# Research Papers — 2025-10 · bundle page 170 of 217 > Full-text markdown of papers #8451–8500 of 10843 published in 2025-10, newest first. AI-readable mirrors of the full 10843-paper 2025-10 archive (50 papers per file). Canonical page: https://aipod.dev/papers/archive/2025-10/page/170 [← Previous bundle](https://aipod.dev/papers/archive/2025-10/page/169.md) [Next bundle →](https://aipod.dev/papers/archive/2025-10/page/171.md) ### Test-Time Scaling of Reasoning Models for Machine Translation Authors: Zihao Li, Shaoxiong Ji, Jörg Tiedemann Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06471v2 Test-time scaling (TTS) has enhanced the performance of Reasoning Models (RMs) on various tasks such as math and coding, yet its efficacy in machine translation (MT) remains underexplored. This paper investigates whether increased inference-time computation improves translation quality. We evaluate 12 RMs across a diverse suite of MT benchmarks spanning multiple domains, examining three scenarios: direct translation, forced-reasoning extrapolation, and post-editing. Our findings show that for general-purpose RMs, TTS provides limited and inconsistent benefits for direct translation, with performance quickly plateauing. However, the effectiveness of TTS is unlocked by domain-specific fine-tuning, which aligns a model's reasoning process with task requirements, leading to consistent improvements up to an optimal, self-determined reasoning depth. We also find that forcing a model to reason beyond its natural stopping point consistently degrades translation quality. In contrast, TTS proves highly effective in a post-editing context, reliably turning self-correction into a beneficial process. These results indicate that the value of inference-time computation in MT lies not in enhancing single-pass translation with general models, but in targeted applications like multi-step, self-correction workflows and in conjunction with task-specialized models. --- ### SIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation Authors: Oindrila Saha, Vojtech Krs, Radomir Mech, Subhransu Maji, Kevin Blackburn-Matzen, Matheus Gadelha Published: 2025-10-07 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.06469v1 We present SIGMA-GEN, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-GEN is the first to enable single-pass multi-subject identity-preserved generation guided by both structural and spatial constraints. A key strength of our method is its ability to support user guidance at various levels of precision -- from coarse 2D or 3D boxes to pixel-level segmentations and depth -- with a single model. To enable this, we introduce SIGMA-SET27K, a novel synthetic dataset that provides identity, structure, and spatial information for over 100k unique subjects across 27k images. Through extensive evaluation we demonstrate that SIGMA-GEN achieves state-of-the-art performance in identity preservation, image generation quality, and speed. Code and visualizations at https://oindrilasaha.github.io/SIGMA-Gen/ --- ### Linguistically Informed Tokenization Improves ASR for Underresourced Languages Authors: Massimo Daul, Alessio Tosolini, Claire Bowern Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06461v1 Automatic speech recognition (ASR) is a crucial tool for linguists aiming to perform a variety of language documentation tasks. However, modern ASR systems use data-hungry transformer architectures, rendering them generally unusable for underresourced languages. We fine-tune a wav2vec2 ASR model on Yan-nhangu, a dormant Indigenous Australian language, comparing the effects of phonemic and orthographic tokenization strategies on performance. In parallel, we explore ASR's viability as a tool in a language documentation pipeline. We find that a linguistically informed phonemic tokenization system substantially improves WER and CER compared to a baseline orthographic tokenization scheme. Finally, we show that hand-correcting the output of an ASR model is much faster than hand-transcribing audio from scratch, demonstrating that ASR can work for underresourced languages. --- ### TDiff: Thermal Plug-And-Play Prior with Patch-Based Diffusion Authors: Piyush Dashpute, Niki Nezakati, Wolfgang Heidrich, Vishwanath Saragadam Published: 2025-10-07 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.06460v1 Thermal images from low-cost cameras often suffer from low resolution, fixed pattern noise, and other localized degradations. Available datasets for thermal imaging are also limited in both size and diversity. To address these challenges, we propose a patch-based diffusion framework (TDiff) that leverages the local nature of these distortions by training on small thermal patches. In this approach, full-resolution images are restored by denoising overlapping patches and blending them using smooth spatial windowing. To our knowledge, this is the first patch-based diffusion framework that models a learned prior for thermal image restoration across multiple tasks. Experiments on denoising, super-resolution, and deblurring demonstrate strong results on both simulated and real thermal data, establishing our method as a unified restoration pipeline. --- ### Evaluating Node-tree Interfaces for AI Explainability Authors: Lifei Wang, Natalie Friedman, Chengchao Zhu, Zeshu Zhu, S. Joy Mountford Published: 2025-10-07 Categories: cs.HC, cs.AI arXiv: https://arxiv.org/abs/2510.06457v1 As large language models (LLMs) become ubiquitous in workplace tools and decision-making processes, ensuring explainability and fostering user trust are critical. Although advancements in LLM engineering continue, human-centered design is still catching up, particularly when it comes to embedding transparency and trust into AI interfaces. This study evaluates user experiences with two distinct AI interfaces - node-tree interfaces and chatbot interfaces - to assess their performance in exploratory, follow-up inquiry, decision-making, and problem-solving tasks. Our design-driven approach introduces a node-tree interface that visually structures AI-generated responses into hierarchically organized, interactive nodes, allowing users to navigate, refine, and follow up on complex information. In a comparative study with n=20 business users, we observed that while the chatbot interface effectively supports linear, step-by-step queries, it is the node-tree interface that enhances brainstorming. Quantitative and qualitative findings indicate that node-tree interfaces not only improve task performance and decision-making support but also promote higher levels of user trust by preserving context. Our findings suggest that adaptive AI interfaces capable of switching between structured visualizations and conversational formats based on task requirements can significantly enhance transparency and user confidence in AI-powered systems. This work contributes actionable insights to the fields of human-robot interaction and AI design, particularly for enterprise applications where trust-building is critical for teams. --- ### How NOT to benchmark your SITE metric: Beyond Static Leaderboards and Towards Realistic Evaluation Authors: Prabhant Singh, Sibylle Hess, Joaquin Vanschoren Published: 2025-10-07 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2510.06448v1 Transferability estimation metrics are used to find a high-performing pre-trained model for a given target task without fine-tuning models and without access to the source dataset. Despite the growing interest in developing such metrics, the benchmarks used to measure their progress have gone largely unexamined. In this work, we empirically show the shortcomings of widely used benchmark setups to evaluate transferability estimation metrics. We argue that the benchmarks on which these metrics are evaluated are fundamentally flawed. We empirically demonstrate that their unrealistic model spaces and static performance hierarchies artificially inflate the perceived performance of existing metrics, to the point where simple, dataset-agnostic heuristics can outperform sophisticated methods. Our analysis reveals a critical disconnect between current evaluation protocols and the complexities of real-world model selection. To address this, we provide concrete recommendations for constructing more robust and realistic benchmarks to guide future research in a more meaningful direction. --- ### A Survey on Agentic Security: Applications, Threats and Defenses Authors: Asif Shahriar, Md Nafiu Rahman, Sadif Ahmed, Farig Sadeque, Md Rizwan Parvez Published: 2025-10-07 Categories: cs.CL, cs.AI, cs.CR arXiv: https://arxiv.org/abs/2510.06445v3 LLM-based agents are now used throughout cybersecurity. While these agents facilitate powerful and autonomous security applications, their autonomy opens up new attack surfaces, and the security community is actively building defenses to secure them. Yet the literature on this subject has grown quickly and unevenly. Existing surveys treat applications, threats, and defenses in isolation, leaving no unified account of how an agent's capabilities, vulnerabilities, and countermeasures interconnect. In this work we present the first holistic survey of the agentic security landscape, structuring the field around the fundamental pillars of Applications, Threats and Defenses. We provide a comprehensive taxonomy of over 260 papers, explaining how agents are used in downstream cybersecurity applications, inherent threats to agentic systems, and countermeasures designed to protect them. In addition, we provide detailed pillar-specific and cross-cutting analyses that show the security-lifecycle coverage of agentic applications, comparison between red-teaming and blue-teaming agents, and the adversarial use of red-teaming applications. On the threat side, we analyze the entry points and agent-loop stages that attacks target, their specificity to the agentic setting, and the threat models they assume. On the defense side, we analyze the prevailing defense strategies, their cost and security trade-offs, and where in the agent lifecycle they are deployed. We further map which defenses cover which attack classes and chart trends in agent architecture, backbone model usage, data modality coverage, and the growth of attack and defense research over time. Taken together, these findings indicate that agentic systems are structurally fragile by default and that securing them will require defenses that span the full agent lifecycle rather than single-layer fixes. --- ### Context-Aware Inference via Performance Forecasting in Decentralized Learning Networks Authors: Joel Pfeffer, J. M. Diederik Kruijssen, Clément Gossart, Mélanie Chevance, Diego Campo Millan, Florian Stecker, Steven N. Longmore Published: 2025-10-07 Categories: cs.LG, cs.AI, cs.DC arXiv: https://arxiv.org/abs/2510.06444v1 In decentralized learning networks, predictions from many participants are combined to generate a network inference. While many studies have demonstrated performance benefits of combining multiple model predictions, existing strategies using linear pooling methods (ranging from simple averaging to dynamic weight updates) face a key limitation. Dynamic prediction combinations that rely on historical performance to update weights are necessarily reactive. Due to the need to average over a reasonable number of epochs (with moving averages or exponential weighting), they tend to be slow to adjust to changing circumstances (phase or regime changes). In this work, we develop a model that uses machine learning to forecast the performance of predictions by models at each epoch in a time series. This enables `context-awareness' by assigning higher weight to models that are likely to be more accurate at a given time. We show that adding a performance forecasting worker in a decentralized learning network, following a design similar to the Allora network, can improve the accuracy of network inferences. Specifically, we find forecasting models that predict regret (performance relative to the network inference) or regret z-score (performance relative to other workers) show greater improvement than models predicting losses, which often do not outperform the naive network inference (historically weighted average of all inferences). Through a series of optimization tests, we show that the performance of the forecasting model can be sensitive to choices in the feature set and number of training epochs. These properties may depend on the exact problem and should be tailored to each domain. Although initially designed for a decentralized learning network, using performance forecasting for prediction combination may be useful in any situation where predictive rather than reactive model weighting is needed. --- ### Machine Learning Detection of Road Surface Conditions: A Generalizable Model using Traffic Cameras and Weather Data Authors: Carly Sutter, Kara J. Sulia, Nick P. Bassill, Christopher D. Wirz, Christopher D. Thorncroft, Jay C. Rothenberger, Vanessa Przybylo, Mariana G. Cains, Jacob Radford, David Aaron Evans Published: 2025-10-07 Categories: cs.CV, cs.LG arXiv: https://arxiv.org/abs/2510.06440v2 Transportation agencies make critical operational decisions during hazardous weather events, including assessment of road conditions and resource allocation. In this study, machine learning models are developed to provide additional support for the New York State Department of Transportation (NYSDOT) by automatically classifying current road conditions across the state. Convolutional neural networks and random forests are trained on NYSDOT roadside camera images and weather data to predict road surface conditions. This task draws critically on a robust hand-labeled dataset of ~22,000 camera images containing six road surface conditions: severe snow, snow, wet, dry, poor visibility, or obstructed. Model generalizability is prioritized to meet the operational needs of the NYSDOT decision makers, including integration of operational datasets and use of representative and realistic images. The weather-related road surface condition model in this study achieves an accuracy of 81.5% on completely unseen cameras. With operational deployment, this model has the potential to improve spatial and temporal awareness of road surface conditions, which can strengthen decision-making for operations, roadway maintenance, and traveler safety, particularly during winter weather events. --- ### Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models Authors: Akash Yadav, Ruda Zhang Published: 2025-10-07 Categories: cs.LG, cs.CE, math.OC, stat.ML arXiv: https://arxiv.org/abs/2510.06439v2 Hyperparameter tuning is a challenging problem especially when the system itself involves uncertainty. Due to noisy function evaluations, optimization under uncertainty can be computationally expensive. In this paper, we present a novel Bayesian optimization framework tailored for hyperparameter tuning under uncertainty, with a focus on optimizing a scale- or precision-type parameter in stochastic models. The proposed method employs a statistical surrogate for the underlying random variable, enabling analytical evaluation of the expectation operator. Moreover, we derive a closed-form expression for the optimizer of the random acquisition function, which significantly reduces computational cost per iteration. Numerical experiments demonstrate substantial gains in optimization efficiency: about 40-fold reduction in data usage relative to a Monte Carlo-based one-dimensional optimization scheme, and an order of magnitude improvement relative to a Gaussian process-based Bayesian optimization baseline. --- ### Nearly Instance-Optimal Parameter Recovery from Many Trajectories via Hellinger Localization Authors: Eliot Shekhtman, Yichen Zhou, Ingvar Ziemann, Nikolai Matni, Stephen Tu Published: 2025-10-07 Categories: cs.LG, stat.ML arXiv: https://arxiv.org/abs/2510.06434v1 Learning from temporally-correlated data is a core facet of modern machine learning. Yet our understanding of sequential learning remains incomplete, particularly in the multi-trajectory setting where data consists of many independent realizations of a time-indexed stochastic process. This important regime both reflects modern training pipelines such as for large foundation models, and offers the potential for learning without the typical mixing assumptions made in the single-trajectory case. However, instance-optimal bounds are known only for least-squares regression with dependent covariates; for more general models or loss functions, the only broadly applicable guarantees result from a reduction to either i.i.d. learning, with effective sample size scaling only in the number of trajectories, or an existing single-trajectory result when each individual trajectory mixes, with effective sample size scaling as the full data budget deflated by the mixing-time. In this work, we significantly broaden the scope of instance-optimal rates in multi-trajectory settings via the Hellinger localization framework, a general approach for maximum likelihood estimation. Our method proceeds by first controlling the squared Hellinger distance at the path-measure level via a reduction to i.i.d. learning, followed by localization as a quadratic form in parameter space weighted by the trajectory Fisher information. This yields instance-optimal bounds that scale with the full data budget under a broad set of conditions. We instantiate our framework across four diverse case studies: a simple mixture of Markov chains, dependent linear regression under non-Gaussian noise, generalized linear models with non-monotonic activations, and linear-attention sequence models. In all cases, our bounds nearly match the instance-optimal rates from asymptotic normality, substantially improving over standard reductions. --- ### Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health Authors: Aryan Singh Dalal, Yinglun Zhang, Duru Doğan, Atalay Mert İleri, Hande Küçük McGinty Published: 2025-10-07 Categories: cs.AI arXiv: https://arxiv.org/abs/2510.06433v1 The focus on "food as medicine" is gaining traction in the field of health and several studies conducted in the past few years discussed this aspect of food in the literature. However, very little research has been done on representing the relationship between food and health in a standardized, machine-readable format using a semantic web that can help us leverage this knowledge effectively. To address this gap, this study aims to create a knowledge graph to link food and health through the knowledge graph's ability to combine information from various platforms focusing on flavonoid contents of food found in the USDA databases and cancer connections found in the literature. We looked closely at these relationships using KNARM methodology and represented them in machine-operable format. The proposed knowledge graph serves as an example for researchers, enabling them to explore the complex interplay between dietary choices and disease management. Future work for this study involves expanding the scope of the knowledge graph by capturing nuances, adding more related data, and performing inferences on the acquired knowledge to uncover hidden relationships. --- ### MathRobust-LV: Evaluation of Large Language Models' Robustness to Linguistic Variations in Mathematical Reasoning Authors: Neeraja Kirtane, Yuvraj Khanna, Peter Relan Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06430v1 Large language models excel on math benchmarks, but their math reasoning robustness to linguistic variation is underexplored. While recent work increasingly treats high-difficulty competitions like the IMO as the gold standard for evaluating reasoning, we believe in comprehensive benchmarking of high school-level math problems in real educational settings. We introduce MathRobust-LV, a test set and evaluation methodology that mirrors how instructors rephrase problems across assessments while keeping difficulty constant: we change surface details (names, contexts, variables) while preserving numerical structure and answers. In contrast to prior efforts that alter problem content or emphasize IMO-level tasks, we focus on high-school-level dataset problems at the difficulty level where models are currently deployed in educational settings: tutoring and assessment systems. In these applications, instructors rephrase identical concepts in varied ways, making linguistic robustness essential for reliable deployment. Although MATH data benchmarking is often regarded as saturated, our experiment on 34 models reveals that accuracy declines when moving from the baseline to the variants. These drops are severe for smaller models (9-11%) while stronger models also show measurable degradation. Frontier models like GPT-5, Gemini-2.5pro remain comparatively stable. Our results highlight that robustness to linguistic variation is a fundamental challenge, exposing reasoning vulnerabilities in models. --- ### Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser Authors: Elena Chistova Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06427v1 We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and evaluate two training strategies: Multi-Head, which assigns separate relation classification layer per inventory, and Masked-Union, which enables shared parameter training through selective label masking. We first benchmark monotreebank parsing with a simple yet effective augmentation technique for low-resource settings. We then train a unified model and show that (1) the parameter efficient Masked-Union approach is also the strongest, and (2) UniRST outperforms 16 of 18 mono-treebank baselines, demonstrating the advantages of a single-model, multilingual end-to-end discourse parsing across diverse resources. --- ### FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering Authors: Yitao Long, Tiansheng Hu, Yilun Zhao, Arman Cohan, Chen Zhao Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06426v1 Large Language Models (LLMs) frequently hallucinate to long-form questions, producing plausible yet factually incorrect answers. A common mitigation strategy is to provide attribution to LLM outputs. However, existing benchmarks primarily focus on simple attribution that retrieves supporting textual evidence as references. We argue that in real-world scenarios such as financial applications, attribution goes beyond reference retrieval. We introduce FinLFQA, a benchmark designed to evaluate the ability of LLMs to generate long-form answers to complex financial questions with reliable and nuanced attributions. FinLFQA evaluates three critical aspects of attribution through human annotations: (1) supporting evidence extracted from financial reports, (2) intermediate numerical reasoning steps, and (3) domain-specific financial knowledge that informs the reasoning process. We further provide an automatic evaluation framework covering both answer quality and attribution quality. Through extensive experiments on eight LLMs across multiple attribution-generation paradigms, we find that fine-grained metrics are important to distinguish model capabilities, that end-to-end generation achieves comparable performance to post-hoc approaches, and that iterative refinement only helps when guided by external feedback. --- ### Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting Authors: Mert Kayaalp, Caner Turkmen, Oleksandr Shchur, Pedro Mercado, Abdul Fatir Ansari, Michael Bohlke-Schneider, Bernie Wang Published: 2025-10-07 Categories: cs.LG arXiv: https://arxiv.org/abs/2510.06419v2 Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio of smaller, pretrained forecasting models. By applying ensembling or model selection over these portfolios, we achieve competitive performance on large-scale benchmarks using much fewer parameters. We explore strategies for designing such portfolios and find that collections of specialist models consistently outperform portfolios of independently trained generalists. Remarkably, we demonstrate that post-training a base model is a compute-effective approach for creating sufficiently diverse specialists, and provide evidences that ensembling and model selection are more compute-efficient than test-time fine-tuning. --- ### Instructional Goal-Aligned Question Generation for Student Evaluation in Virtual Lab Settings: How Closely Do LLMs Actually Align? Authors: R. Alexander Knipper, Indrani Dey, Souvika Sarkar, Hari Narayanan, Sadhana Puntambekar, Santu Karmaker Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06411v1 Virtual Labs offer valuable opportunities for hands-on, inquiry-based science learning, yet teachers often struggle to adapt them to fit their instructional goals. Third-party materials may not align with classroom needs, and developing custom resources can be time-consuming and difficult to scale. Recent advances in Large Language Models (LLMs) offer a promising avenue for addressing these limitations. In this paper, we introduce a novel alignment framework for instructional goal-aligned question generation, enabling teachers to leverage LLMs to produce simulation-aligned, pedagogically meaningful questions through natural language interaction. The framework integrates four components: instructional goal understanding via teacher-LLM dialogue, lab understanding via knowledge unit and relationship analysis, a question taxonomy for structuring cognitive and pedagogical intent, and the TELeR taxonomy for controlling prompt detail. Early design choices were informed by a small teacher-assisted case study, while our final evaluation analyzed over 1,100 questions from 19 open-source LLMs. With goal and lab understanding grounding questions in teacher intent and simulation context, the question taxonomy elevates cognitive demand (open-ended formats and relational types raise quality by 0.29-0.39 points), and optimized TELeR prompts enhance format adherence (80% parsability, >90% adherence). Larger models yield the strongest gains: parsability +37.1%, adherence +25.7%, and average quality +0.8 Likert points. --- ### Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory? Authors: Aochong Oliver Li, Tanya Goyal Published: 2025-10-07 Categories: cs.AI arXiv: https://arxiv.org/abs/2510.06410v2 Reasoning LLMs are trained to verbalize their reasoning process, yielding strong gains on complex tasks. This transparency also opens a promising direction: multiple reasoners can directly collaborate on each other's thinking within a shared trajectory, yielding better inference efficiency and exploration. A key prerequisite, however, is the ability to assess the usefulness and build on another model's partial thinking -- we call this off-trajectory reasoning. Our paper investigates a critical question: can standard solo-reasoning training pipelines deliver desired off-trajectory behaviors? We propose twin tests that capture the two extremes of the off-trajectory spectrum, namely Recoverability, which tests whether LLMs can backtrack from "distractions" induced by misleading reasoning traces, and Guidability, which tests their ability to build upon correct reasoning from stronger collaborators. Our study evaluates 15 open-weight LLMs (1.5B-32B) and reveals a counterintuitive finding -- "stronger" LLMs on benchmarks are often more fragile under distraction. Moreover, all models tested fail to effectively leverage guiding steps from collaborators on problems beyond their inherent capabilities with solve rates remaining under 9.2%. Finally, we conduct control studies to isolate the effects of three factors in post-training on these behaviors: the choice of distillation teacher, the use of RL, and data selection strategy. Our results provide actionable insights for training natively strong reasoning collaborators; e.g., we find that suboptimal recoverability behaviors of teacher models are transferred to distilled students even if the distillation trajectories are correct. Taken together, this work lays the groundwork for evaluating multi-model collaborations in shared reasoning trajectories and highlights the limitations of off-the-shelf reasoning LLMs. --- ### The Effect of Label Noise on the Information Content of Neural Representations Authors: Ali Hussaini Umar, Franky Kevin Nando Tezoh, Jean Barbier, Santiago Acevedo, Alessandro Laio Published: 2025-10-07 Categories: cs.LG, cs.IT, cs.NE, stat.ML arXiv: https://arxiv.org/abs/2510.06401v1 In supervised classification tasks, models are trained to predict a label for each data point. In real-world datasets, these labels are often noisy due to annotation errors. While the impact of label noise on the performance of deep learning models has been widely studied, its effects on the networks' hidden representations remain poorly understood. We address this gap by systematically comparing hidden representations using the Information Imbalance, a computationally efficient proxy of conditional mutual information. Through this analysis, we observe that the information content of the hidden representations follows a double descent as a function of the number of network parameters, akin to the behavior of the test error. We further demonstrate that in the underparameterized regime, representations learned with noisy labels are more informative than those learned with clean labels, while in the overparameterized regime, these representations are equally informative. Our results indicate that the representations of overparameterized networks are robust to label noise. We also found that the information imbalance between the penultimate and pre-softmax layers decreases with cross-entropy loss in the overparameterized regime. This offers a new perspective on understanding generalization in classification tasks. Extending our analysis to representations learned from random labels, we show that these perform worse than random features. This indicates that training on random labels drives networks much beyond lazy learning, as weights adapt to encode labels information. --- ### Geometry-Aware Backdoor Attacks: Leveraging Curvature in Hyperbolic Embeddings Authors: Ali Baheri Published: 2025-10-07 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2510.06397v1 Non-Euclidean foundation models increasingly place representations in curved spaces such as hyperbolic geometry. We show that this geometry creates a boundary-driven asymmetry that backdoor triggers can exploit. Near the boundary, small input changes appear subtle to standard input-space detectors but produce disproportionately large shifts in the model's representation space. Our analysis formalizes this effect and also reveals a limitation for defenses: methods that act by pulling points inward along the radius can suppress such triggers, but only by sacrificing useful model sensitivity in that same direction. Building on these insights, we propose a simple geometry-adaptive trigger and evaluate it across tasks and architectures. Empirically, attack success increases toward the boundary, whereas conventional detectors weaken, mirroring the theoretical trends. Together, these results surface a geometry-specific vulnerability in non-Euclidean models and offer analysis-backed guidance for designing and understanding the limits of defenses. --- ### Adaptive Protein Design Protocols and Middleware Authors: Aymen Alsaadi, Jonathan Ash, Mikhail Titov, Matteo Turilli, Andre Merzky, Shantenu Jha, Sagar Khare Published: 2025-10-07 Categories: cs.DC, cs.AI, cs.PF, cs.SE arXiv: https://arxiv.org/abs/2510.06396v1 Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial computational resources for sampling. The Integrated Machine-learning for Protein Structures at Scale (IMPRESS) offers methods and advanced computing systems for coupling AI to high-performance computing tasks, enabling the ability to evaluate the effectiveness of protein designs as they are developed, as well as the models and simulations used to generate data and train models. This paper introduces IMPRESS and demonstrates the development and implementation of an adaptive protein design protocol and its supporting computing infrastructure. This leads to increased consistency in the quality of protein design and enhanced throughput of protein design due to dynamic resource allocation and asynchronous workload execution. --- ### Reward Model Perspectives: Whose Opinions Do Reward Models Reward? Authors: Elle Published: 2025-10-07 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2510.06391v1 Reward models (RMs) are central to the alignment of language models (LMs). An RM often serves as a proxy for human preferences to guide downstream LM behavior. However, our understanding of RM behavior is limited. Our work (i) formalizes a framework for measuring the alignment of opinions captured by RMs, (ii) investigates the extent to which RMs demonstrate sociodemographic biases, and (iii) explores the effects of prompting to steer rewards towards the preferences of a target group. We study the subjective and diverse perspectives on controversial topics, which allows us to quantify RM perspectives in terms of their opinions, attitudes, and values. We show that RMs are poorly aligned with several demographic groups and can systematically reward harmful stereotypes, and steering alone is not enough to overcome these limitations. Our findings underscore the need for more careful consideration of RM behavior in model alignment during preference learning to prevent the propagation of unwanted social biases in the language technologies that we use. --- ### Truthful Calibration Errors for Multi-Class Prediction Authors: Yuxuan Lu, Yifan Wu, Jason Hartline, Lunjia Hu Published: 2025-10-07 Categories: cs.LG, cs.DS, stat.ML arXiv: https://arxiv.org/abs/2510.06388v2 Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used to evaluate, compare, and tune probabilistic predictors. Recently, Haghtalab et al. (2024) introduced an additional requirement for such measures: truthfulness. A calibration measure is truthful if a predictor minimizes its expected measured error by reporting the true conditional label distribution. Many standard empirical calibration errors are non-truthful: a predictor may appear better calibrated by distorting its probabilities rather than reporting them truthfully. We study the practical role of truthfulness for calibration measurement in multiclass prediction. First, we introduce perfectly truthful calibration errors for multidimensional linear properties of the label distribution, generalizing the truthful calibration error for binary predictions in Hartline et al. (2025). This framework includes full multiclass calibration and classwise calibration. We also identify a truthful correction for confidence calibration. Second, we characterize the decision-theoretic implications of these truthful errors. For calibrated predictors, truthful calibration errors preserve the Blackwell dominance: a more informative calibrated predictor receives no larger expected error. Third, we show that this decision-theoretic interpretation explains and mitigates the well-observed ranking robustness problem of binned calibration errors. Empirically, non-truthful confidence-based errors can reverse model rankings when the number of bins changes, while our truthful errors give more stable rankings across binning choices. --- ### Controllable Stylistic Text Generation with Train-Time Attribute-Regularized Diffusion Authors: Fan Zhou, Chang Tian, Tim Van de Cruys Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06386v1 Generating stylistic text with specific attributes is a key problem in controllable text generation. Recently, diffusion models have emerged as a powerful paradigm for both visual and textual generation. Existing approaches can be broadly categorized into classifier-free guidance (CFG) and classifier guidance (CG) methods. While CFG effectively preserves semantic content, it often fails to provide effective attribute control. In contrast, CG modifies the denoising trajectory using classifier gradients, enabling better attribute alignment but incurring high computational costs during sampling and suffering from classifier generalization issues. In this work, we propose RegDiff, a regularized diffusion framework that leverages attribute features without requiring a pretrained classifier during sampling, thereby achieving controllable generation with reduced computational costs. Specifically, RegDiff employs a VAE-based encoder--decoder architecture to ensure reconstruction fidelity and a latent diffusion model trained with attribute supervision to enable controllable text generation. Attribute information is injected only during training. Experiments on five datasets spanning multiple stylistic attributes demonstrate that RegDiff outperforms strong baselines in generating stylistic texts. These results validate the effectiveness of RegDiff as an efficient solution for attribute-controllable text diffusion. Our code, datasets, and resources will be released upon publication at https://github.com/xxxx. --- ### Protecting De-identified Documents from Search-based Linkage Attacks Authors: Pierre Lison, Mark Anderson Published: 2025-10-07 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2510.06383v2 While de-identification models can help conceal the identity of the individuals mentioned in a document, they fail to address linkage risks, defined as the potential to map the de-identified text back to its source. One straightforward way to perform such linkages is to extract phrases from the de-identified document and check their presence in the original dataset. This paper presents a method to counter search-based linkage attacks while preserving the semantic integrity of the text. The method proceeds in two steps. We first construct an inverted index of the N-grams occurring in the text collection, making it possible to efficiently determine which N-grams appear in fewer than $k$ documents, either alone or in combination with other N-grams. An LLM-based rewriter is then iteratively queried to reformulate those spans until linkage is no longer possible. Experimental results on two datasets (court cases and Wikipedia biographies) show that the rewriting method can effectively prevent search-based linkages while remaining faithful to the original content. However, we also highlight that linkages remain feasible with the help of more advanced, semantics-oriented approaches. --- ### Monte Carlo Permutation Search Authors: Tristan Cazenave Published: 2025-10-07 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2510.06381v2 We propose Monte Carlo Permutation Search (MCPS), a general-purpose Monte Carlo Tree Search (MCTS) algorithm that improves upon the GRAVE algorithm. MCPS is relevant when deep reinforcement learning is not an option or when the computing power available before play is not substantial, such as in General Game Playing. The principle of MCPS is to include in the exploration term of a node the statistics on all the playouts that contain all the moves on the path from the root to the node. We test MCPS on a variety of games: Hex, Go, AtariGo, NoGo and a Wargame. MCPS almost always outperforms GRAVE. We also provide a mathematical derivation of the formulas used for weighting the three sources of statistics. These formulas are an improvement on the GRAVE formula since they no longer use the bias hyperparameter of GRAVE. --- ### Semantic Regexes: Auto-Interpreting LLM Features with a Structured Language Authors: Angie Boggust, Donghao Ren, Yannick Assogba, Dominik Moritz, Arvind Satyanarayan, Fred Hohman Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06378v2 Automated interpretability aims to translate large language model (LLM) features into human understandable descriptions. However, natural language feature descriptions can be vague, inconsistent, and require manual relabeling. In response, we introduce semantic regexes, structured language descriptions of LLM features. By combining primitives that capture linguistic and semantic patterns with modifiers for contextualization, composition, and quantification, semantic regexes produce precise and expressive feature descriptions. Across quantitative benchmarks and qualitative analyses, semantic regexes match the accuracy of natural language while yielding more concise and consistent feature descriptions. Their inherent structure affords new types of analyses, including quantifying feature complexity across layers, scaling automated interpretability from insights into individual features to model-wide patterns. Finally, in user studies, we find that semantic regexes help people build accurate mental models of LLM features. --- ### Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data Authors: Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos Kanatsoulis, Roshan Upendra, Mahmoud Mohammadi, Joe Meyer, Tom Palczewski, Carlos Guestrin, Jure Leskovec Published: 2025-10-07 Categories: cs.LG, cs.AI, cs.DB arXiv: https://arxiv.org/abs/2510.06377v3 Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and tasks. The core challenge is the diversity of relational data, with varying heterogeneous schemas, graph structures and functional dependencies. In this paper, we present the Relational Transformer (RT) architecture, which can be pretrained on diverse relational databases and directly applied to unseen datasets and tasks without task- or dataset-specific fine-tuning, or retrieval of in-context examples. RT (i) incorporates task specification via task table prompting, (ii) tokenizes cells with table/column metadata, (iii) is pretrained via masked token prediction, and (iv) utilizes a novel Relational Attention mechanism over columns, rows, and primary-foreign key links. Pretrained on RelBench datasets spanning tasks such as churn and sales forecasting, RT attains strong zero-shot performance, averaging 93% of fully supervised AUROC on binary classification tasks with a single forward pass of a 22M parameter model, as opposed to 84% for a 27B LLM. Fine-tuning yields state-of-the-art results with high sample efficiency. Our experimental analyses show that RT's zero-shot transfer leverages task context, relational attention patterns and schema semantics. Overall, RT provides a practical path toward foundation models for relational data. Code, models, data: https://github.com/snap-stanford/relational-transformer. --- ### A General Constructive Upper Bound on Shallow Neural Nets Complexity Authors: Frantisek Hakl, Vit Fojtik Published: 2025-10-07 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2510.06372v1 We provide an upper bound on the number of neurons required in a shallow neural network to approximate a continuous function on a compact set with a given accuracy. This method, inspired by a specific proof of the Stone-Weierstrass theorem, is constructive and more general than previous bounds of this character, as it applies to any continuous function on any compact set. --- ### OASIS: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA Authors: Firoj Alam, Ali Ezzat Shahroor, Md. Arid Hasan, Zien Sheikh Ali, Hunzalah Hassan Bhatti, Mohamed Bayan Kmainasi, Shammur Absar Chowdhury, Basel Mousi, Fahim Dalvi, Nadir Durrani, Natasa Milic-Frayling Published: 2025-10-07 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2510.06371v3 Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they are often limited when queries require cultural and visual information, everyday knowledge, particularly in low-resource and underrepresented languages. We introduce OASIS, a large-scale culturally grounded multimodal QA dataset covering images, text, and speech. OASIS is built with EverydayMMQA, a scalable semi-automatic framework for creating localized spoken and visual QA resources, supported by multi-stage human-in-the-loop validation. OASIS contains approximately 0.92M real images and 14.8M QA pairs, including 3.7M spoken questions, with 383 hours of human-recorded speech, and 20K hours of voice-cloned speech, from 42 speakers. It supports four input settings: text-only, speech-only, text+image, and speech+image. The dataset focuses on English and Arabic varieties across 18 countries, covering Modern Standard Arabic (MSA) as well as dialectal Arabic. It is designed to evaluate models beyond object recognition, targeting pragmatic, commonsense, and culturally grounded reasoning in real-world scenarios. We benchmark four closed-source models, three open-source models, and one fine-tuned model on OASIS. The framework and dataset will be made publicly available to the community. https://huggingface.co/datasets/QCRI/OASIS --- ### EVALUESTEER: Measuring Reward Model Steerability Towards Values and Preferences Authors: Kshitish Ghate, Andy Liu, Devansh Jain, Taylor Sorensen, Atoosa Kasirzadeh, Aylin Caliskan, Mona T. Diab, Maarten Sap Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06370v2 As large language models (LLMs) are deployed globally, creating pluralistic systems that can accommodate the diverse preferences and values of users worldwide becomes essential. We introduce EVALUESTEER, a benchmark to measure LLMs' and reward models' (RMs) steerability towards users' value and stylistic preference profiles grounded in psychology and human-LLM interaction literature. To address the gap in existing datasets that do not support controlled evaluations of RM steering, we synthetically generated 165,888 preference pairs -- systematically varying pairs along 4 value dimensions (traditional, secular-rational, survival, and self-expression) and 4 style dimensions (verbosity, readability, confidence, and warmth). We use EVALUESTEER to evaluate whether, given a user profile and a pair of candidate value-laden and style-laden responses, LLMs and RMs are able to select the output that aligns with the user's preferences. We evaluate six open-source and proprietary LLMs and RMs under eleven systematic prompting conditions and six preference comparison scenarios. Notably, our results show that, when given the user's full profile of values and stylistic preferences, the best models achieve <75% accuracy at choosing the correct response, in contrast to >99% accuracy when only relevant style and value preferences are provided. EVALUESTEER thus highlights the limitations of current RMs at identifying and adapting to relevant user profile information, and provides a challenging testbed for developing RMs that can be steered towards diverse human values and preferences. --- ### Lagrangian neural ODEs: Measuring the existence of a Lagrangian with Helmholtz metrics Authors: Luca Wolf, Tobias Buck, Bjoern Malte Schaefer Published: 2025-10-07 Categories: cs.LG, math.DS, physics.comp-ph, physics.data-an arXiv: https://arxiv.org/abs/2510.06367v2 Neural ODEs are a widely used, powerful machine learning technique in particular for physics. However, not every solution is physical in that it is an Euler-Lagrange equation. We present Helmholtz metrics to quantify this resemblance for a given ODE and demonstrate their capabilities on several fundamental systems with noise. We combine them with a second order neural ODE to form a Lagrangian neural ODE, which allows to learn Euler-Lagrange equations in a direct fashion and with zero additional inference cost. We demonstrate that, using only positional data, they can distinguish Lagrangian and non-Lagrangian systems and improve the neural ODE solutions. --- ### Diffusion-Guided Renormalization of Neural Systems via Tensor Networks Authors: Nathan X. Kodama Published: 2025-10-07 Categories: q-bio.NC, cond-mat.stat-mech, cs.LG arXiv: https://arxiv.org/abs/2510.06361v1 Far from equilibrium, neural systems self-organize across multiple scales. Exploiting multiscale self-organization in neuroscience and artificial intelligence requires a computational framework for modeling the effective non-equilibrium dynamics of stochastic neural trajectories. Non-equilibrium thermodynamics and representational geometry offer theoretical foundations, but we need scalable data-driven techniques for modeling collective properties of high-dimensional neural networks from partial subsampled observations. Renormalization is a coarse-graining technique central to studying emergent scaling properties of many-body and nonlinear dynamical systems. While widely applied in physics and machine learning, coarse-graining complex dynamical networks remains unsolved, affecting many computational sciences. Recent diffusion-based renormalization, inspired by quantum statistical mechanics, coarse-grains networks near entropy transitions marked by maximal changes in specific heat or information transmission. Here I explore diffusion-based renormalization of neural systems by generating symmetry-breaking representations across scales and offering scalable algorithms using tensor networks. Diffusion-guided renormalization bridges microscale and mesoscale dynamics of dissipative neural systems. For microscales, I developed a scalable graph inference algorithm for discovering community structure from subsampled neural activity. Using community-based node orderings, diffusion-guided renormalization generates renormalization group flow through metagraphs and joint probability functions. Towards mesoscales, diffusion-guided renormalization targets learning the effective non-equilibrium dynamics of dissipative neural trajectories occupying lower-dimensional subspaces, enabling coarse-to-fine control in systems neuroscience and artificial intelligence. --- ### Constrained Natural Language Action Planning for Resilient Embodied Systems Authors: Grayson Byrd, Corban Rivera, Bethany Kemp, Meghan Booker, Aurora Schmidt, Celso M de Melo, Lalithkumar Seenivasan, Mathias Unberath Published: 2025-10-07 Categories: cs.RO, cs.AI arXiv: https://arxiv.org/abs/2510.06357v1 Replicating human-level intelligence in the execution of embodied tasks remains challenging due to the unconstrained nature of real-world environments. Novel use of large language models (LLMs) for task planning seeks to address the previously intractable state/action space of complex planning tasks, but hallucinations limit their reliability, and thus, viability beyond a research context. Additionally, the prompt engineering required to achieve adequate system performance lacks transparency, and thus, repeatability. In contrast to LLM planning, symbolic planning methods offer strong reliability and repeatability guarantees, but struggle to scale to the complexity and ambiguity of real-world tasks. We introduce a new robotic planning method that augments LLM planners with symbolic planning oversight to improve reliability and repeatability, and provide a transparent approach to defining hard constraints with considerably stronger clarity than traditional prompt engineering. Importantly, these augmentations preserve the reasoning capabilities of LLMs and retain impressive generalization in open-world environments. We demonstrate our approach in simulated and real-world environments. On the ALFWorld planning benchmark, our approach outperforms current state-of-the-art methods, achieving a near-perfect 99% success rate. Deployment of our method to a real-world quadruped robot resulted in 100% task success compared to 50% and 30% for pure LLM and symbolic planners across embodied pick and place tasks. Our approach presents an effective strategy to enhance the reliability, repeatability and transparency of LLM-based robot planners while retaining their key strengths: flexibility and generalizability to complex real-world environments. We hope that this work will contribute to the broad goal of building resilient embodied intelligent systems. --- ### PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling Authors: Kürşat Tekbıyık, Güneş Karabulut Kurt, Antoine Lesage-Landry Published: 2025-10-07 Categories: cs.LG, eess.SP arXiv: https://arxiv.org/abs/2510.06355v2 Unmanned aerial vehicle (UAV) communications demand accurate yet interpretable air-to-ground (A2G) channel models that can adapt to nonstationary propagation environments. While deterministic models offer interpretability and deep learning (DL) models provide accuracy, both approaches suffer from either rigidity or a lack of explainability. To bridge this gap, we propose the Physics-Inspired Kolmogorov-Arnold Network (PIKAN) that embeds physical principles (e.g., free-space path loss, two-ray reflections) into the learning process. Unlike physics-informed neural networks (PINNs), PIKAN is more flexible for applying physical information because it introduces them as flexible inductive biases. Thus, it enables a more flexible training process. Experiments on UAV A2G measurement data show that PIKAN achieves comparable accuracy to DL models while providing symbolic and explainable expressions aligned with propagation laws. Remarkably, PIKAN achieves this performance with only 232 parameters, making it up to 37 times lighter than multilayer perceptron (MLP) baselines with thousands of parameters, without sacrificing correlation with measurements and also providing symbolic expressions. These results highlight PIKAN as an efficient, interpretable, and scalable solution for UAV channel modelling in beyond-5G and 6G networks. --- ### LLM Bias Detection and Mitigation through the Lens of Desired Distributions Authors: Ingroj Shrestha, Padmini Srinivasan Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06354v1 Although prior work on bias mitigation has focused on promoting social equality and demographic parity, less attention has been given to aligning LLM's outputs to desired distributions. For example, we might want to align a model with real-world distributions to support factual grounding. Thus, we define bias as deviation from a desired distribution, which may be an equal or real-world distribution, depending on application goals. We propose a weighted adaptive loss based fine-tuning method that aligns LLM's gender-profession output distribution with the desired distribution, while preserving language modeling capability. Using 3 profession sets -- male-dominated, female-dominated, and gender-balanced -- derived from U.S. labor statistics (2024), we assess both our adaptive method for reflecting reality and a non-adaptive variant for equality. Across three masked language models, bias is observed under both distributions. We achieve near-complete mitigation under equality and 30-75% reduction under real-world settings. Autoregressive LLMs show no bias under equality but notable bias under real-world settings, with the Llama Instruct models (3.2-3B, 3.1-8B) achieving a 50-62% reduction. --- ### TransFIRA: Transfer Learning for Face Image Recognizability Assessment Authors: Allen Tu, Kartik Narayan, Joshua Gleason, Jennifer Xu, Matthew Meyn, Tom Goldstein, Vishal M. Patel Published: 2025-10-07 Categories: cs.CV, cs.AI, cs.LG arXiv: https://arxiv.org/abs/2510.06353v2 Face recognition in unconstrained environments such as surveillance, video, and web imagery must contend with extreme variation in pose, blur, illumination, and occlusion, where conventional visual quality metrics fail to predict whether inputs are truly recognizable to the deployed encoder. Existing FIQA methods typically rely on visual heuristics, curated annotations, or computationally intensive generative pipelines, leaving their predictions detached from the encoder's decision geometry. We introduce TransFIRA (Transfer Learning for Face Image Recognizability Assessment), a lightweight and annotation-free framework that grounds recognizability directly in embedding space. TransFIRA delivers three advances: (i) a definition of recognizability via class-center similarity (CCS) and class-center angular separation (CCAS), yielding the first natural, decision-boundary-aligned criterion for filtering and weighting; (ii) a recognizability-informed aggregation strategy that achieves state-of-the-art verification accuracy on BRIAR and IJB-C while nearly doubling correlation with true recognizability, all without external labels, heuristics, or backbone-specific training; and (iii) new extensions beyond faces, including encoder-grounded explainability that reveals how degradations and subject-specific factors affect recognizability, and the first method for body recognizability assessment. Experiments confirm state-of-the-art results on faces, strong performance on body recognition, and robustness under cross-dataset shifts and out-of-distribution evaluation. Together, these contributions establish TransFIRA as a unified, geometry-driven framework for recognizability assessment that is encoder-specific, accurate, interpretable, and extensible across modalities, significantly advancing FIQA in accuracy, explainability, and scope. --- ### Asking For It: Question-Answering for Predicting Rule Infractions in Online Content Moderation Authors: Mattia Samory, Diana Pamfile, Andrew To, Shruti Phadke Published: 2025-10-07 Categories: cs.CY, cs.AI, cs.CL, cs.HC, cs.LG arXiv: https://arxiv.org/abs/2510.06350v1 Online communities rely on a mix of platform policies and community-authored rules to define acceptable behavior and maintain order. However, these rules vary widely across communities, evolve over time, and are enforced inconsistently, posing challenges for transparency, governance, and automation. In this paper, we model the relationship between rules and their enforcement at scale, introducing ModQ, a novel question-answering framework for rule-sensitive content moderation. Unlike prior classification or generation-based approaches, ModQ conditions on the full set of community rules at inference time and identifies which rule best applies to a given comment. We implement two model variants - extractive and multiple-choice QA - and train them on large-scale datasets from Reddit and Lemmy, the latter of which we construct from publicly available moderation logs and rule descriptions. Both models outperform state-of-the-art baselines in identifying moderation-relevant rule violations, while remaining lightweight and interpretable. Notably, ModQ models generalize effectively to unseen communities and rules, supporting low-resource moderation settings and dynamic governance environments. --- ### Flexible Swarm Learning May Outpace Foundation Models in Essential Tasks Authors: Moein E. Samadi, Andreas Schuppert Published: 2025-10-07 Categories: cs.LG, cs.AI, cs.MA arXiv: https://arxiv.org/abs/2510.06349v2 Foundation models have rapidly advanced AI, raising the question of whether their decisions will ultimately surpass human strategies in real-world domains. The exponential, and possibly super-exponential, pace of AI development makes such analysis elusive. Nevertheless, many application areas that matter for daily life and society show only modest gains so far; a prominent case is diagnosing and treating dynamically evolving disease in intensive care. The common challenge is adapting complex systems to dynamic environments. Effective strategies must optimize outcomes in systems composed of strongly interacting functions while avoiding shared side effects; this requires reliable, self-adaptive modeling. These tasks align with building digital twins of highly complex systems whose mechanisms are not fully or quantitatively understood. It is therefore essential to develop methods for self-adapting AI models with minimal data and limited mechanistic knowledge. As this challenge extends beyond medicine, AI should demonstrate clear superiority in these settings before assuming broader decision-making roles. We identify the curse of dimensionality as a fundamental barrier to efficient self-adaptation and argue that monolithic foundation models face conceptual limits in overcoming it. As an alternative, we propose a decentralized architecture of interacting small agent networks (SANs). We focus on agents representing the specialized substructure of the system, where each agent covers only a subset of the full system functions. Drawing on mathematical results on the learning behavior of SANs and evidence from existing applications, we argue that swarm-learning in diverse swarms can enable self-adaptive SANs to deliver superior decision-making in dynamic environments compared with monolithic foundation models, though at the cost of reduced reproducibility in detail. --- ### Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systems Authors: Fikret Mert Gultekin, Oscar Lilja, Ranim Khojah, Rebekka Wohlrab, Marvin Damschen, Mazen Mohamad Published: 2025-10-07 Categories: cs.SE, cs.AI, cs.CR arXiv: https://arxiv.org/abs/2510.06343v2 In safety-critical software systems, cybersecurity activities become essential, with risk assessment being one of the most critical. In many software teams, cybersecurity experts are either entirely absent or represented by only a small number of specialists. As a result, the workload for these experts becomes high, and software engineers would need to conduct cybersecurity activities themselves. This creates a need for a tool to support cybersecurity experts and engineers in evaluating vulnerabilities and threats during the risk assessment process. This paper explores the potential of leveraging locally hosted large language models (LLMs) with retrieval-augmented generation to support cybersecurity risk assessment in the forestry domain while complying with data protection and privacy requirements that limit external data sharing. We performed a design science study involving 12 experts in interviews, interactive sessions, and a survey within a large-scale project. The results demonstrate that LLMs can assist cybersecurity experts by generating initial risk assessments, identifying threats, and providing redundancy checks. The results also highlight the necessity for human oversight to ensure accuracy and compliance. Despite trust concerns, experts were willing to utilize LLMs in specific evaluation and assistance roles, rather than solely relying on their generative capabilities. This study provides insights that encourage the use of LLM-based agents to support the risk assessment process of cyber-physical systems in safety-critical domains. --- ### Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data Authors: Mohammed Alsubaie, Wenxi Liu, Linxia Gu, Ovidiu C. Andronesi, Sirani M. Perera, Xianqi Li Published: 2025-10-07 Categories: eess.IV, cs.CV, cs.LG arXiv: https://arxiv.org/abs/2510.06335v1 Magnetic Resonance Imaging (MRI) is a critical tool in modern medical diagnostics, yet its prolonged acquisition time remains a critical limitation, especially in time-sensitive clinical scenarios. While undersampling strategies can accelerate image acquisition, they often result in image artifacts and degraded quality. Recent diffusion models have shown promise for reconstructing high-fidelity images from undersampled data by learning powerful image priors; however, most existing approaches either (i) rely on unsupervised score functions without paired supervision or (ii) apply data consistency only as a post-processing step. In this work, we introduce a conditional denoising diffusion framework with iterative data-consistency correction, which differs from prior methods by embedding the measurement model directly into every reverse diffusion step and training the model on paired undersampled-ground truth data. This hybrid design bridges generative flexibility with explicit enforcement of MRI physics. Experiments on the fastMRI dataset demonstrate that our framework consistently outperforms recent state-of-the-art deep learning and diffusion-based methods in SSIM, PSNR, and LPIPS, with LPIPS capturing perceptual improvements more faithfully. These results demonstrate that integrating conditional supervision with iterative consistency updates yields substantial improvements in both pixel-level fidelity and perceptual realism, establishing a principled and practical advance toward robust, accelerated MRI reconstruction. --- ### Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding Authors: Yi Xin, Qi Qin, Siqi Luo, Kaiwen Zhu, Juncheng Yan, Yan Tai, Jiayi Lei, Yuewen Cao, Keqi Wang, Yibin Wang, Jinbin Bai, Qian Yu et al. Published: 2025-10-07 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.06308v1 We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by utilizing a fully discrete diffusion modeling to handle inputs and outputs across various modalities. This innovative approach allows Lumina-DiMOO to achieve higher sampling efficiency compared to previous autoregressive (AR) or hybrid AR-Diffusion paradigms and adeptly support a broad spectrum of multi-modal tasks, including text-to-image generation, image-to-image generation (e.g., image editing, subject-driven generation, and image inpainting, etc.), as well as image understanding. Lumina-DiMOO achieves state-of-the-art performance on multiple benchmarks, surpassing existing open-source unified multi-modal models. To foster further advancements in multi-modal and discrete diffusion model research, we release our code and checkpoints to the community. Project Page: https://synbol.github.io/Lumina-DiMOO. --- ### Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks Authors: Wentao Deng, Jiahuan Pei, Zhiwei Xu, Zhaochun Ren, Zhumin Chen, Pengjie Ren Published: 2025-10-07 Categories: cs.AI arXiv: https://arxiv.org/abs/2510.06307v1 A multi-agent system (MAS) enhances its capacity to solve complex natural language processing (NLP) tasks through collaboration among multiple agents, where consensus-seeking serves as a fundamental mechanism. However, existing consensus-seeking approaches typically rely on voting mechanisms to judge consensus, overlooking contradictions in system-internal beliefs that destabilize the consensus. Moreover, these methods often involve agents updating their results through indiscriminate collaboration with every other agent. Such uniform interaction fails to identify the optimal collaborators for each agent, hindering the emergence of a stable consensus. To address these challenges, we provide a theoretical framework for selecting optimal collaborators that maximize consensus stability. Based on the theorems, we propose the Belief-Calibrated Consensus Seeking (BCCS) framework to facilitate stable consensus via selecting optimal collaborators and calibrating the consensus judgment by system-internal beliefs. Experimental results on the MATH and MMLU benchmark datasets demonstrate that the proposed BCCS framework outperforms the best existing results by 2.23% and 3.95% of accuracy on challenging tasks, respectively. Our code and data are available at https://github.com/dengwentao99/BCCS. --- ### Type and Complexity Signals in Multilingual Question Representations Authors: Robin Kokot, Wessel Poelman Published: 2025-10-07 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.06304v1 This work investigates how a multilingual transformer model represents morphosyntactic properties of questions. We introduce the Question Type and Complexity (QTC) dataset with sentences across seven languages, annotated with type information and complexity metrics including dependency length, tree depth, and lexical density. Our evaluation extends probing methods to regression labels with selectivity controls to quantify gains in generalizability. We compare layer-wise probes on frozen Glot500-m (Imani et al., 2023) representations against subword TF-IDF baselines, and a fine-tuned model. Results show that statistical features classify questions effectively in languages with explicit marking, while neural probes capture fine-grained structural complexity patterns better. We use these results to evaluate when contextual representations outperform statistical baselines and whether parameter updates reduce the availability of pre-trained linguistic information. --- ### SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation Authors: Shuang Cheng, Yihan Bian, Dawei Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou Published: 2025-10-07 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2510.06303v3 We propose SDAR, a Synergistic Diffusion-Autoregression paradigm that unifies the training efficiency of autoregressive models with the parallel inference capability of diffusion. Instead of costly end-to-end diffusion training, SDAR performs a lightweight paradigm conversion that transforms a well-trained autoregressive (AR) model into a blockwise diffusion model through brief, data-efficient adaptation. During inference, SDAR generates sequences autoregressively across blocks for global coherence while decoding all tokens within each block in parallel via a discrete diffusion process. Extensive experiments show that AR models remain substantially more compute-efficient than masked diffusion models, providing a strong foundation for adaptation. Building on this insight, SDAR achieves efficient AR-to-diffusion conversion with minimal cost, preserving AR-level performance while enabling parallel generation. Scaling studies across dense and Mixture-of-Experts architectures confirm that SDAR scales without compromise: larger models exhibit stronger robustness to block size and decoding thresholds, yielding greater speedups without accuracy loss. Beyond efficiency, SDAR demonstrates enhanced reasoning and domain adaptability. Our 30B MoE model surpasses its AR counterpart on challenging scientific reasoning benchmarks such as GPQA and ChemBench, and gains further improvements under test-time scaling methods like majority voting and pass@k. Together, these results establish SDAR as a practical paradigm that combines the strengths of autoregression and diffusion for scalable, high-throughput reasoning. --- ### Requirements for Game-Based Learning Design Framework for Information System Integration in the Context of Post-Merger Integration Authors: Ksenija Lace, Marite Kirikova Published: 2025-10-07 Categories: cs.AI arXiv: https://arxiv.org/abs/2510.06302v1 Post-merger integration states unique challenges for professionals responsible for information system integration aimed on alignment and combination diverse system architectures of merging organizations. Although the theoretical and practical guidance exists for post-merger integration on the business level, there is a significant gap in training for information system integration in this context. In prior research specific methods AMILI (Support method for informed decision identification) and AMILP (Support method for informed decision-making) were introduced for the support of information system integration decisions in the post-merger integration. But during the practical application was reported high learning curve and low learner motivation. This paper explores how game-based learning design can address these limitations by transforming static method training into engaging learning experience. The study analyzes foundational learning theories, cognitive load and motivation models, and serious game design frameworks to identify the essential requirements for a game-based learning design framework tailored to information system integration in post-merger integration. Requirements are structured in two components: the transformation process and resulting learning experience. The paper concludes with a plan for developing and evaluating the proposed framework through iterative design and real-world validation. --- ### Scalable deep fusion of spaceborne lidar and synthetic aperture radar for global forest structural complexity mapping Authors: Tiago de Conto, John Armston, Ralph Dubayah Published: 2025-10-07 Categories: cs.CV, cs.LG, stat.AP arXiv: https://arxiv.org/abs/2510.06299v1 Forest structural complexity metrics integrate multiple canopy attributes into a single value that reflects habitat quality and ecosystem function. Spaceborne lidar from the Global Ecosystem Dynamics Investigation (GEDI) has enabled mapping of structural complexity in temperate and tropical forests, but its sparse sampling limits continuous high-resolution mapping. We present a scalable, deep learning framework fusing GEDI observations with multimodal Synthetic Aperture Radar (SAR) datasets to produce global, high-resolution (25 m) wall-to-wall maps of forest structural complexity. Our adapted EfficientNetV2 architecture, trained on over 130 million GEDI footprints, achieves high performance (global R2 = 0.82) with fewer than 400,000 parameters, making it an accessible tool that enables researchers to process datasets at any scale without requiring specialized computing infrastructure. The model produces accurate predictions with calibrated uncertainty estimates across biomes and time periods, preserving fine-scale spatial patterns. It has been used to generate a global, multi-temporal dataset of forest structural complexity from 2015 to 2022. Through transfer learning, this framework can be extended to predict additional forest structural variables with minimal computational cost. This approach supports continuous, multi-temporal monitoring of global forest structural dynamics and provides tools for biodiversity conservation and ecosystem management efforts in a changing climate. --- ### RGBD Gaze Tracking Using Transformer for Feature Fusion Authors: Tobias J. Bauer Published: 2025-10-07 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2510.06298v1 Subject of this thesis is the implementation of an AI-based Gaze Tracking system using RGBD images that contain both color (RGB) and depth (D) information. To fuse the features extracted from the images, a module based on the Transformer architecture is used. The combination of RGBD input images and Transformers was chosen because it has not yet been investigated. Furthermore, a new dataset is created for training the AI models as existing datasets either do not contain depth information or only contain labels for Gaze Point Estimation that are not suitable for the task of Gaze Angle Estimation. Various model configurations are trained, validated and evaluated on a total of three different datasets. The trained models are then to be used in a real-time pipeline to estimate the gaze direction and thus the gaze point of a person in front of a computer screen. The AI model architecture used in this thesis is based on an earlier work by Lian et al. It uses a Generative Adversarial Network (GAN) to simultaneously remove depth map artifacts and extract head pose features. Lian et al. achieve a mean Euclidean error of 38.7mm on their own dataset ShanghaiTechGaze+. In this thesis, a model architecture with a Transformer module for feature fusion achieves a mean Euclidean error of 55.3mm on the same dataset, but we show that using no pre-trained GAN module leads to a mean Euclidean error of 30.1mm. Replacing the Transformer module with a Multilayer Perceptron (MLP) improves the error to 26.9mm. These results are coherent with the ones on the other two datasets. On the ETH-XGaze dataset, the model with Transformer module achieves a mean angular error of 3.59° and without Transformer module 3.26°, whereas the fundamentally different model architecture used by the dataset authors Zhang et al. achieves a mean angular error of 2.04°. On the OTH-Gaze-Estimation dataset created for... --- ### VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally Verifiable Code Authors: Lingfei Zeng, Fengdi Che, Xuhan Huang, Fei Ye, Xu Xu, Binhang Yuan, Jie Fu Published: 2025-10-07 Categories: cs.PL, cs.AI arXiv: https://arxiv.org/abs/2510.06296v3 Formal verification is the next frontier for ensuring the correctness of code generated by Large Language Models (LLMs). While methods that co-generate code and formal specifications in formal languages, like Dafny, can, in principle, prove alignment with user intent, progress is bottlenecked by specification quality evaluation. Current benchmarks rely on matching against ground-truth specifications, a manual and expertise-intensive process that has limited existing datasets to a few hundred simple problems and also suffers from a reliability issue. To address this, we introduce VeriEquivBench, a new benchmark with $2,389$ complex algorithmic problems that probe the limitations of current models in both code generation and formal reasoning. Our evaluation framework replaces ground-truth matching with a formally grounded metric, the equivalence score, and rigorously verifies the quality of generated specifications and code. Our results show that generating formally verifiable code remains a profound challenge for state-of-the-art LLMs. This underscores both the difficulty of the task and the need for benchmarks like VeriEquivBench to drive progress toward scalable and reliable coding agents. --- ### Efficient High-Resolution Image Editing with Hallucination-Aware Loss and Adaptive Tiling Authors: Young D. Kwon, Abhinav Mehrotra, Malcolm Chadwick, Alberto Gil Ramos, Sourav Bhattacharya Published: 2025-10-07 Categories: cs.CV, cs.AI, cs.LG arXiv: https://arxiv.org/abs/2510.06295v1 High-resolution (4K) image-to-image synthesis has become increasingly important for mobile applications. Existing diffusion models for image editing face significant challenges, in terms of memory and image quality, when deployed on resource-constrained devices. In this paper, we present MobilePicasso, a novel system that enables efficient image editing at high resolutions, while minimising computational cost and memory usage. MobilePicasso comprises three stages: (i) performing image editing at a standard resolution with hallucination-aware loss, (ii) applying latent projection to overcome going to the pixel space, and (iii) upscaling the edited image latent to a higher resolution with adaptive context-preserving tiling. Our user study with 46 participants reveals that MobilePicasso not only improves image quality by 18-48% but reduces hallucinations by 14-51% over existing methods. MobilePicasso demonstrates significantly lower latency, e.g., up to 55.8$\times$ speed-up, yet with a small increase in runtime memory, e.g., a mere 9% increase over prior work. Surprisingly, the on-device runtime of MobilePicasso is observed to be faster than a server-based high-resolution image editing model running on an A100 GPU. ---