# Research Papers — 2025-10 · bundle page 92 of 217 > Full-text markdown of papers #4551–4600 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/92 [← Previous bundle](https://aipod.dev/papers/archive/2025-10/page/91.md) [Next bundle →](https://aipod.dev/papers/archive/2025-10/page/93.md) ### Designing a Convolutional Neural Network for High-Accuracy Oral Cavity Squamous Cell Carcinoma (OCSCC) Detection Authors: Vishal Manikanden, Aniketh Bandlamudi, Daniel Haehn Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16235v1 Oral Cavity Squamous Cell Carcinoma (OCSCC) is the most common type of head and neck cancer. Due to the subtle nature of its early stages, deep and hidden areas of development, and slow growth, OCSCC often goes undetected, leading to preventable deaths. However, properly trained Convolutional Neural Networks (CNNs), with their precise image segmentation techniques and ability to apply kernel matrices to modify the RGB values of images for accurate image pattern recognition, would be an effective means for early detection of OCSCC. Pairing this neural network with image capturing and processing hardware would allow increased efficacy in OCSCC detection. The aim of our project is to develop a Convolutional Neural Network trained to recognize OCSCC, as well as to design a physical hardware system to capture and process detailed images, in order to determine the image quality required for accurate predictions. A CNN was trained on 4293 training images consisting of benign and malignant tumors, as well as negative samples, and was evaluated for its precision, recall, and Mean Average Precision (mAP) in its predictions of OCSCC. A testing dataset of randomly assorted images of cancerous, non-cancerous, and negative images was chosen, and each image was altered to represent 5 common resolutions. This test data set was thoroughly analyzed by the CNN and predictions were scored on the basis of accuracy. The designed enhancement hardware was used to capture detailed images, and its impact was scored. An application was developed to facilitate the testing process and bring open access to the CNN. Images of increasing resolution resulted in higher-accuracy predictions on a logarithmic scale, demonstrating the diminishing returns of higher pixel counts. --- ### ScholarEval: Research Idea Evaluation Grounded in Literature Authors: Hanane Nour Moussa, Patrick Queiroz Da Silva, Daniel Adu-Ampratwum, Alyson East, Zitong Lu, Nikki Puccetti, Mingyi Xue, Huan Sun, Bodhisattwa Prasad Majumder, Sachin Kumar Published: 2025-10-17 Categories: cs.AI, cs.CL, cs.LG arXiv: https://arxiv.org/abs/2510.16234v2 As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas. We introduce ScholarEval, a retrieval augmented evaluation framework that assesses research ideas based on two fundamental criteria: soundness - the empirical validity of proposed methods based on existing literature, and contribution - the degree of advancement made by the idea across different dimensions relative to prior research. To evaluate ScholarEval, we introduce ScholarIdeas, the first expert-annotated dataset of multi-domain research ideas and reviews, comprised of 117 ideas across four disciplines: artificial intelligence, neuroscience, biochemistry, and ecology. Our evaluation shows that ScholarEval achieves significantly higher coverage of points mentioned in the human expert annotated rubrics in ScholarIdeas compared to all baselines. Furthermore, ScholarEval is consistently preferred over our strongest baseline o4-mini-deep-research, a reasoning and search-enabled agentic system by OpenAI, in terms of evaluation actionability, depth, and evidence support. Our large-scale user study also shows that ScholarEval significantly outperforms deep research in literature engagement, idea refinement, and usefulness. We openly release our code, dataset, and ScholarEval tool for the community to use and build on. --- ### Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal Authors: Patricia West, Michelle WL Wan, Alexander Hepburn, Edwin Simpson, Raul Santos-Rodriguez, Jeffrey N Clark Published: 2025-10-17 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2510.16233v1 Climate change demands effective legislative action to mitigate its impacts. This study explores the application of machine learning (ML) to understand the progression of climate policy from announcement to adoption, focusing on policies within the European Green Deal. We present a dataset of 165 policies, incorporating text and metadata. We aim to predict a policy's progression status, and compare text representation methods, including TF-IDF, BERT, and ClimateBERT. Metadata features are included to evaluate the impact on predictive performance. On text features alone, ClimateBERT outperforms other approaches (RMSE = 0.17, R^2 = 0.29), while BERT achieves superior performance with the addition of metadata features (RMSE = 0.16, R^2 = 0.38). Using methods from explainable AI highlights the influence of factors such as policy wording and metadata including political party and country representation. These findings underscore the potential of ML tools in supporting climate policy analysis and decision-making. --- ### Personalized Collaborative Learning with Affinity-Based Variance Reduction Authors: Chenyu Zhang, Navid Azizan Published: 2025-10-17 Categories: stat.ML, cs.LG, cs.MA, eess.SY arXiv: https://arxiv.org/abs/2510.16232v3 Multi-agent learning faces a fundamental tension: leveraging distributed collaboration without sacrificing the personalization needed for diverse agents. This tension intensifies when aiming for full personalization while adapting to unknown heterogeneity levels -- gaining collaborative speedup when agents are similar, without performance degradation when they are different. Embracing the challenge, we propose personalized collaborative learning (PCL), a novel framework for heterogeneous agents to collaboratively learn personalized solutions with seamless adaptivity. Through carefully designed bias correction and importance correction mechanisms, our method AffPCL robustly handles both environment and objective heterogeneity. We prove that AffPCL reduces sample complexity over independent learning by a factor of $\max\{n^{-1}, δ\}$, where $n$ is the number of agents and $δ\in(0,1)$ measures their heterogeneity. This affinity-based acceleration automatically interpolates between the linear speedup of federated learning in homogeneous settings and the baseline of independent learning, without requiring prior knowledge of the system. Our analysis further reveals that an agent may obtain linear speedup even by collaborating with arbitrarily dissimilar agents, unveiling new insights into personalization and collaboration in the high heterogeneity regime. --- ### What Can String Probability Tell Us About Grammaticality? Authors: Jennifer Hu, Ethan Gotlieb Wilcox, Siyuan Song, Kyle Mahowald, Roger P. Levy Published: 2025-10-17 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2510.16227v2 What have language models (LMs) learned about grammar? This question remains hotly debated, with major ramifications for linguistic theory. However, since probability and grammaticality are distinct notions in linguistics, it is not obvious what string probabilities can reveal about an LM's underlying grammatical knowledge. We present a theoretical analysis of the relationship between grammar, meaning, and string probability, based on simple assumptions about the generative process of corpus data. Our framework makes three predictions, which we validate empirically using 280K sentence pairs in English and Chinese: (1) correlation between the probability of strings within minimal pairs, i.e., string pairs with minimal semantic differences; (2) correlation between models' and humans' deltas within minimal pairs; and (3) poor separation in probability space between unpaired grammatical and ungrammatical strings. Our analyses give theoretical grounding for using probability to learn about LMs' structural knowledge, and suggest directions for future work in LM grammatical evaluation. --- ### VM-BeautyNet: A Synergistic Ensemble of Vision Transformer and Mamba for Facial Beauty Prediction Authors: Djamel Eddine Boukhari Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16220v1 Facial Beauty Prediction (FBP) is a complex and challenging computer vision task, aiming to model the subjective and intricate nature of human aesthetic perception. While deep learning models, particularly Convolutional Neural Networks (CNNs), have made significant strides, they often struggle to capture the global, holistic facial features that are critical to human judgment. Vision Transformers (ViT) address this by effectively modeling long-range spatial relationships, but their quadratic complexity can be a bottleneck. This paper introduces a novel, heterogeneous ensemble architecture, \textbf{VM-BeautyNet}, that synergistically fuses the complementary strengths of a Vision Transformer and a Mamba-based Vision model, a recent advancement in State-Space Models (SSMs). The ViT backbone excels at capturing global facial structure and symmetry, while the Mamba backbone efficiently models long-range dependencies with linear complexity, focusing on sequential features and textures. We evaluate our approach on the benchmark SCUT-FBP5500 dataset. Our proposed VM-BeautyNet achieves state-of-the-art performance, with a \textbf{Pearson Correlation (PC) of 0.9212}, a \textbf{Mean Absolute Error (MAE) of 0.2085}, and a \textbf{Root Mean Square Error (RMSE) of 0.2698}. Furthermore, through Grad-CAM visualizations, we provide interpretability analysis that confirms the complementary feature extraction of the two backbones, offering new insights into the model's decision-making process and presenting a powerful new architectural paradigm for computational aesthetics. --- ### SentinelNet: Safeguarding Multi-Agent Collaboration Through Credit-Based Dynamic Threat Detection Authors: Yang Feng, Xudong Pan Published: 2025-10-17 Categories: cs.CR, cs.AI arXiv: https://arxiv.org/abs/2510.16219v3 Malicious agents pose significant threats to the reliability and decision-making capabilities of Multi-Agent Systems (MAS) powered by Large Language Models (LLMs). Existing defenses often fall short due to reactive designs or centralized architectures which may introduce single points of failure. To address these challenges, we propose SentinelNet, the first decentralized framework for proactively detecting and mitigating malicious behaviors in multi-agent collaboration. SentinelNet equips each agent with a credit-based detector trained via contrastive learning on augmented adversarial debate trajectories, enabling autonomous evaluation of message credibility and dynamic neighbor ranking via bottom-k elimination to suppress malicious communications. To overcome the scarcity of attack data, it generates adversarial trajectories simulating diverse threats, ensuring robust training. Experiments on MAS benchmarks show SentinelNet achieves near-perfect detection of malicious agents, close to 100% within two debate rounds, and recovers 95% of system accuracy from compromised baselines. By exhibiting strong generalizability across domains and attack patterns, SentinelNet establishes a novel paradigm for safeguarding collaborative MAS. --- ### Benchmarking noisy label detection methods Authors: Henrique Pickler, Jorge K. S. Kamassury, Danilo Silva Published: 2025-10-17 Categories: cs.LG, stat.ML arXiv: https://arxiv.org/abs/2510.16211v2 Label noise is a common problem in real-world datasets, affecting both model training and validation. Clean data are essential for achieving strong performance and ensuring reliable evaluation. While various techniques have been proposed to detect noisy labels (or label errors), there is no clear consensus on optimal approaches. We perform a comprehensive benchmark of detection methods by decomposing them into three fundamental components: gathering strategy (in-sample vs out-of-sample), label disagreement measure, and aggregation method. This decomposition can be applied to many existing detection methods, and enables systematic comparison across diverse approaches. To fairly compare methods, we propose a unified benchmark task: detecting a fraction of training samples equal to the dataset's noise rate. We also introduce a novel metric: the false negative rate at this fixed operating point. Our evaluation spans vision and tabular datasets under both synthetic and real-world noise conditions. We identify that in-sample gathering using average probability aggregation combined with the logit margin as the label disagreement measure achieves the best results across most scenarios. Our findings provide practical guidance for designing new detection methods and selecting techniques for specific applications. --- ### StretchySnake: Flexible SSM Training Unlocks Action Recognition Across Spatio-Temporal Scales Authors: Nyle Siddiqui, Rohit Gupta, Sirnam Swetha, Mubarak Shah Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16209v1 State space models (SSMs) have emerged as a competitive alternative to transformers in various tasks. Their linear complexity and hidden-state recurrence make them particularly attractive for modeling long sequences, whereas attention becomes quadratically expensive. However, current training methods for video understanding are tailored towards transformers and fail to fully leverage the unique attributes of SSMs. For example, video models are often trained at a fixed resolution and video length to balance the quadratic scaling of attention cost against performance. Consequently, these models suffer from degraded performance when evaluated on videos with spatial and temporal resolutions unseen during training; a property we call spatio-temporal inflexibility. In the context of action recognition, this severely limits a model's ability to retain performance across both short- and long-form videos. Therefore, we propose a flexible training method that leverages and improves the inherent adaptability of SSMs. Our method samples videos at varying temporal and spatial resolutions during training and dynamically interpolates model weights to accommodate any spatio-temporal scale. This instills our SSM, which we call StretchySnake, with spatio-temporal flexibility and enables it to seamlessly handle videos ranging from short, fine-grained clips to long, complex activities. We introduce and compare five different variants of flexible training, and identify the most effective strategy for video SSMs. On short-action (UCF-101, HMDB-51) and long-action (COIN, Breakfast) benchmarks, StretchySnake outperforms transformer and SSM baselines alike by up to 28%, with strong adaptability to fine-grained actions (SSV2, Diving-48). Therefore, our method provides a simple drop-in training recipe that makes video SSMs more robust, resolution-agnostic, and efficient across diverse action recognition scenarios. --- ### Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics Authors: Sunmook Choi, Yahya Sattar, Yassir Jedra, Maryam Fazel, Sarah Dean Published: 2025-10-17 Categories: cs.LG, eess.SY, math.OC, stat.ML arXiv: https://arxiv.org/abs/2510.16208v1 We study a nonstationary bandit problem where rewards depend on both actions and latent states, the latter governed by unknown linear dynamics. Crucially, the state dynamics also depend on the actions, resulting in tension between short-term and long-term rewards. We propose an explore-then-commit algorithm for a finite horizon $T$. During the exploration phase, random Rademacher actions enable estimation of the Markov parameters of the linear dynamics, which characterize the action-reward relationship. In the commit phase, the algorithm uses the estimated parameters to design an optimized action sequence for long-term reward. Our proposed algorithm achieves $\tilde{\mathcal{O}}(T^{2/3})$ regret. Our analysis handles two key challenges: learning from temporally correlated rewards, and designing action sequences with optimal long-term reward. We address the first challenge by providing near-optimal sample complexity and error bounds for system identification using bilinear rewards. We address the second challenge by proving an equivalence with indefinite quadratic optimization over a hypercube, a known NP-hard problem. We provide a sub-optimality guarantee for this problem, enabling our regret upper bound. Lastly, we propose a semidefinite relaxation with Goemans-Williamson rounding as a practical approach. --- ### Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions Authors: Mateus Pinto da Silva, Sabrina P. L. P. Correa, Hugo N. Oliveira, Ian M. Nunes, Jefersson A. dos Santos Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16207v1 Mapping agriculture in tropical areas through remote sensing presents unique challenges, including the lack of high-quality annotated data, the elevated costs of labeling, data variability, and regional generalisation. This paper advocates a Data-Centric Artificial Intelligence (DCAI) perspective and pipeline, emphasizing data quality and curation as key drivers for model robustness and scalability. It reviews and prioritizes techniques such as confident learning, core-set selection, data augmentation, and active learning. The paper highlights the readiness and suitability of 25 distinct strategies in large-scale agricultural mapping pipelines. The tropical context is of high interest, since high cloudiness, diverse crop calendars, and limited datasets limit traditional model-centric approaches. This tutorial outlines practical solutions as a data-centric approach for curating and training AI models better suited to the dynamic realities of tropical agriculture. Finally, we propose a practical pipeline using the 9 most mature and straightforward methods that can be applied to a large-scale tropical agricultural mapping project. --- ### The Right to Be Remembered: Preserving Maximally Truthful Digital Memory in the Age of AI Authors: Alex Zhavoronkov, Dominika Wilczok, Roman Yampolskiy Published: 2025-10-17 Categories: cs.AI arXiv: https://arxiv.org/abs/2510.16206v2 Since the rapid expansion of large language models (LLMs), people have begun to rely on them for information retrieval. While traditional search engines display ranked lists of sources shaped by search engine optimization (SEO), advertising, and personalization, LLMs typically provide a synthesized response that feels singular and authoritative. While both approaches carry risks of bias and omission, LLMs may amplify the effect by collapsing multiple perspectives into one answer, reducing users ability or inclination to compare alternatives. This concentrates power over information in a few LLM vendors whose systems effectively shape what is remembered and what is overlooked. As a result, certain narratives, individuals or groups, may be disproportionately suppressed, while others are disproportionately elevated. Over time, this creates a new threat: the gradual erasure of those with limited digital presence, and the amplification of those already prominent, reshaping collective memory. To address these concerns, this paper presents a concept of the Right To Be Remembered (RTBR) which encompasses minimizing the risk of AI-driven information omission, embracing the right of fair treatment, while ensuring that the generated content would be maximally truthful. --- ### EgMM-Corpus: A Multimodal Vision-Language Dataset for Egyptian Culture Authors: Mohamed Gamil, Abdelrahman Elsayed, Abdelrahman Lila, Ahmed Gad, Hesham Abdelgawad, Mohamed Aref, Ahmed Fares Published: 2025-10-17 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.16198v1 Despite recent advances in AI, multimodal culturally diverse datasets are still limited, particularly for regions in the Middle East and Africa. In this paper, we introduce EgMM-Corpus, a multimodal dataset dedicated to Egyptian culture. By designing and running a new data collection pipeline, we collected over 3,000 images, covering 313 concepts across landmarks, food, and folklore. Each entry in the dataset is manually validated for cultural authenticity and multimodal coherence. EgMM-Corpus aims to provide a reliable resource for evaluating and training vision-language models in an Egyptian cultural context. We further evaluate the zero-shot performance of Contrastive Language-Image Pre-training CLIP on EgMM-Corpus, on which it achieves 21.2% Top-1 accuracy and 36.4% Top-5 accuracy in classification. These results underscore the existing cultural bias in large-scale vision-language models and demonstrate the importance of EgMM-Corpus as a benchmark for developing culturally aware models. --- ### Revealing Low-Dimensional Structure in 2D Richtmyer-Meshkov Instabilities via Parametric Reduced-Order Modeling Authors: Daniel Messenger, Daniel Serino, Balu Nadiga, Marc Klasky Published: 2025-10-17 Categories: physics.flu-dyn, cs.AI arXiv: https://arxiv.org/abs/2510.16197v1 Efficient modeling of the Richtmyer-Meshkov instability (RMI) is essential to many engineering tasks, including high-speed combustion and drive and capsule geometry optimization in Inertial Confinement Fusion (ICF). In the latter, RMI causes the ablator and fuel to mix, introducing cold spots into the fuel and lowering performance; controlling RMI is thus a core ICF design concern. In this work, we introduce a reduced-order model for two-dimensional RMI based on the Latent Space Dynamics Identification (LaSDI) algorithm. We demonstrate the efficacy of the proposed methodology in efficiently parametrizing the solution space over a high-dimensional parameter vector consisting of material EOS parameters and initial conditions known to affect RMI growth rates. Using only late-time partial observations of the dynamics, we use our framework to not only provide a highly efficient dynamic surrogate model, but to reveal that the RMI exhibits the structure of a surprisingly low-dimensional and linear dynamical system, into the nonlinear growth regime, after a suitable nonlinear transformation is applied to the material interface, which we approximate as a trained autoencoder. Our use of practical observables and fundamental parameters suggests that such ROMs may be useful for downstream engineering tasks which confront the RMI, while the low-dimensional representation suggests a new direction for theoretical work. --- ### Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI Authors: Zheng Huang, Enpei Zhang, Weikang Qiu, Yinghao Cai, Carl Yang, Elynn Chen, Xiang Zhang, Rex Ying, Dawei Zhou, Yujun Yan Published: 2025-10-17 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2510.16196v2 Understanding how the brain encodes visual information is a central challenge in neuroscience and machine learning. A promising approach is to reconstruct visual stimuli, essentially images, from functional Magnetic Resonance Imaging (fMRI) signals. This involves two stages: transforming fMRI signals into a latent space and then using a pretrained generative model to reconstruct images. The reconstruction quality depends on how similar the latent space is to the structure of neural activity and how well the generative model produces images from that space. Yet, it remains unclear which type of latent space best supports this transformation and how it should be organized to represent visual stimuli effectively. We present two key findings. First, fMRI signals are more similar to the text space of a language model than to either a vision based space or a joint text image space. Second, text representations and the generative model should be adapted to capture the compositional nature of visual stimuli, including objects, their detailed attributes, and relationships. Building on these insights, we propose PRISM, a model that Projects fMRI sIgnals into a Structured text space as an interMediate representation for visual stimuli reconstruction. It includes an object centric diffusion module that generates images by composing individual objects to reduce object detection errors, and an attribute relationship search module that automatically identifies key attributes and relationships that best align with the neural activity. Extensive experiments on real world datasets demonstrate that our framework outperforms existing methods, achieving up to an 8% reduction in perceptual loss. These results highlight the importance of using structured text as the intermediate space to bridge fMRI signals and image reconstruction. --- ### Towards Automatic Evaluation and Selection of PHI De-identification Models via Multi-Agent Collaboration Authors: Guanchen Wu, Zuhui Chen, Yuzhang Xie, Carl Yang Published: 2025-10-17 Categories: cs.AI arXiv: https://arxiv.org/abs/2510.16194v2 Protected health information (PHI) de-identification is critical for enabling the safe reuse of clinical notes, yet evaluating and comparing PHI de-identification models typically depends on costly, small-scale expert annotations. We present TEAM-PHI, a multi-agent evaluation and selection framework that uses large language models (LLMs) to automatically measure de-identification quality and select the best-performing model without heavy reliance on gold labels. TEAM-PHI deploys multiple Evaluation Agents, each independently judging the correctness of PHI extractions and outputting structured metrics. Their results are then consolidated through an LLM-based majority voting mechanism that integrates diverse evaluator perspectives into a single, stable, and reproducible ranking. Experiments on a real-world clinical note corpus demonstrate that TEAM-PHI produces consistent and accurate rankings: despite variation across individual evaluators, LLM-based voting reliably converges on the same top-performing systems. Further comparison with ground-truth annotations and human evaluation confirms that the framework's automated rankings closely match supervised evaluation. By combining independent evaluation agents with LLM majority voting, TEAM-PHI offers a practical, secure, and cost-effective solution for automatic evaluation and best-model selection in PHI de-identification, even when ground-truth labels are limited. --- ### Operationalising Extended Cognition: Formal Metrics for Corporate Knowledge and Legal Accountability Authors: Elija Perrier Published: 2025-10-17 Categories: cs.AI arXiv: https://arxiv.org/abs/2510.16193v2 Corporate responsibility turns on notions of corporate \textit{mens rea}, traditionally imputed from human agents. Yet these assumptions are under challenge as generative AI increasingly mediates enterprise decision-making. Building on the theory of extended cognition, we argue that in response corporate knowledge may be redefined as a dynamic capability, measurable by the efficiency of its information-access procedures and the validated reliability of their outputs. We develop a formal model that captures epistemic states of corporations deploying sophisticated AI or information systems, introducing a continuous organisational knowledge metric $S_S(\varphi)$ which integrates a pipeline's computational cost and its statistically validated error rate. We derive a thresholded knowledge predicate $\mathsf{K}_S$ to impute knowledge and a firm-wide epistemic capacity index $\mathcal{K}_{S,t}$ to measure overall capability. We then operationally map these quantitative metrics onto the legal standards of actual knowledge, constructive knowledge, wilful blindness, and recklessness. Our work provides a pathway towards creating measurable and justiciable audit artefacts, that render the corporate mind tractable and accountable in the algorithmic age. --- ### Human-Allied Relational Reinforcement Learning Authors: Fateme Golivand Darvishvand, Hikaru Shindo, Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan Published: 2025-10-17 Categories: cs.LG arXiv: https://arxiv.org/abs/2510.16188v1 Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of propositional tasks ignoring the inherent structure that exists in the problem. Consequently, relational extensions (RRL) have been developed for such structured problems that allow for effective generalization to arbitrary number of objects. However, they inherently make strong assumptions about the problem structure. We introduce a novel framework that combines RRL with object-centric representation to handle both structured and unstructured data. We enhance learning by allowing the system to actively query the human expert for guidance by explicitly modeling the uncertainty over the policy. Our empirical evaluation demonstrates the effectiveness and efficiency of our proposed approach. --- ### Zero-Shot Coordination in Ad Hoc Teams with Generalized Policy Improvement and Difference Rewards Authors: Rupal Nigam, Niket Parikh, Hamid Osooli, Mikihisa Yuasa, Jacob Heglund, Huy T. Tran Published: 2025-10-17 Categories: cs.MA, cs.AI, cs.RO arXiv: https://arxiv.org/abs/2510.16187v2 Real-world multi-agent systems may require ad hoc teaming, where an agent must coordinate with other previously unseen teammates to solve a task in a zero-shot manner. Prior work often either selects a pretrained policy based on an inferred model of the new teammates or pretrains a single policy that is robust to potential teammates. Instead, we propose to leverage all pretrained policies in a zero-shot transfer setting. We formalize this problem as an ad hoc multi-agent Markov decision process and present a solution that uses two key ideas, generalized policy improvement and difference rewards, for efficient and effective knowledge transfer between different teams. We empirically demonstrate that our algorithm, Generalized Policy improvement for Ad hoc Teaming (GPAT), successfully enables zero-shot transfer to new teams in three simulated environments: cooperative foraging, predator-prey, and Overcooked. We also demonstrate our algorithm in a real-world multi-robot setting. --- ### Expressive Reward Synthesis with the Runtime Monitoring Language Authors: Daniel Donnelly, Angelo Ferrando, Francesco Belardinelli Published: 2025-10-17 Categories: cs.LG, cs.AI, cs.FL, stat.ML arXiv: https://arxiv.org/abs/2510.16185v2 A key challenge in reinforcement learning (RL) is reward (mis)specification, whereby imprecisely defined reward functions can result in unintended, possibly harmful, behaviours. Indeed, reward functions in RL are typically treated as black-box mappings from state-action pairs to scalar values. While effective in many settings, this approach provides no information about why rewards are given, which can hinder learning and interpretability. Reward Machines address this issue by representing reward functions as finite state automata, enabling the specification of structured, non-Markovian reward functions. However, their expressivity is typically bounded by regular languages, leaving them unable to capture more complex behaviours such as counting or parametrised conditions. In this work, we build on the Runtime Monitoring Language (RML) to develop a novel class of language-based Reward Machines. By leveraging the built-in memory of RML, our approach can specify reward functions for non-regular, non-Markovian tasks. We demonstrate the expressiveness of our approach through experiments, highlighting additional advantages in flexible event-handling and task specification over existing Reward Machine-based methods. --- ### Cost Savings from Automatic Quality Assessment of Generated Images Authors: Xavier Giro-i-Nieto, Nefeli Andreou, Anqi Liang, Manel Baradad, Francesc Moreno-Noguer, Aleix Martinez Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16179v1 Deep generative models have shown impressive progress in recent years, making it possible to produce high quality images with a simple text prompt or a reference image. However, state of the art technology does not yet meet the quality standards offered by traditional photographic methods. For this reason, production pipelines that use generated images often include a manual stage of image quality assessment (IQA). This process is slow and expensive, especially because of the low yield of automatically generated images that pass the quality bar. The IQA workload can be reduced by introducing an automatic pre-filtering stage, that will increase the overall quality of the images sent to review and, therefore, reduce the average cost required to obtain a high quality image. We present a formula that estimates the cost savings depending on the precision and pass yield of a generic IQA engine. This formula is applied in a use case of background inpainting, showcasing a significant cost saving of 51.61% obtained with a simple AutoML solution. --- ### The Formalism-Implementation Gap in Reinforcement Learning Research Authors: Pablo Samuel Castro Published: 2025-10-17 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2510.16175v2 The last decade has seen an upswing in interest and adoption of reinforcement learning (RL) techniques, in large part due to its demonstrated capabilities at performing certain tasks at "super-human levels". This has incentivized the community to prioritize research that demonstrates RL agent performance, often at the expense of research aimed at understanding their learning dynamics. Performance-focused research runs the risk of overfitting on academic benchmarks -- thereby rendering them less useful -- which can make it difficult to transfer proposed techniques to novel problems. Further, it implicitly diminishes work that does not push the performance-frontier, but aims at improving our understanding of these techniques. This paper argues two points: (i) RL research should stop focusing solely on demonstrating agent capabilities, and focus more on advancing the science and understanding of reinforcement learning; and (ii) we need to be more precise on how our benchmarks map to the underlying mathematical formalisms. We use the popular Arcade Learning Environment (ALE; Bellemare et al., 2013) as an example of a benchmark that, despite being increasingly considered "saturated", can be effectively used for developing this understanding, and facilitating the deployment of RL techniques in impactful real-world problems. --- ### In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions Authors: Aria Pessianzadeh, Naima Sultana, Hildegarde Van den Bulck, David Gefen, Shahin Jabbari, Rezvaneh Rezapour Published: 2025-10-17 Categories: cs.CL arXiv: https://arxiv.org/abs/2510.16173v2 The rise of generative AI (GenAI) has impacted many aspects of human life. As these systems become embedded in everyday practices, understanding public trust in them is also essential for responsible adoption and governance. Prior work on trust in AI has largely drawn from psychology and human-computer interaction, but there is a lack of computational, large-scale, and longitudinal approaches to measuring trust and distrust in GenAI and large language models (LLMs). This paper presents the first computational study of trust and distrust in GenAI, using a multi-year Reddit dataset (2022--2025) spanning 39 subreddits and 230,576 posts. Crowd-sourced annotations of a representative sample were combined with classification models to scale analysis. We find that trust and distrust are nearly balanced over time, although trust modestly outweighs distrust, with shifts around major model releases. Technical performance and usability dominate as dimensions, while personal experience is the most frequent reason shaping attitudes. Distinct patterns also emerge across trustors (e.g., experts, ethicists, and general users). Our results provide a methodological framework for large-scale trust analysis and insights into evolving public perceptions of GenAI. --- ### Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness Authors: Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh, Chaowei Zhang, Xiao Qin, Yang Zhou Published: 2025-10-17 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2510.16171v3 Adversarial examples reveal critical vulnerabilities in deep neural networks by exploiting their sensitivity to imperceptible input perturbations. While adversarial training remains the predominant defense strategy, it often incurs significant computational cost and may compromise clean-data accuracy. In this work, we investigate an architectural approach to adversarial robustness by embedding group-equivariant convolutions-specifically, rotation- and scale-equivariant layers-into standard convolutional neural networks (CNNs). These layers encode symmetry priors that align model behavior with structured transformations in the input space, promoting smoother decision boundaries and greater resilience to adversarial attacks. We propose and evaluate two symmetry-aware architectures: a parallel design that processes standard and equivariant features independently before fusion, and a cascaded design that applies equivariant operations sequentially. Theoretically, we demonstrate that such models reduce hypothesis space complexity, regularize gradients, and yield tighter certified robustness bounds under the CLEVER (Cross Lipschitz Extreme Value for nEtwork Robustness) framework. Empirically, our models consistently improve adversarial robustness and generalization across CIFAR-10, CIFAR-100, and CIFAR-10C under both FGSM and PGD attacks, without requiring adversarial training. These findings underscore the potential of symmetry-enforcing architectures as efficient and principled alternatives to data augmentation-based defenses. --- ### Alignment is Localized: A Causal Probe into Preference Layers Authors: Archie Chaudhury Published: 2025-10-17 Categories: cs.LG, cs.CL arXiv: https://arxiv.org/abs/2510.16167v1 Reinforcement Learning frameworks, particularly those utilizing human annotations, have become an increasingly popular method for preference fine-tuning, where the outputs of a language model are tuned to match a certain set of behavioral policies or guidelines. Reinforcement Learning through Human Feedback (RLHF) is perhaps the most popular implementation of such a framework, particularly for aligning LMs toward safety and human intent. However, the internal workings of how such alignment is achieved remain largely opaque. In this work, we systematically analyze preference optimization for language model alignment by applying layer-wide causal patching between a base model and its tuned counterpart across human preference pairs. We implement our methodology on \textit{Llama-3.2-1B}, and find that alignment is spatially localized: mid-layer activations encode a distinct subspace that causally determines reward-consistent behavior, while early and late layers remain largely unaffected. Utilizing LASSO regression, we also find that only a small number of layers possess non-zero coefficients linking activation distances to reward gains. Overall, we show that, at least for some language models, alignment from human-based, preferential tuning is a directional, low rank process, rather than diffuse and parameteric. --- ### Extending Prediction-Powered Inference through Conformal Prediction Authors: Daniel Csillag, Pedro Dall'Antonia, Claudio José Struchiner, Guilherme Tegoni Goedert Published: 2025-10-17 Categories: stat.ME, cs.LG, stat.ML arXiv: https://arxiv.org/abs/2510.16166v1 Prediction-powered inference is a recent methodology for the safe use of black-box ML models to impute missing data, strengthening inference of statistical parameters. However, many applications require strong properties besides valid inference, such as privacy, robustness or validity under continuous distribution shifts; deriving prediction-powered methods with such guarantees is generally an arduous process, and has to be done case by case. In this paper, we resolve this issue by connecting prediction-powered inference with conformal prediction: by performing imputation through a calibrated conformal set-predictor, we attain validity while achieving additional guarantees in a natural manner. We instantiate our procedure for the inference of means, Z- and M-estimation, as well as e-values and e-value-based procedures. Furthermore, in the case of e-values, ours is the first general prediction-powered procedure that operates off-line. We demonstrate these advantages by applying our method on private and time-series data. Both tasks are nontrivial within the standard prediction-powered framework but become natural under our method. --- ### AtomBench: A Benchmarking Framework for Generative Crystal Reconstruction Models in Conventional Superconductors Authors: Charles Rhys Campbell, Aldo H. Romero, Kamal Choudhary Published: 2025-10-17 Categories: cs.LG, cond-mat.supr-con arXiv: https://arxiv.org/abs/2510.16165v2 A key question in benchmarking generative crystal reconstruction models is how the amount and type of crystallographic information provided to a generative model affects its ability to reconstruct atomic structures. Yet such comparisons often overlook the fact that models receive unequal information about the target during reconstruction, thereby confounding architectural conclusions. We present AtomBench, an extensible, model-agnostic framework for comparing generative models on a well-defined crystal reconstruction task (rather than \textit{de novo} generation), which we here apply to conventional superconductors. We train and evaluate four models, AtomGPT, CDVAE, FlowMM, and MatterGen, on the JARVIS Supercon-3D and Alexandria DS-A/B datasets, grouping them by the information each accesses at inference. Reconstruction fidelity is measured by the Kullback-Leibler divergence (KLD) and mean absolute error (MAE) of lattice parameters and the root-mean-squared displacement (RMSD) of atomic coordinates. We further introduce the continuous corrected RMSD (ccRMSD), a continuous measure of local geometric fidelity defined for every structure in the test set. MatterGen achieves the best atomic-coordinate reconstruction, followed by AtomGPT, while CDVAE reconstructs lattices most accurately, and FlowMM is the least accurate but fastest overall. We find that conditioning on critical temperature T$_c$ does not consistently improve fidelity. We also release AtomBench as an open-source Python package that reproduces all reported reconstruction metrics, figures, and tables from one or more benchmark files and supports direct submission to the JARVIS-Leaderboard. Any inverse model emitting crystal reconstructions can be benchmarked with \texttt{atombench}, and we encourage community use. https://github.com/atomgptlab/atombench --- ### Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction Authors: Ankitkumar Joshi, Milos Hauskrecht Published: 2025-10-17 Categories: cs.LG, stat.ML arXiv: https://arxiv.org/abs/2510.16161v2 Modeling irregularly sampled multivariate time series is a persistent challenge in domains like healthcare and sensor networks. While recent works have explored a variety of complex learning architectures to solve the prediction problems for irregularly sampled time series, it remains unclear what the true benefits of some of these architectures are, and whether clever modifications of simpler and more efficient RNN-based algorithms are still competitive, i.e. they are on par with or even superior to these methods. In this work, we propose and study GRUwE: Gated Recurrent Unit with Exponential basis functions, that builds upon RNN-based architectures for observations made at irregular times. GRUwE supports both regression-based and event-based predictions in continuous time. GRUwE works by maintaining a Markov state representation of the time series that updates with the arrival of irregular observations. The Markov state update relies on two reset mechanisms: (i) observation-triggered reset to account for the new observation, and (ii) time-triggered reset that relies on learnable exponential decays, to support the predictions in continuous time. Our empirical evaluations across several real-world benchmarks on next-observation and next-event prediction tasks demonstrate that GRUwE can indeed achieve competitive or superior performance compared to the recent state-of-the-art (SOTA) methods. Thanks to its simplicity, GRUwE offers compelling advantages: it is easy to implement, requires minimal hyper-parameter tuning efforts, and significantly reduces the computational overhead in the online deployment. --- ### Automated C-Arm Positioning via Conformal Landmark Localization Authors: Ahmad Arrabi, Jay Hwasung Jung, Jax Luo, Nathan Franssen, Scott Raymond, Safwan Wshah Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16160v1 Accurate and reliable C-arm positioning is essential for fluoroscopy-guided interventions. However, clinical workflows rely on manual alignment that increases radiation exposure and procedural delays. In this work, we present a pipeline that autonomously navigates the C-arm to predefined anatomical landmarks utilizing X-ray images. Given an input X-ray image from an arbitrary starting location on the operating table, the model predicts a 3D displacement vector toward each target landmark along the body. To ensure reliable deployment, we capture both aleatoric and epistemic uncertainties in the model's predictions and further calibrate them using conformal prediction. The derived prediction regions are interpreted as 3D confidence regions around the predicted landmark locations. The training framework combines a probabilistic loss with skeletal pose regularization to encourage anatomically plausible outputs. We validate our approach on a synthetic X-ray dataset generated from DeepDRR. Results show not only strong localization accuracy across multiple architectures but also well-calibrated prediction bounds. These findings highlight the pipeline's potential as a component in safe and reliable autonomous C-arm systems. Code is available at https://github.com/AhmadArrabi/C_arm_guidance_APAH --- ### Zeroth-Order Sharpness-Aware Learning with Exponential Tilting Authors: Xuchen Gong, Tian Li Published: 2025-10-17 Categories: cs.LG, cs.CL, stat.ML arXiv: https://arxiv.org/abs/2510.16157v1 Classic zeroth-order optimization approaches typically optimize for a smoothed version of the original function, i.e., the expected objective under randomly perturbed model parameters. This can be interpreted as encouraging the loss values in the perturbation set to be small on average. Popular sharpness-aware minimization (SAM) objectives, however, typically focus on the largest loss within the neighborhood to arrive at flat minima more effectively. In this work, we connect zeroth-order optimization (and its corresponding objectives) with SAM approaches explicitly, through an exponential tilting objective that provides a smooth transition between the average- and the max-loss formulations. We explore new zeroth-order algorithms to solve a soft SAM objective parameterized by a tilting parameter $t$. We provide precise characterizations of the sharpness notions of the tilted SAM framework. Practically, our approach can be used as a gradient-free and memory-efficient alternative to SAM variants, and it achieves better generalization compared to vanilla zeroth-order baselines on a wide range of downstream tasks, including classification, multiple choice QA, and language generation. --- ### AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning Authors: Yueqian Lin, Zhengmian Hu, Jayakumar Subramanian, Qinsi Wang, Nikos Vlassis, Hai "Helen" Li, Yiran Chen Published: 2025-10-17 Categories: eess.AS, cs.AI, cs.MM arXiv: https://arxiv.org/abs/2510.16156v1 Effective human-AI collaboration on complex reasoning tasks requires that users understand and interact with the model's process, not just receive an output. However, the monolithic text from methods like Chain-of-Thought (CoT) prevents this, as current interfaces lack real-time verbalization and robust user barge-in. We present AsyncVoice Agent, a system whose asynchronous architecture decouples a streaming LLM backend from a conversational voice frontend. This design allows narration and inference to run in parallel, empowering users to interrupt, query, and steer the model's reasoning process at any time. Objective benchmarks show this approach reduces interaction latency by more than 600x compared to monolithic baselines while ensuring high fidelity and competitive task accuracy. By enabling a two-way dialogue with a model's thought process, AsyncVoice Agent offers a new paradigm for building more effective, steerable, and trustworthy human-AI systems for high-stakes tasks. --- ### Mapping Scientific Literature with Large Language Models and Topic Modeling Authors: Mason Smetana, Lev Khazanovich Published: 2025-10-17 Categories: cs.DL, cs.AI, cs.CL, cs.LG arXiv: https://arxiv.org/abs/2510.16152v2 Scientific literature is increasingly fragmented by disciplinary boundaries, specialized terminology, and potentially sparse keyword systems, making it difficult to capture the evolving structure of modern science. This study introduces a large language model (LLM)-driven framework for mapping scientific literature from a topic modeling perspective. The approach is demonstrated on a 20-year corpus of more than 1,500 engineering-related articles published in the Proceedings of the National Academy of Sciences (PNAS). A two-stage classification pipeline first assigns a primary thematic category to each article based on its abstract, followed by full-text analysis to identify secondary classifications that reveal latent cross-topic connections within the corpus. Unlike conventional topic models, the LLM-based framework produces semantically interpretable topics while maintaining strong quantitative performance. Comparative evaluation against established topic modeling methods shows higher topic diversity and lower overlap with competitive coherence metrics. Manual validation on a randomly sampled subset of abstracts yields an accuracy of 75.9%. Additional traditional natural language processing analyses confirm that the generated topics correspond to meaningful linguistic patterns in the corpus. A bipartite network linking primary and secondary classifications further reveals implicit thematic relationships that are not readily observable through abstracts or keyword systems alone. The findings indicate that the framework independently recovers much of the journal's editorial dual-classification structure without prior knowledge of its schema. Overall, the proposed approach offers a powerful tool for mapping science and identifying emerging cross-topic connections in research. --- ### DuetMatch: Harmonizing Semi-Supervised Brain MRI Segmentation via Decoupled Branch Optimization Authors: Thanh-Huy Nguyen, Hoang-Thien Nguyen, Vi Vu, Ba-Thinh Lam, Phat Huynh, Tianyang Wang, Xingjian Li, Ulas Bagci, Min Xu Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16146v2 The limited availability of annotated data in medical imaging makes semi-supervised learning increasingly appealing for its ability to learn from imperfect supervision. Recently, teacher-student frameworks have gained popularity for their training benefits and robust performance. However, jointly optimizing the entire network can hinder convergence and stability, especially in challenging scenarios. To address this for medical image segmentation, we propose DuetMatch, a novel dual-branch semi-supervised framework with asynchronous optimization, where each branch optimizes either the encoder or decoder while keeping the other frozen. To improve consistency under noisy conditions, we introduce Decoupled Dropout Perturbation, enforcing regularization across branches. We also design Pair-wise CutMix Cross-Guidance to enhance model diversity by exchanging pseudo-labels through augmented input pairs. To mitigate confirmation bias from noisy pseudo-labels, we propose Consistency Matching, refining labels using stable predictions from frozen teacher models. Extensive experiments on benchmark brain MRI segmentation datasets, including ISLES2022 and BraTS, show that DuetMatch consistently outperforms state-of-the-art methods, demonstrating its effectiveness and robustness across diverse semi-supervised segmentation scenarios. --- ### C-arm Guidance: A Self-supervised Approach To Automated Positioning During Stroke Thrombectomy Authors: Ahmad Arrabi, Jay hwasung Jung, J Le, A Nguyen, J Reed, E Stahl, Nathan Franssen, Scott Raymond, Safwan Wshah Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16145v1 Thrombectomy is one of the most effective treatments for ischemic stroke, but it is resource and personnel-intensive. We propose employing deep learning to automate critical aspects of thrombectomy, thereby enhancing efficiency and safety. In this work, we introduce a self-supervised framework that classifies various skeletal landmarks using a regression-based pretext task. Our experiments demonstrate that our model outperforms existing methods in both regression and classification tasks. Notably, our results indicate that the positional pretext task significantly enhances downstream classification performance. Future work will focus on extending this framework toward fully autonomous C-arm control, aiming to optimize trajectories from the pelvis to the head during stroke thrombectomy procedures. All code used is available at https://github.com/AhmadArrabi/C_arm_guidance --- ### Agentic AI for Ultra-Modern Networks: Multi-Agent Framework for RAN Autonomy and Assurance Authors: Sukhdeep Singh, Avinash Bhat, Shweta M, Subhash K Singh, Moonki Hong, Madhan Raj K, Kandeepan Sithamparanathan, Sunder A. Khowaja, Kapal Dev Published: 2025-10-17 Categories: cs.NI, cs.AI, cs.MA arXiv: https://arxiv.org/abs/2510.16144v1 The increasing complexity of Beyond 5G and 6G networks necessitates new paradigms for autonomy and assur- ance. Traditional O-RAN control loops rely heavily on RIC- based orchestration, which centralizes intelligence and exposes the system to risks such as policy conflicts, data drift, and unsafe actions under unforeseen conditions. In this work, we argue that the future of autonomous networks lies in a multi-agentic architecture, where specialized agents collaborate to perform data collection, model training, prediction, policy generation, verification, deployment, and assurance. By replacing tightly- coupled centralized RIC-based workflows with distributed agents, the framework achieves autonomy, resilience, explainability, and system-wide safety. To substantiate this vision, we design and evaluate a traffic steering use case under surge and drift conditions. Results across four KPIs: RRC connected users, IP throughput, PRB utilization, and SINR, demonstrate that a naive predictor-driven deployment improves local KPIs but destabilizes neighbors, whereas the agentic system blocks unsafe policies, preserving global network health. This study highlights multi- agent architectures as a credible foundation for trustworthy AI- driven autonomy in next-generation RANs. --- ### The Cultural Mapping and Pattern Analysis (CMAP) Visualization Toolkit: Open Source Text Analysis for Qualitative and Computational Social Science Authors: Corey M. Abramson, Yuhan, Nian Published: 2025-10-17 Categories: stat.AP, cs.LG, stat.CO arXiv: https://arxiv.org/abs/2510.16140v1 The CMAP (cultural mapping and pattern analysis) visualization toolkit introduced in this paper is an open-source suite for analyzing and visualizing text data - from qualitative fieldnotes and in-depth interview transcripts to historical documents and web-scaped data like message board posts or blogs. The toolkit is designed for scholars integrating pattern analysis, data visualization, and explanation in qualitative and/or computational social science (CSS). Despite the existence of off-the-shelf commercial qualitative data analysis software, there is a dearth of highly scalable open source options that can work with large data sets, and allow advanced statistical and language modeling. The foundation of the toolkit is a pragmatic approach that aligns research tools with social science project goals- empirical explanation, theory-guided measurement, comparative design, or evidence-based recommendations- guided by the principle that research paradigm and questions should determine methods. Consequently, the CMAP visualization toolkit offers a range of possibilities through the adjustment of relatively small number of parameters, and allows integration with other python tools. --- ### Expert Merging in Sparse Mixture of Experts with Nash Bargaining Authors: Dung V. Nguyen, Anh T. Nguyen, Minh H. Nguyen, Luc Q. Nguyen, Shiqi Jiang, Ethan Fetaya, Linh Duy Tran, Gal Chechik, Tan M. Nguyen Published: 2025-10-17 Categories: cs.LG, stat.ML arXiv: https://arxiv.org/abs/2510.16138v2 Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert parameters, but often lack a principled weighting mechanism. In this work, we reinterpret expert merging through the lens of game theory, revealing cooperative and competitive dynamics among experts. Based on this perspective, we introduce Nash Merging of Experts (NAMEx), a novel framework that incorporates Nash Bargaining into the merging process, enabling more balanced and efficient collaboration among experts. Additionally, we incorporate complex momentum into NAMEx to accelerate expert propagation with theoretical guarantees for convergence. Extensive experiments across language modelling, text classification, image classification, and zero-shot robustness under data corruption show that NAMEx consistently outperforms competing methods while integrating seamlessly with popular MoE architectures. Finally, we demonstrate NAMEx's scalability by applying it to large-scale systems, including Qwen1.5-MoE (14B) and DeepSeek-MoE (16B), where it proves effective in both zero-shot and fine-tuning settings. The code is publicly available at: https://github.com/anh147/NAMEx. --- ### GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer Authors: Sayan Deb Sarkar, Sinisa Stekovic, Vincent Lepetit, Iro Armeni Published: 2025-10-17 Categories: cs.CV, cs.AI, cs.GR arXiv: https://arxiv.org/abs/2510.16136v1 Transferring appearance to 3D assets using different representations of the appearance object - such as images or text - has garnered interest due to its wide range of applications in industries like gaming, augmented reality, and digital content creation. However, state-of-the-art methods still fail when the geometry between the input and appearance objects is significantly different. A straightforward approach is to directly apply a 3D generative model, but we show that this ultimately fails to produce appealing results. Instead, we propose a principled approach inspired by universal guidance. Given a pretrained rectified flow model conditioned on image or text, our training-free method interacts with the sampling process by periodically adding guidance. This guidance can be modeled as a differentiable loss function, and we experiment with two different types of guidance including part-aware losses for appearance and self-similarity. Our experiments show that our approach successfully transfers texture and geometric details to the input 3D asset, outperforming baselines both qualitatively and quantitatively. We also show that traditional metrics are not suitable for evaluating the task due to their inability of focusing on local details and comparing dissimilar inputs, in absence of ground truth data. We thus evaluate appearance transfer quality with a GPT-based system objectively ranking outputs, ensuring robust and human-like assessment, as further confirmed by our user study. Beyond showcased scenarios, our method is general and could be extended to different types of diffusion models and guidance functions. --- ### Aria Gen 2 Pilot Dataset Authors: Chen Kong, James Fort, Aria Kang, Jonathan Wittmer, Simon Green, Tianwei Shen, Yipu Zhao, Cheng Peng, Gustavo Solaira, Andrew Berkovich, Nikhil Raina, Vijay Baiyya et al. Published: 2025-10-17 Categories: cs.CV, cs.AI, cs.HC, cs.LG, cs.RO arXiv: https://arxiv.org/abs/2510.16134v1 The Aria Gen 2 Pilot Dataset (A2PD) is an egocentric multimodal open dataset captured using the state-of-the-art Aria Gen 2 glasses. To facilitate timely access, A2PD is released incrementally with ongoing dataset enhancements. The initial release features Dia'ane, our primary subject, who records her daily activities alongside friends, each equipped with Aria Gen 2 glasses. It encompasses five primary scenarios: cleaning, cooking, eating, playing, and outdoor walking. In each of the scenarios, we provide comprehensive raw sensor data and output data from various machine perception algorithms. These data illustrate the device's ability to perceive the wearer, the surrounding environment, and interactions between the wearer and the environment, while maintaining robust performance across diverse users and conditions. The A2PD is publicly available at projectaria.com, with open-source tools and usage examples provided in Project Aria Tools. --- ### A Minimal-Assumption Analysis of Q-Learning with Time-Varying Policies Authors: Phalguni Nanda, Zaiwei Chen Published: 2025-10-17 Categories: cs.LG, math.OC, stat.ML arXiv: https://arxiv.org/abs/2510.16132v2 In this work, we present the first finite-time analysis of Q-learning with time-varying learning policies (i.e., on-policy sampling) for discounted Markov decision processes under minimal assumptions, requiring only the existence of a policy that induces an irreducible Markov chain over the state space. We establish a last-iterate convergence rate for $\mathbb{E}(\|Q_k - Q^*\|_\infty^2)$, implying a sample complexity of order $\mathcal{O}(1/ξ^2)$ for achieving $\mathbb{E}(\|Q_k - Q^*\|_\infty)\le ξ$. This matches the rate of off-policy Q-learning, but with worse dependence on exploration-related parameters. We also derive a finite-time rate for $\mathbb{E}(\|Q^{π_k} - Q^*\|_\infty^2)$, where $π_k$ is the learning policy at iteration $k$, highlighting the exploration-exploitation trade-off in on-policy Q-learning. While exploration is weaker than in off-policy methods, on-policy learning enjoys an exploitation advantage as the learning policy converges to an optimal one. Numerical results support our theory. Technically, rapidly time-varying learning policies induce time-inhomogeneous Markovian noise, creating significant analytical challenges under minimal exploration. To address this, we develop a Poisson-equation-based decomposition of the Markovian noise under a lazy transition matrix, separating it into a martingale-difference term and residual terms. The residuals are controlled via sensitivity analysis of the Poisson equation solution with respect to both the Q-function estimate and the learning policy. These techniques may extend to other RL algorithms with time-varying policies, such as single-timescale actor-critic methods and learning-in-games algorithms. --- ### Learning density ratios in causal inference using Bregman-Riesz regression Authors: Oliver J. Hines, Caleb H. Miles Published: 2025-10-17 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2510.16127v2 The ratio of two probability density functions is a fundamental quantity that appears in many areas of statistics and machine learning, including causal inference, reinforcement learning, covariate shift, outlier detection, independence testing, importance sampling, and diffusion modeling. Naively estimating the numerator and denominator densities separately using, e.g., kernel density estimators, can lead to unstable performance and suffer from the curse of dimensionality as the number of covariates increases. For this reason, several methods have been developed for estimating the density ratio directly based on (a) Bregman divergences or (b) recasting the density ratio as the odds in a probabilistic classification model that predicts whether an observation is sampled from the numerator or denominator distribution. Additionally, the density ratio can be viewed as the Riesz representer of a continuous linear map, making it amenable to estimation via (c) minimization of the so-called Riesz loss, which was developed to learn the Riesz representer in the Riesz regression procedure in causal inference. In this paper we show that all three of these methods can be unified in a common framework, which we call Bregman--Riesz regression. We further show how data augmentation techniques can be used to apply density ratio learning methods to causal problems, where the numerator distribution typically represents an unobserved intervention. We show through simulations how the choice of Bregman divergence and data augmentation strategy can affect the performance of the resulting density ratio learner. A Python package is provided for researchers to apply Bregman--Riesz regression in practice using gradient boosting, neural networks, and kernel methods. --- ### Zero-shot World Models via Search in Memory Authors: Federico Malato, Ville Hautamäki Published: 2025-10-17 Categories: cs.LG arXiv: https://arxiv.org/abs/2510.16123v1 World Models have vastly permeated the field of Reinforcement Learning. Their ability to model the transition dynamics of an environment have greatly improved sample efficiency in online RL. Among them, the most notorious example is Dreamer, a model that learns to act in a diverse set of image-based environments. In this paper, we leverage similarity search and stochastic representations to approximate a world model without a training procedure. We establish a comparison with PlaNet, a well-established world model of the Dreamer family. We evaluate the models on the quality of latent reconstruction and on the perceived similarity of the reconstructed image, on both next-step and long horizon dynamics prediction. The results of our study demonstrate that a search-based world model is comparable to a training based one in both cases. Notably, our model show stronger performance in long-horizon prediction with respect to the baseline on a range of visually different environments. --- ### The Hidden Cost of Modeling P(X): Vulnerability to Membership Inference Attacks in Generative Text Classifiers Authors: Owais Makroo, Siva Rajesh Kasa, Sumegh Roychowdhury, Karan Gupta, Nikhil Pattisapu, Santhosh Kasa, Sumit Negi Published: 2025-10-17 Categories: cs.CR, cs.CL, cs.LG, stat.ML arXiv: https://arxiv.org/abs/2510.16122v1 Membership Inference Attacks (MIAs) pose a critical privacy threat by enabling adversaries to determine whether a specific sample was included in a model's training dataset. Despite extensive research on MIAs, systematic comparisons between generative and discriminative classifiers remain limited. This work addresses this gap by first providing theoretical motivation for why generative classifiers exhibit heightened susceptibility to MIAs, then validating these insights through comprehensive empirical evaluation. Our study encompasses discriminative, generative, and pseudo-generative text classifiers across varying training data volumes, evaluated on nine benchmark datasets. Employing a diverse array of MIA strategies, we consistently demonstrate that fully generative classifiers which explicitly model the joint likelihood $P(X,Y)$ are most vulnerable to membership leakage. Furthermore, we observe that the canonical inference approach commonly used in generative classifiers significantly amplifies this privacy risk. These findings reveal a fundamental utility-privacy trade-off inherent in classifier design, underscoring the critical need for caution when deploying generative classifiers in privacy-sensitive applications. Our results motivate future research directions in developing privacy-preserving generative classifiers that can maintain utility while mitigating membership inference vulnerabilities. --- ### ObjectTransforms for Uncertainty Quantification and Reduction in Vision-Based Perception for Autonomous Vehicles Authors: Nishad Sahu, Shounak Sural, Aditya Satish Patil, Ragunathan, Rajkumar Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16118v1 Reliable perception is fundamental for safety critical decision making in autonomous driving. Yet, vision based object detector neural networks remain vulnerable to uncertainty arising from issues such as data bias and distributional shifts. In this paper, we introduce ObjectTransforms, a technique for quantifying and reducing uncertainty in vision based object detection through object specific transformations at both training and inference times. At training time, ObjectTransforms perform color space perturbations on individual objects, improving robustness to lighting and color variations. ObjectTransforms also uses diffusion models to generate realistic, diverse pedestrian instances. At inference time, object perturbations are applied to detected objects and the variance of detection scores are used to quantify predictive uncertainty in real time. This uncertainty signal is then used to filter out false positives and also recover false negatives, improving the overall precision recall curve. Experiments with YOLOv8 on the NuImages 10K dataset demonstrate that our method yields notable accuracy improvements and uncertainty reduction across all object classes during training, while predicting desirably higher uncertainty values for false positives as compared to true positives during inference. Our results highlight the potential of ObjectTransforms as a lightweight yet effective mechanism for reducing and quantifying uncertainty in vision-based perception during training and inference respectively. --- ### StripRFNet: A Strip Receptive Field and Shape-Aware Network for Road Damage Detection Authors: Jianhan Lin, Yuchu Qin, Shuai Gao, Yikang Rui, Jie Liu, Yanjie Lv Published: 2025-10-17 Categories: cs.CV arXiv: https://arxiv.org/abs/2510.16115v1 Well-maintained road networks are crucial for achieving Sustainable Development Goal (SDG) 11. Road surface damage not only threatens traffic safety but also hinders sustainable urban development. Accurate detection, however, remains challenging due to the diverse shapes of damages, the difficulty of capturing slender cracks with high aspect ratios, and the high error rates in small-scale damage recognition. To address these issues, we propose StripRFNet, a novel deep neural network comprising three modules: (1) a Shape Perception Module (SPM) that enhances shape discrimination via large separable kernel attention (LSKA) in multi-scale feature aggregation; (2) a Strip Receptive Field Module (SRFM) that employs large strip convolutions and pooling to capture features of slender cracks; and (3) a Small-Scale Enhancement Module (SSEM) that leverages a high-resolution P2 feature map, a dedicated detection head, and dynamic upsampling to improve small-object detection. Experiments on the RDD2022 benchmark show that StripRFNet surpasses existing methods. On the Chinese subset, it improves F1-score, mAP50, and mAP50:95 by 4.4, 2.9, and 3.4 percentage points over the baseline, respectively. On the full dataset, it achieves the highest F1-score of 80.33% compared with CRDDC'2022 participants and ORDDC'2024 Phase 2 results, while maintaining competitive inference speed. These results demonstrate that StripRFNet achieves state-of-the-art accuracy and real-time efficiency, offering a promising tool for intelligent road maintenance and sustainable infrastructure management. --- ### Narrowing Action Choices with AI Improves Human Sequential Decisions Authors: Eleni Straitouri, Stratis Tsirtsis, Ander Artola Velasco, Manuel Gomez-Rodriguez Published: 2025-10-17 Categories: cs.LG, cs.AI, cs.CY, cs.HC, stat.ML arXiv: https://arxiv.org/abs/2510.16097v1 Recent work has shown that, in classification tasks, it is possible to design decision support systems that do not require human experts to understand when to cede agency to a classifier or when to exercise their own agency to achieve complementarity$\unicode{x2014}$experts using these systems make more accurate predictions than those made by the experts or the classifier alone. The key principle underpinning these systems reduces to adaptively controlling the level of human agency, by design. Can we use the same principle to achieve complementarity in sequential decision making tasks? In this paper, we answer this question affirmatively. We develop a decision support system that uses a pre-trained AI agent to narrow down the set of actions a human can take to a subset, and then asks the human to take an action from this action set. Along the way, we also introduce a bandit algorithm that leverages the smoothness properties of the action sets provided by our system to efficiently optimize the level of human agency. To evaluate our decision support system, we conduct a large-scale human subject study ($n = 1{,}600$) where participants play a wildfire mitigation game. We find that participants who play the game supported by our system outperform those who play on their own by $\sim$$30$% and the AI agent used by our system by $>$$2$%, even though the AI agent largely outperforms participants playing without support. We have made available the data gathered in our human subject study as well as an open source implementation of our system at https://github.com/Networks-Learning/narrowing-action-choices . --- ### Facts in Stats: Impacts of Pretraining Diversity on Language Model Generalization Authors: Tina Behnia, Puneesh Deora, Christos Thrampoulidis Published: 2025-10-17 Categories: cs.CL, cs.LG arXiv: https://arxiv.org/abs/2510.16096v1 Language models are pretrained on sequences that blend statistical regularities (making text fluent) with factual associations between specific tokens (knowledge of facts). While recent work suggests that the variability of their interaction, such as paraphrases of factual associations, critically determines generalization ability, we lack a systematic analysis of these impacts. This paper introduces a flexible synthetic testbed that combines a statistical stream of generic tokens with an abstract factual stream of source-target token pairs, enabling fine-grained control over their interaction. The design enables the independent control of diversity nature by manipulating stream composition (contextual structure) and the diversity level by varying which statistical streams each fact appears in. Through controlled experiments, we find that while higher contextual diversity delays in-distribution (ID) factual accuracy, its impact on out-of-distribution (OOD) factual generalization depends critically on contextual structure. In some cases, OOD performance follows the same trend as ID, but in others, diversity becomes essential for non-trivial factual recall. Even when low diversity prohibits factual recall, optimal diversity levels depend on training duration. Beyond factual recall failures, we identify structures where statistical generalization fails independently, and others where both capabilities degrade. This shows how the interplay between contextual design and diversity level impacts different generalization aspects. Further, through a series of controlled interventions on the model components, we trace the OOD failures to distinct optimization bottlenecks, highlighting the importance of the embedding and unembedding layers. Our synthetic framework allows us to isolate effects that would be confounded in large-scale studies, offering a controlled testbed for future investigations. --- ### Reliability of Large Language Model Generated Clinical Reasoning in Assisted Reproductive Technology: Blinded Comparative Evaluation Study Authors: Dou Liu, Ying Long, Sophia Zuoqiu, Di Liu, Kang Li, Yiting Lin, Hanyi Liu, Rong Yin, Tian Tang Published: 2025-10-17 Categories: cs.AI arXiv: https://arxiv.org/abs/2510.16095v1 Creating high-quality clinical Chains-of-Thought (CoTs) is crucial for explainable medical Artificial Intelligence (AI) while constrained by data scarcity. Although Large Language Models (LLMs) can synthesize medical data, their clinical reliability remains unverified. This study evaluates the reliability of LLM-generated CoTs and investigates prompting strategies to enhance their quality. In a blinded comparative study, senior clinicians in Assisted Reproductive Technology (ART) evaluated CoTs generated via three distinct strategies: Zero-shot, Random Few-shot (using shallow examples), and Selective Few-shot (using diverse, high-quality examples). These expert ratings were compared against evaluations from a state-of-the-art AI model (GPT-4o). The Selective Few-shot strategy significantly outperformed other strategies across all human evaluation metrics (p < .001). Critically, the Random Few-shot strategy offered no significant improvement over the Zero-shot baseline, demonstrating that low-quality examples are as ineffective as no examples. The success of the Selective strategy is attributed to two principles: "Gold-Standard Depth" (reasoning quality) and "Representative Diversity" (generalization). Notably, the AI evaluator failed to discern these critical performance differences. The clinical reliability of synthetic CoTs is dictated by strategic prompt curation, not the mere presence of examples. We propose a "Dual Principles" framework as a foundational methodology to generate trustworthy data at scale. This work offers a validated solution to the data bottleneck and confirms the indispensable role of human expertise in evaluating high-stakes clinical AI. --- ### Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression Authors: Md. Imtyaz Ahmed, Md. Delwar Hossain, Md Mostafizer Rahman, Md. Ahsan Habib, Md. Mamunur Rashid, Md. Selim Reza, Md Ashad Alam Published: 2025-10-17 Categories: q-bio.GN, cs.LG arXiv: https://arxiv.org/abs/2510.16093v1 Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. In this paper, we analyze lung cancer multi-omics datasets from The Cancer Genome Atlas (TCGA). Using four statistical methods, LIMMA, the T test, Canonical Correlation Analysis (CCA), and the Wilcoxon test, we identified differentially expressed genes across gene expression, DNA methylation, and miRNA expression data. We then integrated these multi-omics data using the Kernel Machine Regression (KMR) approach. Our findings reveal significant interactions among the three omics: gene expression, miRNA expression, and DNA methylation in lung cancer. From our data analysis, we identified 38 genes significantly associated with lung cancer. From our data analysis, we identified 38 genes significantly associated with lung cancer. Among these, eight genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that could play a crucial role in the treatment of lung cancer. These proposed drugs are also supported by the findings of other independent studies, which underscore their potential efficacy in the fight against lung cancer. --- ### Compressing Many-Shots in In-Context Learning Authors: Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta, Yerram Varun, Liqian Peng, Jay Yagnik, Praneeth Netrapalli, Cho-Jui Hsieh, Alec Go, Inderjit S Dhillon, Aditya Kusupati, Prateek Jain Published: 2025-10-17 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2510.16092v1 Large Language Models (LLMs) have been shown to be able to learn different tasks without explicit finetuning when given many input-output examples / demonstrations through In-Context Learning (ICL). Increasing the number of examples, called ``shots'', improves downstream task performance but incurs higher memory and computational costs. In this work, we study an approach to improve the memory and computational efficiency of ICL inference by compressing the many-shot prompts. Given many shots comprising t tokens, our goal is to generate a m soft-token summary, where m < t. We first show that existing prompt compression methods are ineffective for many-shot compression, and simply using fewer shots as a baseline is surprisingly strong. To achieve effective compression, we find that: (a) a stronger compressor model with more trainable parameters is necessary, and (b) compressing many-shot representations at each transformer layer enables more fine-grained compression by providing each layer with its own compressed representation. Based on these insights, we propose MemCom, a layer-wise compression method. We systematically evaluate various compressor models and training approaches across different model sizes (2B and 7B), architectures (Gemma and Mistral), many-shot sequence lengths (3k-6k tokens), and compression ratios (3x to 8x). MemCom outperforms strong baselines across all compression ratios on multiple classification tasks with large label sets. Notably, while baseline performance degrades sharply at higher compression ratios, often by over 20-30%, MemCom maintains high accuracy with minimal degradation, typically dropping by less than 10%. ---