitability for modular robot applications. To demonstrate the adaptability of the system, we introduce nine functional configurations assembled from the same set of modules: 4DOF-limb, 8DOF-limb, vehicle, dragon, minimal, quadruped, cargo, cargo-minimal, and bike. These configurations reflect different locomotion strategies and task-specific behaviors, offering a practical foundation for further research in reconfigurable robotic systems.
Deep Dive into the Abuse of DL APIs To Create Malicious AI Models and How to Detect Them
Venue: arXiv cs.CR 2026
Year: 2026

According to Gartner, more than 70% of organizations will have integrated AI models into their workflows by the end of 2025. In order to reduce cost and foster innovation, it is often the case that pre-trained models are fetched from model hubs like Hugging Face or TensorFlow Hub. However, this introduces a security risk where attackers can inject malicious code into the models they upload to these hubs, leading to various kinds of attacks including remote code execution (RCE), sensitive data exfiltration, and system file modification when these models are loaded or executed (predict function). Since AI models play a critical role in digital transformation, this would drastically increase the number of software supply chain attacks. While there are several efforts at detecting malware when deserializing pickle based saved models (hiding malware in model parameters), the risk of abusing DL APIs (e.g. TensorFlow APIs) is understudied. Specifically, we show how one can abuse hidden functionalities of TensorFlow APIs such as file read/write and network send/receive along with their persistence APIs to launch attacks. It is concerning to note that existing scanners in model hubs like Hugging Face and TensorFlow Hub are unable to detect some of the stealthy abuse of such APIs. This is because scanning tools only have a syntactically identified set of suspicious functionality that is being analysed. They often do not have a semantic-level understanding of the functionality utilized. After demonstrating the possible attacks, we show how one may identify potentially abusable hidden API functionalities using LLMs and build scanners to detect such abuses.
Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing
Venue: arXiv cs.IR 2026
Year: 2026

In recommender systems, online A/B testing is a crucial method for evaluating the performance of different models. However, conducting online A/B testing often presents significant challenges, including substantial economic costs, user experience degradation, and considerable time requirements. With the Large Language Models' powerful capacity, LLM-based agent shows great potential to replace traditional online A/B testing. Nonetheless, current agents fail to simulate the perception process and interaction patterns, due to the lack of real environments and visual perception capability. To address these challenges, we introduce a multi-modal user agent for A/B testing (A/B Agent). Specifically, we construct a recommendation sandbox environment for A/B testing, enabling multimodal and multi-page interactions that align with real user behavior on online platforms. The designed agent leverages multimodal information perception, fine-grained user preferences, and integrates profiles, action memory retrieval, and a fatigue system to simulate complex human decision-making. We validated the potential of the agent as an alternative to traditional A/B testing from three perspectives: model, data, and features. Furthermore, we found that the data generated by A/B Agent can effectively enhance the capabilities of recommendation models. Our code is publicly available at https://github.com/Applied-Machine-Learning-Lab/ABAgent.
Improving Semi-Supervised Contrastive Learning via Entropy-Weighted Confidence Integration of Anchor-Positive Pairs
Venue: arXiv cs.LG 2026
Year: 2026

Conventional semi-supervised contrastive learning methods assign pseudo-labels only to samples whose highest predicted class probability exceeds a predefined threshold, and then perform supervised contrastive learning using those selected samples. In this study, we propose a novel loss function that estimates the confidence of each sample based on the entropy of its predicted probability distribution and applies confidence-based adaptive weighting. This approach enables pseudo-label assignment even to samples that were previously excluded from training and facilitates contrastive learning that accounts for the confidence of both anchor and positive samples in a more principled manner. Experimental results demonstrate that the proposed method improves classification accuracy and achieves more stable learning performance even under low-label conditions.
Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence for Industry 5.0
Venue: arXiv cs.CE 2026
Year: 2026

The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.