OpenRaiser/Intern-Atlas huggingface dataset:
Remote sensing for sustainable river management: Estimating riverscape vulnerability for Ganga, the world's most densely populated river basin
Venue: arXiv cs.CY 2026
Year: 2026

Surface water mixed with wastewater creates serious environmental concerns, particularly in densely populated urban areas with inadequate infrastructure. Such contamination threatens to cause major public health crises in the Ganga Basin where monsoonal flooding converges with 6 billion liters of untreated sewage that is discharged daily into the basin by 650 million people. GIS-based analytic hierarchy process (AHP) with remote sensing data was conducted to highlight areas of vulnerability along a 20-km wide riverscape. Analytic network process (ANP), Nested AHP, fuzzy AHP, and 1-N AHP (novel variant of AHP) were used to constrain AHP model uncertainties, and composites of these analyses were utilized to define the vulnerability of the river Ganga to pollution. AHP categorized 83.7% of the area as having extremely low or low vulnerability and 3.5% of the area as having highly or extremely high vulnerability. ANP and Nested AHP produced focused, yet dampened, vulnerability-score maps compared to AHP. Fuzzy AHP and 1-N AHP detected sensitivities to factor variability and potential unknown acute and chronic factors. While fuzzy AHP identified quintile-level changes in vulnerability based on scenario parameters, vulnerability scores of 1-N AHP and AHP showed no major differences. Normalized composite vulnerability \(\geq\)2 standard deviations highlighted particularly vulnerable locations and identified instances where network effects were greater than factor class and vice versa. Together, these analyses located areas of extreme vulnerability at the nexus of river Ganga and urban landscapes as well as regions of low vulnerability potentially suitable for conservation efforts or sustainable development practices to prevent their degradation.
Automatic Item Generation for Personality Situational Judgment Tests with Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Personality assessment through situational judgment tests (SJTs) offers unique advantages over traditional Likert-type self-report scales, yet their development remains labor-intensive, time-consuming, and heavily dependent on subject matter experts. Recent advances in large language models (LLMs) have shown promise for automatic item generation (AIG). Building on these developments, the present study focuses on developing and evaluating a structured and generalizable framework for automatically generating personality SJTs, using GPT-4 and ChatGPT-5 as empirical examples. Three studies were conducted. Study 1 systematically compared the effects of prompt design and temperature settings on the content validity of LLM-generated items to develop an effective and stable LLM-based AIG approach for personality SJT. Results showed that optimized prompts and a temperature of 1.0 achieved the best balance of creativity and accuracy on GPT-4. Study 2 examined the cross-model generalizability and reproducibility of this automated SJT generation approach through multiple rounds. The results showed that the approach consistently produced reproducible and high-quality items on ChatGPT-5. Study 3 evaluated the psychometric properties of LLM-generated SJTs covering five facets of the Big Five personality traits. Results demonstrated satisfactory reliability and validity across most facets, though limitations were observed in the convergent validity of the compliance facet and certain aspects of criterion-related validity. These findings provide robust evidence that the proposed LLM-based AIG approach can produce culturally appropriate and psychometrically sound SJTs with efficiency comparable to or exceeding traditional methods.
SAMPAI: A VR Framework for Industrial Safety Training Inspired by Cultural Heritage Education
Venue: arXiv cs.HC 2026
Year: 2026

This study explores the application of Virtual Reality (VR) to industrial safety training by adapting immersive design principles from cultural heritage education. The SAMPAI simulator, developed for the IPLOM refinery in Busalla (Italy), offers a controlled environment that enhances spatial awareness and procedural retention in high-risk scenarios. The project emphasizes user-centered interaction and anticipates future integration with Augmented Reality (AR) for real-time operational support. To ensure reliability, a rigorous validation protocol is proposed: initial testing with a small operator group will identify usability issues, followed by incremental expansion to strengthen system robustness and real-world relevance. By shifting training from abstract instruction to situated, experiential learning, SAMPAI bridges humanistic and technical domains. The result is a scalable, immersive framework that enhances readiness and supports safe, informed decision-making in complex industrial environments.
Improving Graph Neural Network Training, Defense and Hypergraph Clustering via Adversarial Robustness Evaluation
Venue: arXiv cs.LG 2026
Year: 2026

Graph Neural Networks (GNNs) are a highly effective neural network architecture for processing graph-structured data. Unlike traditional neural networks that rely solely on the features of the data as input, GNNs leverage both the graph structure, which represents the relationships between data points, and the feature matrix of the data to optimize their feature representation. This unique capability enables GNNs to achieve superior performance across various tasks. However, it also makes GNNs more susceptible to noise and adversarial attacks from both the graph structure and data features, which can significantly increase the training difficulty and degrade their performance. Similarly, a hypergraph is a highly complex structure, and partitioning a hypergraph is a challenging task. This paper leverages spectral adversarial robustness evaluation to effectively address key challenges in complex-graph algorithms. By using spectral adversarial robustness evaluation to distinguish robust nodes from non-robust ones and treating them differently, we propose a training-set construction strategy that improves the training quality of GNNs. In addition, we develop algorithms to enhance both the adversarial robustness of GNNs and the performance of hypergraph clustering. Experimental results show that this series of methods is highly effective.
Finite Sample Bounds for Non-Parametric Regression: Optimal Sample Efficiency and Space Complexity
Venue: arXiv cs.LG 2026
Year: 2026

We address the problem of learning an unknown smooth function and its derivatives from noisy pointwise evaluations under the supremum norm. While classical nonparametric regression provides a strong theoretical foundation, traditional kernel-based estimators often incur high computational costs and memory requirements that scale with the sample size, limiting their utility in real-time applications such as reinforcement learning. To overcome these challenges, we propose a parametric approach based on a finite-dimensional representation that achieves minimax-optimal uniform convergence rates. Our method enables lightweight inference without storing all samples in memory. We provide sharp finite-sample bounds under sub-Gaussian noise, derive second-order Bernstein-type guarantees, and prove matching lower bounds, thereby confirming the optimality of our approach in both estimation error and memory efficiency.
Expressivity of AuDaLa: Turing Completeness and Possible Extensions
Venue: arXiv cs.LO 2026
Year: 2026

AuDaLa is a recently introduced programming language that follows the new data autonomous paradigm. In this paradigm, small pieces of data execute functions autonomously. Considering the paradigm and the design choices of AuDaLa, it is interesting to determine the expressivity of the language. In this paper, we implement Turing machines in AuDaLa and prove that implementation correct. This proves that AuDaLa is Turing complete, giving an initial indication of AuDaLa's expressivity. Additionally, we give examples of how to add extensions to AuDaLa to increase its practical expressivity and to better match conventional parallel languages, allowing for a more straightforward and performant implementation of algorithms.
The Content Moderator's Dilemma: Removal of Toxic Content and Distortions to Online Discourse
Venue: arXiv cs.SI 2026
Year: 2026

There is an ongoing debate about how to moderate toxic speech on social media and the impact of content moderation on online discourse. This paper proposes and validates a methodology for measuring the content-moderation-induced distortions in online discourse using text embeddings from computational linguistics. Applying the method to a representative sample of 5 million US political Tweets, we find that removing toxic Tweets alters the semantic composition of content. This finding is consistent across different embedding models, toxicity metrics, and samples. Importantly, we demonstrate that these effects are not solely driven by toxic language but by the removal of topics often expressed in toxic form. We propose an alternative approach to content moderation that uses generative Large Language Models to rephrase toxic Tweets, preserving their salvageable content rather than removing them entirely. We show that this rephrasing strategy reduces toxicity while minimizing distortions in online content.
ViLBias: Detecting and Reasoning about Bias in Multimodal Content
Venue: arXiv cs.AI 2026
Year: 2026

Detecting bias in multimodal news requires models that reason over text--image pairs, not just classify text. In response, we present ViLBias, a VQA-style benchmark and framework for detecting and reasoning about bias in multimodal news. The dataset comprises 40,945 text--image pairs from diverse outlets, each annotated with a bias label and concise rationale using a two-stage LLM-as-annotator pipeline with hierarchical majority voting and human-in-the-loop validation. We evaluate Small Language Models (SLMs), Large Language Models (LLMs), and Vision--Language Models (VLMs) across closed-ended classification and open-ended reasoning (oVQA), and compare parameter-efficient tuning strategies. Results show that incorporating images alongside text improves detection accuracy by 3--5\%, and that LLMs/VLMs better capture subtle framing and text--image inconsistencies than SLMs. Parameter-efficient methods (LoRA/QLoRA/Adapters) recover 97--99\% of full fine-tuning performance with $<5\%$ trainable parameters. For oVQA, reasoning accuracy spans 52--79\% and faithfulness 68--89\%, both improved by instruction tuning; closed accuracy correlates strongly with reasoning ($r = 0.91$). ViLBias offers a scalable benchmark and strong baselines for multimodal bias detection and rationale quality.
Talking with Tables for Better LLM Factual Data Interactions
Venue: arXiv cs.AI 2026
Year: 2026

Large Language Models (LLMs) often struggle with requests related to information retrieval and data manipulation that frequently arise in real-world scenarios under multiple conditions. In this paper, we demonstrate that leveraging tabular structures in LLM interactions, is more effective than utilizing other structures for handling prevalent requests that operate over factual data. Through comprehensive evaluations across various scenarios and request types, we show that providing tabular structures yields a 40.29\% average performance gain along with better robustness and token efficiency. Through attention-value analysis, we discover that tables help LLMs better locate relevant information, explaining these improvements. Beyond tables and text, we evaluate whether (1) blending structuredness within text, such as providing templates or fixing the order of attributes, and (2) other representative structures, such as knowledge graphs and JSON are helpful. We observe that utilizing tables offers the best balance between efficiency and effectiveness. The method remains robust to task complexity and adapts to unstructured sources through text-to-table conversion. Overall, we highlight the untapped potential of tabular representations for future LLM applications.
Sampling-Based Constrained Motion Planning with Products of Experts
Venue: arXiv cs.RO 2026
Year: 2026

We present a novel approach to enhance the performance of sampling-based Model Predictive Control (MPC) in constrained optimization by leveraging products of experts. Our methodology divides the main problem into two components: one focused on optimality and the other on feasibility. By combining the solutions from each component, represented as distributions, we apply products of experts to implement a project-then-sample strategy. In this strategy, the optimality distribution is projected into the feasible area, allowing for more efficient sampling. This approach contrasts with the traditional sample-then-project and naive sample-then-reject method, leading to more diverse exploration and reducing the accumulation of samples on the boundaries. We demonstrate an effective implementation of this principle using a tensor train-based distribution model, which is characterized by its non-parametric nature, ease of combination with other distributions at the task level, and straightforward sampling technique. We adapt existing tensor train models to suit this purpose and validate the efficacy of our approach through experiments in various tasks, including obstacle avoidance, non-prehensile manipulation, and tasks involving staying in a restricted volume. Our experimental results demonstrate that the proposed method consistently outperforms known baselines, providing strong empirical support for its effectiveness. Sample codes for this project are available at https://github.com/idiap/smpc_poe.
Point-DeepONet: Predicting Nonlinear Fields on Non-Parametric Geometries under Variable Load Conditions
Venue: arXiv cs.LG 2026
Year: 2026

Nonlinear structural analyses in engineering often require extensive finite element simulations, limiting their applicability in design optimization and real-time control. Conventional deep learning surrogates often struggle with complex, non-parametric three-dimensional (3D) geometries and directionally varying loads. This work presents Point-DeepONet, an operator-learning-based surrogate that integrates PointNet into the DeepONet framework to learn a mapping from non-parametric geometries and variable load conditions to physical response fields. By leveraging PointNet to learn a geometric representation from raw point clouds, our model circumvents the need for manual parameterization. This geometric embedding is then synergistically fused with load conditions within the DeepONet architecture to accurately predict three-dimensional displacement and von Mises stress fields. Trained on a large-scale dataset, Point-DeepONet demonstrates high fidelity, achieving a coefficient of determination (R^2) reaching 0.987 for displacement and 0.923 for von Mises stress. Furthermore, to rigorously validate its generalization capabilities, we conducted additional experiments on unseen, randomly oriented load directions, where the model maintained exceptional accuracy. Compared to nonlinear finite element analyses that require about 19.32 minutes per case, Point-DeepONet provides predictions in mere seconds--approximately 400 times faster--while maintaining excellent scalability. These findings, validated through extensive experiments and ablation studies, highlight the potential of Point-DeepONet to enable rapid, high-fidelity structural analyses for complex engineering workflows.
Exploring Embedding Priors in Prompt-Tuning for Improved Interpretability and Control
Venue: arXiv cs.CL 2026
Year: 2026

Prompt-Tuning is an efficient method for adapting pre-trained language models to new tasks with minimal computational overhead by modifying prompt embeddings. In this work, we investigate how crucial the phenomenon of embedding collapse, frequently observed in Prompt-Tuning, is for the final performance of the model. To address this question, we designed embedding priors and compared them with posteriors of the converged Soft and Deep Prompt-Tuning methods. Our findings suggest that priors strongly affect the position of the tuned embeddings, and models can effectively work with embeddings from different parts of activation spaces, including completely new regions. As the final Prompt-Tuning capabilities are limited, we hypothesize that controllable Prompt-Tuning posteriors may serve as a good starting point for tasks such as chain-of-thought (COT) distillation. Our experiments also show that generated trajectories are not localized in the activation space of the models. However, there are distinct clusters of activations for distant tasks (e.g., NLP and arithmetic), while activations between NLP tasks (e.g., Question-Answering and MLM) lie in the same cluster. These observations raise questions about the importance of a single activation cluster for the generalization abilities of large language models.
Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach
Venue: arXiv cs.LG 2026
Year: 2026

Accurate tool wear prediction is essential for maintaining productivity and minimizing costs in machining. However, the complex nature of the tool wear process poses significant challenges to achieving reliable predictions. This study explores data-driven methods, in particular deep learning, for tool wear prediction. Traditional data-driven approaches often focus on a single process, relying on multi-sensor setups and extensive data generation, which limits generalization to new settings. Moreover, multi-sensor integration is often impractical in industrial environments. To address these limitations, this research investigates the transferability of predictive models using minimal training data, validated across two processes. Furthermore, it uses a simple setup with a single acceleration sensor to establish a low-cost data generation approach that facilitates the generalization of models to other processes via transfer learning. The study evaluates several machine learning models, including transformer-inspired convolutional neural networks (CNN), long short-term memory networks (LSTM), support vector machines (SVM), and decision trees, trained on different input formats such as feature vectors and short-time Fourier transform (STFT). The performance of the models is evaluated on two machines and on different amounts of training data, including scenarios with significantly reduced datasets, providing insight into their effectiveness under constrained data conditions. The results demonstrate the potential of specific models and configurations for effective tool wear prediction, contributing to the development of more adaptable and efficient predictive maintenance strategies in machining. Notably, the ConvNeXt model has an exceptional performance, achieving 99.1\% accuracy in identifying tool wear using data from only four milling tools operated until they are worn.
Are LLMs Really Not Knowledgeable? Mining the Submerged Knowledge in LLMs' Memory
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) have shown promise as parametric knowledge bases, but often underperform on question answering (QA) tasks due to hallucinations and uncertainty. While prior work attributes these failures to knowledge gaps in the model's parameters, we uncover a complementary phenomenon: LLMs frequently retain correct knowledge even when generating incorrect or "unsure" answers. By analyzing the token-level output distributions, we find that correct answers often appear among high-probability candidates, despite not being selected. Motivated by this, we propose Hits@k, a novel metric to evaluate latent knowledge retention independent of answer surface form. Our experiments reveal that LLMs possess significantly more factual knowledge than is reflected by standard QA accuracy. Building on these insights, we further examine the prevailing few-shot QA paradigm. We find that prompting strategies which allow "unsure" outputs can inadvertently suppress correct answers by discouraging low-confidence generation. We design a set of quantitative experiments to measure this suppression effect, offering practical guidance for future prompt and decoding design in knowledge-intensive tasks.
VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation
Venue: arXiv cs.CV 2026
Year: 2026

Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimensions remains a persistent challenge. Though reinforcement learning from human feedback offers promise for preference alignment, existing reward models for visual generation face limitations, including black-box scoring without interpretability and potentially resultant unexpected biases. We present VisionReward, a general framework for learning human visual preferences in both image and video generation. Specifically, we employ a hierarchical visual assessment framework to capture fine-grained human preferences, and leverages linear weighting to enable interpretable preference learning. Furthermore, we propose a multi-dimensional consistent strategy when using VisionReward as a reward model during preference optimization for visual generation. Experiments show that VisionReward can significantly outperform existing image and video reward models on both machine metrics and human evaluation. Notably, VisionReward surpasses VideoScore by 17.2% in preference prediction accuracy, and text-to-video models with VisionReward achieve a 31.6% higher pairwise win rate compared to the same models using VideoScore. All code and datasets are provided at https://github.com/THUDM/VisionReward.
GRASP: Replace Redundant Layers with Adaptive Singular Parameters for Efficient Model Compression
Venue: arXiv cs.CL 2026
Year: 2026

Recent studies have demonstrated that many layers are functionally redundant in large language models (LLMs), enabling model compression by removing these layers to reduce inference cost. While such approaches can improve efficiency, indiscriminate layer pruning often results in significant performance degradation. In this paper, we propose GRASP (Gradient-based Retention of Adaptive Singular Parameters), a novel compression framework that mitigates this issue by preserving sensitivity-aware singular values. Unlike direct layer pruning, GRASP leverages gradient-based attribution on a small calibration dataset to adaptively identify and retain critical singular components. By replacing redundant layers with only a minimal set of parameters, GRASP achieves efficient compression while maintaining strong performance with minimal overhead. Experiments across multiple LLMs show that GRASP consistently outperforms existing compression methods, achieving 90% of the original model's performance under 20% compression ratio.