# Research Papers — 2025-10 · bundle page 8 of 217

> Full-text markdown of papers #351–400 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/8
[← Previous bundle](https://aipod.dev/papers/archive/2025-10/page/7.md)
[Next bundle →](https://aipod.dev/papers/archive/2025-10/page/9.md)

### Unvalidated Trust: Cross-Stage Vulnerabilities in Large Language Model Architectures

Authors: Dominik Schwarz
Published: 2025-10-30
Categories: cs.CR, cs.AI
arXiv: https://arxiv.org/abs/2510.27190v1

As Large Language Models (LLMs) are increasingly integrated into automated, multi-stage pipelines, risk patterns that arise from unvalidated trust between processing stages become a practical concern. This paper presents a mechanism-centered taxonomy of 41 recurring risk patterns in commercial LLMs. The analysis shows that inputs are often interpreted non-neutrally and can trigger implementation-shaped responses or unintended state changes even without explicit commands. We argue that these behaviors constitute architectural failure modes and that string-level filtering alone is insufficient. To mitigate such cross-stage vulnerabilities, we recommend zero-trust architectural principles, including provenance enforcement, context sealing, and plan revalidation, and we introduce "Countermind" as a conceptual blueprint for implementing these defenses.

---

### Overspecified Mixture Discriminant Analysis: Exponential Convergence, Statistical Guarantees, and Remote Sensing Applications

Authors: Arman Bolatov, Alan Legg, Igor Melnykov, Amantay Nurlanuly, Maxat Tezekbayev, Zhenisbek Assylbekov
Published: 2025-10-30
Categories: stat.ML, cs.LG
arXiv: https://arxiv.org/abs/2510.27056v1

This study explores the classification error of Mixture Discriminant Analysis (MDA) in scenarios where the number of mixture components exceeds those present in the actual data distribution, a condition known as overspecification. We use a two-component Gaussian mixture model within each class to fit data generated from a single Gaussian, analyzing both the algorithmic convergence of the Expectation-Maximization (EM) algorithm and the statistical classification error. We demonstrate that, with suitable initialization, the EM algorithm converges exponentially fast to the Bayes risk at the population level. Further, we extend our results to finite samples, showing that the classification error converges to Bayes risk with a rate $n^{-1/2}$ under mild conditions on the initial parameter estimates and sample size. This work provides a rigorous theoretical framework for understanding the performance of overspecified MDA, which is often used empirically in complex data settings, such as image and text classification. To validate our theory, we conduct experiments on remote sensing datasets.

---

### Detecting Data Contamination in LLMs via In-Context Learning

Authors: Michał Zawalski, Meriem Boubdir, Klaudia Bałazy, Besmira Nushi, Pablo Ribalta
Published: 2025-10-30
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2510.27055v2

We present Contamination Detection via Context (CoDeC), a practical and accurate method to detect and quantify training data contamination in large language models. CoDeC distinguishes between data memorized during training and data outside the training distribution by measuring how in-context learning affects model performance. We find that in-context examples typically boost confidence for unseen datasets but may reduce it when the dataset was part of training, due to disrupted memorization patterns. Experiments show that CoDeC produces interpretable contamination scores that clearly separate seen and unseen datasets, and reveals strong evidence of memorization in open-weight models with undisclosed training corpora. The method is simple, automated, and both model- and dataset-agnostic, making it easy to integrate with benchmark evaluations.

---

### LLM-Centric RAG with Multi-Granular Indexing and Confidence Constraints

Authors: Xiaofan Guo, Yaxuan Luan, Yue Kang, Xiangchen Song, Jinxu Guo
Published: 2025-10-30
Categories: cs.CL
arXiv: https://arxiv.org/abs/2510.27054v1

This paper addresses the issues of insufficient coverage, unstable results, and limited reliability in retrieval-augmented generation under complex knowledge environments, and proposes a confidence control method that integrates multi-granularity memory indexing with uncertainty estimation. The method builds a hierarchical memory structure that divides knowledge representations into different levels of granularity, enabling dynamic indexing and retrieval from local details to global context, and thus establishing closer semantic connections between retrieval and generation. On this basis, an uncertainty estimation mechanism is introduced to explicitly constrain and filter low-confidence paths during the generation process, allowing the model to maintain information coverage while effectively suppressing noise and false content. The overall optimization objective consists of generation loss, entropy constraints, and variance regularization, forming a unified confidence control framework. In the experiments, comprehensive sensitivity tests and comparative analyses were designed, covering hyperparameters, environmental conditions, and data structures, to verify the stability and robustness of the proposed method across different scenarios. The results show that the method achieves superior performance over existing models in QA accuracy, retrieval recall, ranking quality, and factual consistency, demonstrating the effectiveness of combining multi-granularity indexing with confidence control. This study not only provides a new technical pathway for retrieval-augmented generation but also offers practical evidence for improving the reliability and controllability of large models in complex contexts.

---

### VISTA: Verification In Sequential Turn-based Assessment

Authors: Ashley Lewis, Andrew Perrault, Eric Fosler-Lussier, Michael White
Published: 2025-10-30
Categories: cs.CL
arXiv: https://arxiv.org/abs/2510.27052v5

Hallucination--defined here as generating statements unsupported or contradicted by available evidence or conversational context--remains a major obstacle to deploying conversational AI systems in settings that demand factual reliability. Existing metrics either evaluate isolated responses or treat unverifiable content as errors, limiting their use for multi-turn dialogue. We introduce VISTA (Verification In Sequential Turn-based Assessment), a framework for evaluating conversational factuality through claim-level verification and sequential consistency tracking. VISTA decomposes each assistant turn into atomic factual claims, verifies them against trusted sources and dialogue history, and categorizes unverifiable statements (subjective, contradicted, lacking evidence, or abstaining). Across eight large language models and four dialogue factuality benchmarks (AIS, BEGIN, FAITHDIAL, and FADE), VISTA substantially improves hallucination detection over FACTSCORE and LLM-as-Judge baselines. Human evaluation confirms that VISTA's decomposition improves annotator agreement and reveals inconsistencies in existing benchmarks. By modeling factuality as a dynamic property of conversation, VISTA offers a more transparent, human-aligned measure of truthfulness in dialogue systems.

---

### Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent Improvement

Authors: Aaditya Shukla, Sidney Knowles, Meenakshi Madugula, Dave Farris, Ryan Angilly, Santiago Pombo, Anbang Xu, Lu An, Abhinav Balasubramanian, Tan Yu, Jiaxiang Ren, Rama Akkiraju
Published: 2025-10-30
Categories: cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2510.27051v1

Enterprise AI agents must continuously adapt to maintain accuracy, reduce latency, and remain aligned with user needs. We present a practical implementation of a data flywheel in NVInfo AI, NVIDIA's Mixture-of-Experts (MoE) Knowledge Assistant serving over 30,000 employees. By operationalizing a MAPE-driven data flywheel, we built a closed-loop system that systematically addresses failures in retrieval-augmented generation (RAG) pipelines and enables continuous learning. Over a 3-month post-deployment period, we monitored feedback and collected 495 negative samples. Analysis revealed two major failure modes: routing errors (5.25\%) and query rephrasal errors (3.2\%). Using NVIDIA NeMo microservices, we implemented targeted improvements through fine-tuning. For routing, we replaced a Llama 3.1 70B model with a fine-tuned 8B variant, achieving 96\% accuracy, a 10x reduction in model size, and 70\% latency improvement. For query rephrasal, fine-tuning yielded a 3.7\% gain in accuracy and a 40\% latency reduction. Our approach demonstrates how human-in-the-loop (HITL) feedback, when structured within a data flywheel, transforms enterprise AI agents into self-improving systems. Key learnings include approaches to ensure agent robustness despite limited user feedback, navigating privacy constraints, and executing staged rollouts in production. This work offers a repeatable blueprint for building robust, adaptive enterprise AI agents capable of learning from real-world usage at scale.

---

### Accelerating Radiative Transfer for Planetary Atmospheres by Orders of Magnitude with a Transformer-Based Machine Learning Model

Authors: Isaac Malsky, Tiffany Kataria, Natasha E. Batalha, Matthew Graham
Published: 2025-10-30
Categories: astro-ph.EP, astro-ph.IM, cs.LG
arXiv: https://arxiv.org/abs/2510.27050v1

Radiative transfer calculations are essential for modeling planetary atmospheres. However, standard methods are computationally demanding and impose accuracy-speed trade-offs. High computational costs force numerical simplifications in large models (e.g., General Circulation Models) that degrade the accuracy of the simulation. Radiative transfer calculations are an ideal candidate for machine learning emulation: fundamentally, it is a well-defined physical mapping from a static atmospheric profile to the resulting fluxes, and high-fidelity training data can be created from first principles calculations. We developed a radiative transfer emulator using an encoder-only transformer neural network architecture, trained on 1D profiles representative of solar-composition hot Jupiter atmospheres. Our emulator reproduced bolometric two-stream layer fluxes with mean test set errors of ~1% compared to the traditional method and achieved speedups of 100x. Emulating radiative transfer with machine learning opens up the possibility for faster and more accurate routines within planetary atmospheric models such as GCMs.

---

### Recursive numeral systems are highly regular and easy to process

Authors: Ponrawee Prasertsom, Andrea Silvi, Jennifer Culbertson, Moa Johansson, Devdatt Dubhashi, Kenny Smith
Published: 2025-10-30
Categories: cs.CL, cs.FL
arXiv: https://arxiv.org/abs/2510.27049v3

Much recent work has shown how cross-linguistic variation is constrained by competing pressures from efficient communication. However, little attention has been paid to the role of the systematicity of forms (regularity), a key property of natural language. Here, we demonstrate the importance of regularity in explaining the shape of linguistic systems by looking at recursive numeral systems. Previous work has argued that these systems optimise the trade-off between lexicon size and average morphosyntatic complexity (Denić and Szymanik, 2024). However, showing that only natural-language-like systems optimise this trade-off has proven elusive, and existing solutions rely on ad-hoc constraints to rule out unnatural systems (Yang and Regier, 2025). Drawing on the Minimum Description Length (MDL) approach, we argue that recursive numeral systems are better viewed as efficient with regard to their regularity and processing complexity. We show that our MDL-based measures of regularity and processing complexity better capture the key differences between attested, natural systems and theoretically possible ones, including "optimal" recursive numeral systems from previous work, and that the ad-hoc constraints naturally follow from regularity. Our approach highlights the need to incorporate regularity across sets of forms in studies attempting to measure efficiency in language.

---

### AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving

Authors: Het Patel, Mario Camarena, Fatemeh Nazari, Evangelos Papalexakis, Mohamadhossein Noruzoliaee, Jia Chen
Published: 2025-10-30
Categories: cs.CV
arXiv: https://arxiv.org/abs/2510.27047v2

This paper presents the Autonomous Driving Segment Anything Model (AD-SAM), a foundation-model adaptation framework for semantic segmentation in autonomous driving. AD-SAM combines a frozen Segment Anything Model (SAM) Vision Transformer (ViT-H) encoder with a trainable ResNet-50 encoder to integrate general visual representations with multi-scale, domain-specific spatial features. Features from the two encoders are integrated through deformable convolution and channel attention, followed by a multi-stage deformable decoder for semantic prediction. Training employs a hybrid objective combining Focal, Dice, Lovász-Softmax, and Surface losses. Experiments on Cityscapes and Berkeley DeepDrive 100K (BDD100K) show that AD-SAM outperforms SAM, Generalized SAM (G-SAM), and DeepLabV3 under a controlled training protocol. AD-SAM achieves 76.27% mIoU on Cityscapes and 64.74% on BDD100K, exceeding DeepLabV3 by 3.45 and 5.00 percentage points, respectively, with larger gains over the SAM-based baselines. Sample-size experiments reveal dataset-dependent behavior. In particular, AD-SAM performs strongly across training sizes on Cityscapes, while its advantage on the more heterogeneous BDD100K becomes pronounced with increased training data. When trained on Cityscapes and directly evaluated on BDD100K, AD-SAM achieves the highest cross-dataset retention (84.88%) among the evaluated models. AD-SAM also converges rapidly, while precomputed frozen SAM embeddings reduce training memory requirements. These findings demonstrate the potential of combining general foundation-model representations with domain-specific multi-scale features for accurate and robust autonomous-driving semantic segmentation.

---

### Quantitative Intertextuality from the Digital Humanities Perspective: A Survey

Authors: Siyu Duan
Published: 2025-10-30
Categories: cs.CL, cs.CY
arXiv: https://arxiv.org/abs/2510.27045v1

The connection between texts is referred to as intertextuality in literary theory, which served as an important theoretical basis in many digital humanities studies. Over the past decade, advancements in natural language processing have ushered intertextuality studies into the quantitative age. Large-scale intertextuality research based on cutting-edge methods has continuously emerged. This paper provides a roadmap for quantitative intertextuality studies, summarizing their data, methods, and applications. Drawing on data from multiple languages and topics, this survey reviews methods from statistics to deep learning. It also summarizes their applications in humanities and social sciences research and the associated platform tools. Driven by advances in computer technology, more precise, diverse, and large-scale intertext studies can be anticipated. Intertextuality holds promise for broader application in interdisciplinary research bridging AI and the humanities.

---

### Limits of Generalization in RLVR: Two Case Studies in Mathematical Reasoning

Authors: Md Tanvirul Alam, Nidhi Rastogi
Published: 2025-10-30
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.27044v2

Mathematical reasoning is a central challenge for large language models (LLMs), requiring not only correct answers but also faithful reasoning processes. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising approach for enhancing such capabilities; however, its ability to foster genuine reasoning remains unclear. We investigate RLVR on two combinatorial problems with fully verifiable solutions: \emph{Activity Scheduling} and the \emph{Longest Increasing Subsequence}, using carefully curated datasets with unique optima. Across multiple reward designs, we find that RLVR improves evaluation metrics but often by reinforcing superficial heuristics rather than acquiring new reasoning strategies. These findings highlight the limits of RLVR generalization, emphasizing the importance of benchmarks that disentangle genuine mathematical reasoning from shortcut exploitation and provide faithful measures of progress. Code available at https://github.com/xashru/rlvr-seq-generalization.

---

### e1: Learning Adaptive Control of Reasoning Effort

Authors: Michael Kleinman, Matthew Trager, Alessandro Achille, Wei Xia, Stefano Soatto
Published: 2025-10-30
Categories: cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2510.27042v3

Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning. Users may prefer to allocate different amounts of reasoning effort depending on how they value output quality versus latency and cost. To leverage this tradeoff effectively, users need fine-grained control over the amount of thinking used for a particular query, but few approaches enable such control. Existing methods require users to specify the absolute number of desired tokens, but this requires knowing the difficulty of the problem beforehand to appropriately set the token budget for a query. To address these issues, we propose \textit{Adaptive Effort Control}, a self-adaptive reinforcement learning method that trains models to use a user-specified fraction of tokens relative to the current average chain-of-thought length for each query. This approach eliminates dataset- and phase-specific hyperparameter tuning. Users can dynamically adjust the cost-accuracy trade-off through a continuous effort parameter specified at inference time. We observe that the model automatically learns to allocate resources proportionally to the task difficulty. Across model scales ranging from 1.5B to 32B parameters, our approach reduces chain-of-thought length on math tasks by 1.9-3.7x at matched accuracy relative to the base model used for RL training.

---

### GeoPep: A geometry-aware masked language model for protein-peptide binding site prediction

Authors: Dian Chen, Yunkai Chen, Tong Lin, Sijie Chen, Xiaolin Cheng
Published: 2025-10-30
Categories: eess.SP, cs.LG
arXiv: https://arxiv.org/abs/2510.27040v1

Multimodal approaches that integrate protein structure and sequence have achieved remarkable success in protein-protein interface prediction. However, extending these methods to protein-peptide interactions remains challenging due to the inherent conformational flexibility of peptides and the limited availability of structural data that hinder direct training of structure-aware models. To address these limitations, we introduce GeoPep, a novel framework for peptide binding site prediction that leverages transfer learning from ESM3, a multimodal protein foundation model. GeoPep fine-tunes ESM3's rich pre-learned representations from protein-protein binding to address the limited availability of protein-peptide binding data. The fine-tuned model is further integrated with a parameter-efficient neural network architecture capable of learning complex patterns from sparse data. Furthermore, the model is trained using distance-based loss functions that exploit 3D structural information to enhance binding site prediction. Comprehensive evaluations demonstrate that GeoPep significantly outperforms existing methods in protein-peptide binding site prediction by effectively capturing sparse and heterogeneous binding patterns.

---

### Dataset Creation and Baseline Models for Sexism Detection in Hausa

Authors: Fatima Adam Muhammad, Shamsuddeen Muhammad Hassan, Isa Inuwa-Dutse
Published: 2025-10-30
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2510.27038v1

Sexism reinforces gender inequality and social exclusion by perpetuating stereotypes, bias, and discriminatory norms. Noting how online platforms enable various forms of sexism to thrive, there is a growing need for effective sexism detection and mitigation strategies. While computational approaches to sexism detection are widespread in high-resource languages, progress remains limited in low-resource languages where limited linguistic resources and cultural differences affect how sexism is expressed and perceived. This study introduces the first Hausa sexism detection dataset, developed through community engagement, qualitative coding, and data augmentation. For cultural nuances and linguistic representation, we conducted a two-stage user study (n=66) involving native speakers to explore how sexism is defined and articulated in everyday discourse. We further experiment with both traditional machine learning classifiers and pre-trained multilingual language models and evaluating the effectiveness few-shot learning in detecting sexism in Hausa. Our findings highlight challenges in capturing cultural nuance, particularly with clarification-seeking and idiomatic expressions, and reveal a tendency for many false positives in such cases.

---

### Elastic Architecture Search for Efficient Language Models

Authors: Shang Wang
Published: 2025-10-30
Categories: cs.CL, cs.AI, cs.LG, cs.NE
arXiv: https://arxiv.org/abs/2510.27037v1

As large pre-trained language models become increasingly critical to natural language understanding (NLU) tasks, their substantial computational and memory requirements have raised significant economic and environmental concerns. Addressing these challenges, this paper introduces the Elastic Language Model (ELM), a novel neural architecture search (NAS) method optimized for compact language models. ELM extends existing NAS approaches by introducing a flexible search space with efficient transformer blocks and dynamic modules for dimension and head number adjustment. These innovations enhance the efficiency and flexibility of the search process, which facilitates more thorough and effective exploration of model architectures. We also introduce novel knowledge distillation losses that preserve the unique characteristics of each block, in order to improve the discrimination between architectural choices during the search process. Experiments on masked language modeling and causal language modeling tasks demonstrate that models discovered by ELM significantly outperform existing methods.

---

### A Multi-Modal Neuro-Symbolic Approach for Spatial Reasoning-Based Visual Grounding in Robotics

Authors: Simindokht Jahangard, Mehrzad Mohammadi, Abhinav Dhall, Hamid Rezatofighi
Published: 2025-10-30
Categories: cs.RO, cs.AI, cs.CV
arXiv: https://arxiv.org/abs/2510.27033v1

Visual reasoning, particularly spatial reasoning, is a challenging cognitive task that requires understanding object relationships and their interactions within complex environments, especially in robotics domain. Existing vision_language models (VLMs) excel at perception tasks but struggle with fine-grained spatial reasoning due to their implicit, correlation-driven reasoning and reliance solely on images. We propose a novel neuro_symbolic framework that integrates both panoramic-image and 3D point cloud information, combining neural perception with symbolic reasoning to explicitly model spatial and logical relationships. Our framework consists of a perception module for detecting entities and extracting attributes, and a reasoning module that constructs a structured scene graph to support precise, interpretable queries. Evaluated on the JRDB-Reasoning dataset, our approach demonstrates superior performance and reliability in crowded, human_built environments while maintaining a lightweight design suitable for robotics and embodied AI applications.

---

### VitalLens 2.0: High-Fidelity rPPG for Heart Rate Variability Estimation from Face Video

Authors: Philipp V. Rouast
Published: 2025-10-30
Categories: cs.CV, cs.HC
arXiv: https://arxiv.org/abs/2510.27028v1

This report introduces VitalLens 2.0, a new deep learning model for estimating physiological signals from face video. This new model demonstrates a significant leap in accuracy for remote photoplethysmography (rPPG), enabling the robust estimation of not only heart rate (HR) and respiratory rate (RR) but also Heart Rate Variability (HRV) metrics. This advance is achieved through a combination of a new model architecture and a substantial increase in the size and diversity of our training data, now totaling 1,413 unique individuals. We evaluate VitalLens 2.0 on a new, combined test set of 422 unique individuals from four public and private datasets. When averaging results by individual, VitalLens 2.0 achieves a Mean Absolute Error (MAE) of 1.57 bpm for HR, 1.08 bpm for RR, 10.18 ms for HRV-SDNN, and 16.45 ms for HRV-RMSSD. These results represent a new state-of-the-art, significantly outperforming previous methods. This model is now available to developers via the VitalLens API at https://rouast.com/api.

---

### Incremental Human-Object Interaction Detection with Invariant Relation Representation Learning

Authors: Yana Wei, Zeen Chi, Chongyu Wang, Yu Wu, Shipeng Yan, Yongfei Liu, Xuming He
Published: 2025-10-30
Categories: cs.CV
arXiv: https://arxiv.org/abs/2510.27020v1

In open-world environments, human-object interactions (HOIs) evolve continuously, challenging conventional closed-world HOI detection models. Inspired by humans' ability to progressively acquire knowledge, we explore incremental HOI detection (IHOID) to develop agents capable of discerning human-object relations in such dynamic environments. This setup confronts not only the common issue of catastrophic forgetting in incremental learning but also distinct challenges posed by interaction drift and detecting zero-shot HOI combinations with sequentially arriving data. Therefore, we propose a novel exemplar-free incremental relation distillation (IRD) framework. IRD decouples the learning of objects and relations, and introduces two unique distillation losses for learning invariant relation features across different HOI combinations that share the same relation. Extensive experiments on HICO-DET and V-COCO datasets demonstrate the superiority of our method over state-of-the-art baselines in mitigating forgetting, strengthening robustness against interaction drift, and generalization on zero-shot HOIs. Code is available at \href{https://github.com/weiyana/ContinualHOI}{this HTTP URL}

---

### Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks

Authors: Alexander Heinlein, Taniya Kapoor
Published: 2025-10-30
Categories: math.NA, cs.LG
arXiv: https://arxiv.org/abs/2510.27018v1

Approximating the solutions of boundary value problems governed by partial differential equations with neural networks is challenging, largely due to the difficult training process. This difficulty can be partly explained by the spectral bias, that is, the slower convergence of high-frequency components, and can be mitigated by localizing neural networks via (overlapping) domain decomposition. We combine this localization with the Gauss-Newton method as the optimizer to obtain faster convergence than gradient-based schemes such as Adam; this comes at the cost of solving an ill-conditioned linear system in each iteration. Domain decomposition induces a block-sparse structure in the otherwise dense Gauss-Newton system, reducing the computational cost per iteration. Our numerical results indicate that combining localization and Gauss-Newton optimization is promising for neural network-based solvers for partial differential equations.

---

### Kad: A Framework for Proxy-based Test-time Alignment with Knapsack Approximation Deferral

Authors: Ayoub Hammal, Pierre Zweigenbaum, Caio Corro
Published: 2025-10-30
Categories: cs.CL
arXiv: https://arxiv.org/abs/2510.27017v2

Several previous works concluded that the largest part of generation capabilities of large language models (LLM) are learned (early) during pre-training. However, LLMs still require further alignment to adhere to downstream task requirements and stylistic preferences, among other desired properties. As LLMs continue to scale in terms of size, the computational cost of alignment procedures increase prohibitively. In this work, we propose a novel approach to circumvent these costs via proxy-based test-time alignment, i.e. using guidance from a small aligned model. Our approach can be described as a token-specific cascading method, where the token-specific deferral rule is reduced to 0-1 knapsack problem. In this setting, we derive primal and dual approximations of the optimal deferral decision. We experimentally show the benefits of our method both in task performance and speculative decoding speed.

---

### Semantically-Aware LLM Agent to Enhance Privacy in Conversational AI Services

Authors: Jayden Serenari, Stephen Lee
Published: 2025-10-30
Categories: cs.CL
arXiv: https://arxiv.org/abs/2510.27016v1

With the increasing use of conversational AI systems, there is growing concern over privacy leaks, especially when users share sensitive personal data in interactions with Large Language Models (LLMs). Conversations shared with these models may contain Personally Identifiable Information (PII), which, if exposed, could lead to security breaches or identity theft. To address this challenge, we present the Local Optimizations for Pseudonymization with Semantic Integrity Directed Entity Detection (LOPSIDED) framework, a semantically-aware privacy agent designed to safeguard sensitive PII data when using remote LLMs. Unlike prior work that often degrade response quality, our approach dynamically replaces sensitive PII entities in user prompts with semantically consistent pseudonyms, preserving the contextual integrity of conversations. Once the model generates its response, the pseudonyms are automatically depseudonymized, ensuring the user receives an accurate, privacy-preserving output. We evaluate our approach using real-world conversations sourced from ShareGPT, which we further augment and annotate to assess whether named entities are contextually relevant to the model's response. Our results show that LOPSIDED reduces semantic utility errors by a factor of 5 compared to baseline techniques, all while enhancing privacy.

---

### Quantitative Bounds for Length Generalization in Transformers

Authors: Zachary Izzo, Eshaan Nichani, Jason D. Lee
Published: 2025-10-30
Categories: cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2510.27015v1

We study the problem of length generalization (LG) in transformers: the ability of a model trained on shorter sequences to maintain performance when evaluated on much longer, previously unseen inputs. Prior work by Huang et al. (2025) established that transformers eventually achieve length generalization once the training sequence length exceeds some finite threshold, but left open the question of how large it must be. In this work, we provide the first quantitative bounds on the required training length for length generalization to occur. Motivated by previous empirical and theoretical work, we analyze LG in several distinct problem settings: $\ell_\infty$ error control vs. average error control over an input distribution, infinite-precision softmax attention vs. finite-precision attention (which reduces to an argmax) in the transformer, and one- vs. two-layer transformers. In all scenarios, we prove that LG occurs when the internal behavior of the transformer on longer sequences can be "simulated" by its behavior on shorter sequences seen during training. Our bounds give qualitative estimates for the length of training data required for a transformer to generalize, and we verify these insights empirically. These results sharpen our theoretical understanding of the mechanisms underlying extrapolation in transformers, and formalize the intuition that richer training data is required for generalization on more complex tasks.

---

### Enhancing Sentiment Classification with Machine Learning and Combinatorial Fusion

Authors: Sean Patten, Pin-Yu Chen, Christina Schweikert, D. Frank Hsu
Published: 2025-10-30
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.27014v1

This paper presents a novel approach to sentiment classification using the application of Combinatorial Fusion Analysis (CFA) to integrate an ensemble of diverse machine learning models, achieving state-of-the-art accuracy on the IMDB sentiment analysis dataset of 97.072\%. CFA leverages the concept of cognitive diversity, which utilizes rank-score characteristic functions to quantify the dissimilarity between models and strategically combine their predictions. This is in contrast to the common process of scaling the size of individual models, and thus is comparatively efficient in computing resource use. Experimental results also indicate that CFA outperforms traditional ensemble methods by effectively computing and employing model diversity. The approach in this paper implements the combination of a transformer-based model of the RoBERTa architecture with traditional machine learning models, including Random Forest, SVM, and XGBoost.

---

### Causal Masking on Spatial Data: An Information-Theoretic Case for Learning Spatial Datasets with Unimodal Language Models

Authors: Jared Junkin, Samuel Nathanson
Published: 2025-10-30
Categories: cs.AI, cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2510.27009v1

Language models are traditionally designed around causal masking. In domains with spatial or relational structure, causal masking is often viewed as inappropriate, and sequential linearizations are instead used. Yet the question of whether it is viable to accept the information loss introduced by causal masking on nonsequential data has received little direct study, in part because few domains offer both spatial and sequential representations of the same dataset. In this work, we investigate this issue in the domain of chess, which naturally supports both representations. We train language models with bidirectional and causal self-attention mechanisms on both spatial (board-based) and sequential (move-based) data. Our results show that models trained on spatial board states - \textit{even with causal masking} - consistently achieve stronger playing strength than models trained on sequential data. While our experiments are conducted on chess, our results are methodological and may have broader implications: applying causal masking to spatial data is a viable procedure for training unimodal LLMs on spatial data, and in some domains is even preferable to sequentialization.

---

### Mixture-of-Transformers Learn Faster: A Theoretical Study on Classification Problems

Authors: Hongbo Li, Qinhang Wu, Sen Lin, Yingbin Liang, Ness B. Shroff
Published: 2025-10-30
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.27004v1

Mixture-of-Experts (MoE) models improve transformer efficiency but lack a unified theoretical explanation, especially when both feed-forward and attention layers are allowed to specialize. To this end, we study the Mixture-of-Transformers (MoT), a tractable theoretical framework in which each transformer block acts as an expert governed by a continuously trained gating network. This design allows us to isolate and study the core learning dynamics of expert specialization and attention alignment. In particular, we develop a three-stage training algorithm with continuous training of the gating network, and show that each transformer expert specializes in a distinct class of tasks and that the gating network accurately routes data samples to the correct expert. Our analysis shows how expert specialization reduces gradient conflicts and makes each subtask strongly convex. We prove that the training drives the expected prediction loss to near zero in $O(\log(ε^{-1}))$ iteration steps, significantly improving over the $O(ε^{-1})$ rate for a single transformer. We further validate our theoretical findings through extensive real-data experiments, demonstrating the practical effectiveness of MoT. Together, these results offer the first unified theoretical account of transformer-level specialization and learning dynamics, providing practical guidance for designing efficient large-scale models.

---

### Are Online Sports Fan Communities Becoming More Offensive? A Quantitative Review of Topics, Trends, and Toxicity of r/PremierLeague

Authors: Muhammad Zeeshan Mazhar, Tolga Buz, Yiran Su
Published: 2025-10-30
Categories: cs.SI, cs.LG
arXiv: https://arxiv.org/abs/2510.27003v1

Online communities for sports fans have surged in popularity, with Reddit's r/PremierLeague emerging as a focal point for fans of one of the globe's most celebrated sports leagues. This boom has helped the Premier League make significant inroads into the US market, increasing viewership and sparking greater interest in its matches. Despite the league's broad appeal, there's still a notable gap in understanding its online fan community. Therefore, we analyzed a substantial dataset of over 1.1 million comments posted from 2013-2022 on r/PremierLeague. Our study delves into the sentiment, topics, and toxicity of these discussions, tracking trends over time, aiming to map out the conversation landscape. The rapid expansion has brought more diverse discussions, but also a worrying rise in negative sentiment and toxicity. Additionally, the subreddit has become a venue for users to voice frustrations about broader societal issues like racism, the COVID-19 pandemic, and political tensions.

---

### Jasmine: A Simple, Performant and Scalable JAX-based World Modeling Codebase

Authors: Mihir Mahajan, Alfred Nguyen, Franz Srambical, Stefan Bauer
Published: 2025-10-30
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2510.27002v1

While world models are increasingly positioned as a pathway to overcoming data scarcity in domains such as robotics, open training infrastructure for world modeling remains nascent. We introduce Jasmine, a performant JAX-based world modeling codebase that scales from single hosts to hundreds of accelerators with minimal code changes. Jasmine achieves an order-of-magnitude faster reproduction of the CoinRun case study compared to prior open implementations, enabled by performance optimizations across data loading, training and checkpointing. The codebase guarantees fully reproducible training and supports diverse sharding configurations. By pairing Jasmine with curated large-scale datasets, we establish infrastructure for rigorous benchmarking pipelines across model families and architectural ablations.

---

### A Framework for Fair Evaluation of Variance-Aware Bandit Algorithms

Authors: Elise Wolf
Published: 2025-10-30
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2510.27001v1

Multi-armed bandit (MAB) problems serve as a fundamental building block for more complex reinforcement learning algorithms. However, evaluating and comparing MAB algorithms remains challenging due to the lack of standardized conditions and replicability. This is particularly problematic for variance-aware extensions of classical methods like UCB, whose performance can heavily depend on the underlying environment. In this study, we address how performance differences between bandit algorithms can be reliably observed, and under what conditions variance-aware algorithms outperform classical ones. We present a reproducible evaluation designed to systematically compare eight classical and variance-aware MAB algorithms. The evaluation framework, implemented in our Bandit Playground codebase, features clearly defined experimental setups, multiple performance metrics (reward, regret, reward distribution, value-at-risk, and action optimality), and an interactive evaluation interface that supports consistent and transparent analysis. We show that variance-aware algorithms can offer advantages in settings with high uncertainty where the difficulty arises from subtle differences between arm rewards. In contrast, classical algorithms often perform equally well or better in more separable scenarios or if fine-tuned extensively. Our contributions are twofold: (1) a framework for systematic evaluation of MAB algorithms, and (2) insights into the conditions under which variance-aware approaches outperform their classical counterparts.

---

### AIOT based Smart Education System: A Dual Layer Authentication and Context-Aware Tutoring Framework for Learning Environments

Authors: Adithya Neelakantan, Pratik Satpute, Prerna Shinde, Tejas Manjunatha Devang
Published: 2025-10-30
Categories: cs.HC, cs.AI
arXiv: https://arxiv.org/abs/2510.26999v1

The AIoT-Based Smart Education System integrates Artificial Intelligence and IoT to address persistent challenges in contemporary classrooms: attendance fraud, lack of personalization, student disengagement, and inefficient resource use. The unified platform combines four core modules: (1) a dual-factor authentication system leveraging RFID-based ID scans and WiFi verification for secure, fraud-resistant attendance; (2) an AI-powered assistant that provides real-time, context-aware support and dynamic quiz generation based on instructor-supplied materials; (3) automated test generators to streamline adaptive assessment and reduce administrative overhead; and (4) the EcoSmart Campus module, which autonomously regulates classroom lighting, air quality, and temperature using IoT sensors and actuators. Simulated evaluations demonstrate the system's effectiveness in delivering robust real-time monitoring, fostering inclusive engagement, preventing fraudulent practices, and supporting operational scalability. Collectively, the AIoT-Based Smart Education System offers a secure, adaptive, and efficient learning environment, providing a scalable blueprint for future educational innovation and improved student outcomes through the synergistic application of artificial intelligence and IoT technologies.

---

### Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules

Authors: John J. Vastola, Samuel J. Gershman, Kanaka Rajan
Published: 2025-10-30
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.26997v1

Learning rules -- prescriptions for updating model parameters to improve performance -- are typically assumed rather than derived. Why do some learning rules work better than others, and under what assumptions can a given rule be considered optimal? We propose a theoretical framework that casts learning rules as policies for navigating (partially observable) loss landscapes, and identifies optimal rules as solutions to an associated optimal control problem. A range of well-known rules emerge naturally within this framework under different assumptions: gradient descent from short-horizon optimization, momentum from longer-horizon planning, natural gradients from accounting for parameter space geometry, non-gradient rules from partial controllability, and adaptive optimizers like Adam from online Bayesian inference of loss landscape shape. We further show that continual learning strategies like weight resetting can be understood as optimal responses to task uncertainty. By unifying these phenomena under a single objective, our framework clarifies the computational structure of learning and offers a principled foundation for designing adaptive algorithms.

---

### MoME: Mixture of Visual Language Medical Experts for Medical Imaging Segmentation

Authors: Arghavan Rezvani, Xiangyi Yan, Anthony T. Wu, Kun Han, Pooya Khosravi, Xiaohui Xie
Published: 2025-10-30
Categories: cs.CV
arXiv: https://arxiv.org/abs/2510.26996v1

In this study, we propose MoME, a Mixture of Visual Language Medical Experts, for Medical Image Segmentation. MoME adapts the successful Mixture of Experts (MoE) paradigm, widely used in Large Language Models (LLMs), for medical vision-language tasks. The architecture enables dynamic expert selection by effectively utilizing multi-scale visual features tailored to the intricacies of medical imagery, enriched with textual embeddings. This work explores a novel integration of vision-language models for this domain. Utilizing an assembly of 10 datasets, encompassing 3,410 CT scans, MoME demonstrates strong performance on a comprehensive medical imaging segmentation benchmark. Our approach explores the integration of foundation models for medical imaging, benefiting from the established efficacy of MoE in boosting model performance by incorporating textual information. Demonstrating competitive precision across multiple datasets, MoME explores a novel architecture for achieving robust results in medical image analysis.

---

### LLMs are Overconfident: Evaluating Confidence Interval Calibration with FermiEval

Authors: Elliot L. Epstein, John Winnicki, Thanawat Sornwanee, Rajat Dwaraknath
Published: 2025-10-30
Categories: stat.ME, cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2510.26995v1

Large language models (LLMs) excel at numerical estimation but struggle to correctly quantify uncertainty. We study how well LLMs construct confidence intervals around their own answers and find that they are systematically overconfident. To evaluate this behavior, we introduce FermiEval, a benchmark of Fermi-style estimation questions with a rigorous scoring rule for confidence interval coverage and sharpness. Across several modern models, nominal 99\% intervals cover the true answer only 65\% of the time on average. With a conformal prediction based approach that adjusts the intervals, we obtain accurate 99\% observed coverage, and the Winkler interval score decreases by 54\%. We also propose direct log-probability elicitation and quantile adjustment methods, which further reduce overconfidence at high confidence levels. Finally, we develop a perception-tunnel theory explaining why LLMs exhibit overconfidence: when reasoning under uncertainty, they act as if sampling from a truncated region of their inferred distribution, neglecting its tails.

---

### HADSF: Aspect Aware Semantic Control for Explainable Recommendation

Authors: Zheng Nie, Peijie Sun
Published: 2025-10-30
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.26994v2

Recent advances in large language models (LLMs) promise more effective information extraction for review-based recommender systems, yet current methods still (i) mine free-form reviews without scope control, producing redundant and noisy representations, (ii) lack principled metrics that link LLM hallucination to downstream effectiveness, and (iii) leave the cost-quality trade-off across model scales largely unexplored. We address these gaps with the Hyper-Adaptive Dual-Stage Semantic Framework (HADSF), a two-stage approach that first induces a compact, corpus-level aspect vocabulary via adaptive selection and then performs vocabulary-guided, explicitly constrained extraction of structured aspect-opinion triples. To assess the fidelity of the resulting representations, we introduce Aspect Drift Rate (ADR) and Opinion Fidelity Rate (OFR) and empirically uncover a nonmonotonic relationship between hallucination severity and rating prediction error. Experiments on approximately 3 million reviews across LLMs spanning 1.5B-70B parameters show that, when integrated into standard rating predictors, HADSF yields consistent reductions in prediction error and enables smaller models to achieve competitive performance in representative deployment scenarios. We release code, data pipelines, and metric implementations to support reproducible research on hallucination-aware, LLM-enhanced explainable recommendation. Code is available at https://github.com/niez233/HADSF

---

### SUSTAINABLE Platform: Seamless Smart Farming Integration Towards Agronomy Automation

Authors: Agorakis Bompotas, Konstantinos Koutras, Nikitas Rigas Kalogeropoulos, Panagiotis Kechagias, Dimitra Gariza, Athanasios P. Kalogeras, Christos Alexakos
Published: 2025-10-30
Categories: cs.AI, eess.SY
arXiv: https://arxiv.org/abs/2510.26989v2

The global agricultural sector is undergoing a transformative shift, driven by increasing food demands, climate variability and the need for sustainable practices. SUSTAINABLE is a smart farming platform designed to integrate IoT, AI, satellite imaging, and role-based task orchestration to enable efficient, traceable, and sustainable agriculture with a pilot usecase in viticulture. This paper explores current smart agriculture solutions, presents a comparative evaluation, and introduces SUSTAINABLE's key features, including satellite index integration, real-time environmental data, and role-aware task management tailored to Mediterranean vineyards.

---

### Robust fuzzy clustering for high-dimensional multivariate time series with outlier detection

Authors: Ziling Ma, Ángel López-Oriona, Hernando Ombao, Ying Sun
Published: 2025-10-30
Categories: stat.CO, stat.ML
arXiv: https://arxiv.org/abs/2510.26982v1

Fuzzy clustering provides a natural framework for modeling partial memberships, particularly important in multivariate time series (MTS) where state boundaries are often ambiguous. For example, in EEG monitoring of driver alertness, neural activity evolves along a continuum (from unconscious to fully alert, with many intermediate levels of drowsiness) so crisp labels are unrealistic and partial memberships are essential. However, most existing algorithms are developed for static, low-dimensional data and struggle with temporal dependence, unequal sequence lengths, high dimensionality, and contamination by noise or artifacts. To address these challenges, we introduce RFCPCA, a robust fuzzy subspace-clustering method explicitly tailored to MTS that, to the best of our knowledge, is the first of its kind to simultaneously: (i) learn membership-informed subspaces, (ii) accommodate unequal lengths and moderately high dimensions, (iii) achieve robustness through trimming, exponential reweighting, and a dedicated noise cluster, and (iv) automatically select all required hyperparameters. These components enable RFCPCA to capture latent temporal structure, provide calibrated membership uncertainty, and flag series-level outliers while remaining stable under contamination. On driver drowsiness EEG, RFCPCA improves clustering accuracy over related methods and yields a more reliable characterization of uncertainty and outlier structure in MTS.

---

### Fine-Grained Iterative Adversarial Attacks with Limited Computation Budget

Authors: Zhichao Hou, Weizhi Gao, Xiaorui Liu
Published: 2025-10-30
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2510.26981v1

This work tackles a critical challenge in AI safety research under limited compute: given a fixed computation budget, how can one maximize the strength of iterative adversarial attacks? Coarsely reducing the number of attack iterations lowers cost but substantially weakens effectiveness. To fulfill the attainable attack efficacy within a constrained budget, we propose a fine-grained control mechanism that selectively recomputes layer activations across both iteration-wise and layer-wise levels. Extensive experiments show that our method consistently outperforms existing baselines at equal cost. Moreover, when integrated into adversarial training, it attains comparable performance with only 30% of the original budget.

---

### Semantic Frame Aggregation-based Transformer for Live Video Comment Generation

Authors: Anam Fatima, Yi Yu, Janak Kapuriya, Julien Lalanne, Jainendra Shukla
Published: 2025-10-30
Categories: cs.CV, cs.CL
arXiv: https://arxiv.org/abs/2510.26978v1

Live commenting on video streams has surged in popularity on platforms like Twitch, enhancing viewer engagement through dynamic interactions. However, automatically generating contextually appropriate comments remains a challenging and exciting task. Video streams can contain a vast amount of data and extraneous content. Existing approaches tend to overlook an important aspect of prioritizing video frames that are most relevant to ongoing viewer interactions. This prioritization is crucial for producing contextually appropriate comments. To address this gap, we introduce a novel Semantic Frame Aggregation-based Transformer (SFAT) model for live video comment generation. This method not only leverages CLIP's visual-text multimodal knowledge to generate comments but also assigns weights to video frames based on their semantic relevance to ongoing viewer conversation. It employs an efficient weighted sum of frames technique to emphasize informative frames while focusing less on irrelevant ones. Finally, our comment decoder with a cross-attention mechanism that attends to each modality ensures that the generated comment reflects contextual cues from both chats and video. Furthermore, to address the limitations of existing datasets, which predominantly focus on Chinese-language content with limited video categories, we have constructed a large scale, diverse, multimodal English video comments dataset. Extracted from Twitch, this dataset covers 11 video categories, totaling 438 hours and 3.2 million comments. We demonstrate the effectiveness of our SFAT model by comparing it to existing methods for generating comments from live video and ongoing dialogue contexts.

---

### Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations

Authors: Jean-Philippe Corbeil, Asma Ben Abacha, Jerome Tremblay, Phillip Swazinna, Akila Jeeson Daniel, Miguel Del-Agua, Francois Beaulieu
Published: 2025-10-30
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2510.26974v1

Clinical documentation increasingly uses automatic speech recognition and summarization, yet converting conversations into actionable medical orders for Electronic Health Records remains unexplored. A solution to this problem can significantly reduce the documentation burden of clinicians and directly impact downstream patient care. We introduce the MEDIQA-OE 2025 shared task, the first challenge on extracting medical orders from doctor-patient conversations. Six teams participated in the shared task and experimented with a broad range of approaches, and both closed- and open-weight large language models (LLMs). In this paper, we describe the MEDIQA-OE task, dataset, final leaderboard ranking, and participants' solutions.

---

### Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence

Authors: Lívia Dutra, Arthur Lorenzi, Laís Berno, Franciany Campos, Karoline Biscardi, Kenneth Brown, Marcelo Viridiano, Frederico Belcavello, Ely Matos, Olívia Guaranha, Erik Santos, Sofia Reinach et al.
Published: 2025-10-30
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2510.26969v2

We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems.

---

### Using Salient Object Detection to Identify Manipulative Cookie Banners that Circumvent GDPR

Authors: Riley Grossman, Michael Smith, Cristian Borcea, Yi Chen
Published: 2025-10-30
Categories: cs.CY, cs.AI, cs.CV, cs.HC
arXiv: https://arxiv.org/abs/2510.26967v1

The main goal of this paper is to study how often cookie banners that comply with the General Data Protection Regulation (GDPR) contain aesthetic manipulation, a design tactic to draw users' attention to the button that permits personal data sharing. As a byproduct of this goal, we also evaluate how frequently the banners comply with GDPR and the recommendations of national data protection authorities regarding banner designs. We visited 2,579 websites and identified the type of cookie banner implemented. Although 45% of the relevant websites have fully compliant banners, we found aesthetic manipulation on 38% of the compliant banners. Unlike prior studies of aesthetic manipulation, we use a computer vision model for salient object detection to measure how salient (i.e., attention-drawing) each banner element is. This enables the discovery of new types of aesthetic manipulation (e.g., button placement), and leads us to conclude that aesthetic manipulation is more common than previously reported (38% vs 27% of banners). To study the effects of user and/or website location on cookie banner design, we include websites within the European Union (EU), where privacy regulation enforcement is more stringent, and websites outside the EU. We visited websites from IP addresses in the EU and from IP addresses in the United States (US). We find that 13.9% of EU websites change their banner design when the user is from the US, and EU websites are roughly 48.3% more likely to use aesthetic manipulation than non-EU websites, highlighting their innovative responses to privacy regulation.

---

### SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions

Authors: Md. Mehedi Hassan, Shafqat Alam, Shahriar Ahmed Seam, Maruf Ahmed
Published: 2025-10-30
Categories: cs.CV, eess.IV
arXiv: https://arxiv.org/abs/2510.26961v2

Automatic segmentation of diverse heterogeneous brain lesions using multi-modal MRI is a challenging problem in clinical neuroimaging, mainly because of the lack of generalizability and high prediction variance of pathology-specific deep learning models. In this work, we propose a unified and adaptive multi-stream framework called SYNAPSE-Net to perform robust multi-pathology segmentation with reduced performance variance. The framework is based on multi-stream convolutional encoders with global context modeling and a cross-modal attention fusion strategy to ensure stable and effective multi-modal feature integration. It also employs a variance-aware training strategy to enhance the robustness of the network across diverse tasks. The framework is extensively validated using three public challenge datasets: WMH MICCAI 2017, ISLES 2022, and BraTS 2020. The results show consistent improvements in boundary accuracy, delineation quality, and stability across diverse pathologies. This proposed framework achieved a high Dice similarity coefficient (DSC) of 0.831 and a low Hausdorff distance at the 95th percentile (HD95) of 3.03 on the WMH MICCAI 2017 dataset. It also achieved the lowest HD95 of 9.69 on the ISLES 2022 dataset and the highest tumor core DSC of 0.8651 on the BraTS 2020 dataset. These results validate the robustness of the proposed framework in providing a clinically relevant computer-aided solution for automated brain lesion segmentation. Source code and pretrained models are publicly available at https://github.com/mubid-01/SYNAPSE-Net-pre.

---

### Predicting Household Water Consumption Using Satellite and Street View Images in Two Indian Cities

Authors: Qiao Wang, Joseph George
Published: 2025-10-30
Categories: cs.LG, econ.GN
arXiv: https://arxiv.org/abs/2510.26957v1

Monitoring household water use in rapidly urbanizing regions is hampered by costly, time-intensive enumeration methods and surveys. We investigate whether publicly available imagery-satellite tiles, Google Street View (GSV) segmentation-and simple geospatial covariates (nightlight intensity, population density) can be utilized to predict household water consumption in Hubballi-Dharwad, India. We compare four approaches: survey features (benchmark), CNN embeddings (satellite, GSV, combined), and GSV semantic maps with auxiliary data. Under an ordinal classification framework, GSV segmentation plus remote-sensing covariates achieves 0.55 accuracy for water use, approaching survey-based models (0.59 accuracy). Error analysis shows high precision at extremes of the household water consumption distribution, but confusion among middle classes is due to overlapping visual proxies. We also compare and contrast our estimates for household water consumption to that of household subjective income. Our findings demonstrate that open-access imagery, coupled with minimal geospatial data, offers a promising alternative to obtaining reliable household water consumption estimates using surveys in urban analytics.

---

### Can machines think efficiently?

Authors: Adam Winchell
Published: 2025-10-30
Categories: cs.LG, cs.AI, cs.CY
arXiv: https://arxiv.org/abs/2510.26954v3

The Turing Test is no longer adequate for distinguishing human and machine intelligence. With advanced artificial intelligence systems already passing the original Turing Test and contributing to serious ethical and environmental concerns, we urgently need to update the test. This work expands upon the original imitation game by accounting for an additional factor: the energy spent answering the questions. By adding the constraint of energy, the new test forces us to evaluate intelligence through the lens of efficiency, connecting the abstract problem of thinking to the concrete reality of finite resources. Further, this proposed new test ensures the evaluation of intelligence has a measurable, practical finish line that the original test lacks. This additional constraint compels society to weigh the time savings of using artificial intelligence against its total resource cost.

---

### LLM-based Multi-class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

Authors: Seif Ikbarieh, Maanak Gupta, Elmahedi Mahalal
Published: 2025-10-30
Categories: cs.CR, cs.AI
arXiv: https://arxiv.org/abs/2510.26941v1

The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation suggestion. Despite advancements, evaluations remain purely qualitative, and the lack of a standardized, objective benchmark for quantitatively measuring AI-based attack analysis and mitigation hinders consistent assessment of model effectiveness. In this work, we propose a hybrid framework combining Machine Learning (ML) for multi-class attack detection with Large Language Models (LLMs) for attack behavior analysis and mitigation suggestion. After benchmarking several ML and Deep Learning (DL) classifiers on the Edge-IIoTset and CICIoT2023 datasets, we applied structured role-play prompt engineering with Retrieval-Augmented Generation (RAG) to guide ChatGPT-o3 and DeepSeek-R1 in producing detailed, context-aware responses. We introduce novel evaluation metrics for quantitative assessment to guide us and an ensemble of judge LLMs, namely ChatGPT-4o, DeepSeek-V3, Mixtral 8x7B Instruct, Gemini 2.5 Flash, Meta Llama 4, TII Falcon H1 34B Instruct, xAI Grok 3, and Claude 4 Sonnet, to independently evaluate the responses. Results show that Random Forest has the best detection model, and ChatGPT-o3 outperformed DeepSeek-R1 in attack analysis and mitigation.

---

### Mind the Gaps: Auditing and Reducing Group Inequity in Large-Scale Mobility Prediction

Authors: Ashwin Kumar, Hanyu Zhang, David A. Schweidel, William Yeoh
Published: 2025-10-30
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2510.26940v1

Next location prediction underpins a growing number of mobility, retail, and public-health applications, yet its societal impacts remain largely unexplored. In this paper, we audit state-of-the-art mobility prediction models trained on a large-scale dataset, highlighting hidden disparities based on user demographics. Drawing from aggregate census data, we compute the difference in predictive performance on racial and ethnic user groups and show a systematic disparity resulting from the underlying dataset, resulting in large differences in accuracy based on location and user groups. To address this, we propose Fairness-Guided Incremental Sampling (FGIS), a group-aware sampling strategy designed for incremental data collection settings. Because individual-level demographic labels are unavailable, we introduce Size-Aware K-Means (SAKM), a clustering method that partitions users in latent mobility space while enforcing census-derived group proportions. This yields proxy racial labels for the four largest groups in the state: Asian, Black, Hispanic, and White. Built on these labels, our sampling algorithm prioritizes users based on expected performance gains and current group representation. This method incrementally constructs training datasets that reduce demographic performance gaps while preserving overall accuracy. Our method reduces total disparity between groups by up to 40\% with minimal accuracy trade-offs, as evaluated on a state-of-art MetaPath2Vec model and a transformer-encoder model. Improvements are most significant in early sampling stages, highlighting the potential for fairness-aware strategies to deliver meaningful gains even in low-resource settings. Our findings expose structural inequities in mobility prediction pipelines and demonstrate how lightweight, data-centric interventions can improve fairness with little added complexity, especially for low-data applications.

---

### MM-OPERA: Benchmarking Open-ended Association Reasoning for Large Vision-Language Models

Authors: Zimeng Huang, Jinxin Ke, Xiaoxuan Fan, Yufeng Yang, Yang Liu, Liu Zhonghan, Zedi Wang, Junteng Dai, Haoyi Jiang, Yuyu Zhou, Keze Wang, Ziliang Chen
Published: 2025-10-30
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.26937v1

Large Vision-Language Models (LVLMs) have exhibited remarkable progress. However, deficiencies remain compared to human intelligence, such as hallucination and shallow pattern matching. In this work, we aim to evaluate a fundamental yet underexplored intelligence: association, a cornerstone of human cognition for creative thinking and knowledge integration. Current benchmarks, often limited to closed-ended tasks, fail to capture the complexity of open-ended association reasoning vital for real-world applications. To address this, we present MM-OPERA, a systematic benchmark with 11,497 instances across two open-ended tasks: Remote-Item Association (RIA) and In-Context Association (ICA), aligning association intelligence evaluation with human psychometric principles. It challenges LVLMs to resemble the spirit of divergent thinking and convergent associative reasoning through free-form responses and explicit reasoning paths. We deploy tailored LLM-as-a-Judge strategies to evaluate open-ended outputs, applying process-reward-informed judgment to dissect reasoning with precision. Extensive empirical studies on state-of-the-art LVLMs, including sensitivity analysis of task instances, validity analysis of LLM-as-a-Judge strategies, and diversity analysis across abilities, domains, languages, cultures, etc., provide a comprehensive and nuanced understanding of the limitations of current LVLMs in associative reasoning, paving the way for more human-like and general-purpose AI. The dataset and code are available at https://github.com/MM-OPERA-Bench/MM-OPERA.

---

### RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verification

Authors: Yunhao Yang, Neel P. Bhatt, Pranay Samineni, Rohan Siva, Zhanyang Wang, Ufuk Topcu
Published: 2025-10-30
Categories: cs.RO, cs.AI, cs.CL, cs.FL
arXiv: https://arxiv.org/abs/2510.26935v1

As AI systems migrate to safety-critical domains, verifying that their actions comply with well-defined rules remains a challenge. Formal methods provide provable guarantees but demand hand-crafted temporal-logic specifications, offering limited expressiveness and accessibility. Deep learning approaches enable evaluation of plans against natural-language constraints, yet their opaque decision process invites misclassifications with potentially severe consequences. We introduce RepV, a neurosymbolic verifier that unifies both views by learning a latent space where safe and unsafe plans are linearly separable. Starting from a modest seed set of plans labeled by an off-the-shelf model checker, RepV trains a lightweight projector that embeds each plan, together with a language model-generated rationale, into a low-dimensional space; a frozen linear boundary then verifies compliance for unseen natural-language rules in a single forward pass. Beyond binary classification, RepV provides a probabilistic guarantee on the likelihood of correct verification based on its position in the latent space. This guarantee enables a guarantee-driven refinement of the planner, improving rule compliance without human annotations. Empirical evaluations show that RepV improves compliance prediction accuracy by up to 15% compared to baseline methods while adding fewer than 0.2M parameters. Furthermore, our refinement framework outperforms ordinary fine-tuning baselines across various planning domains. These results show that safety-separable latent spaces offer a scalable, plug-and-play primitive for reliable neurosymbolic plan verification. Code and data are available at: https://repv-project.github.io/.

---

### Scale-Aware Curriculum Learning for Ddata-Efficient Lung Nodule Detection with YOLOv11

Authors: Yi Luo, Yike Guo, Hamed Hooshangnejad, Kai Ding
Published: 2025-10-30
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2510.26923v1

Lung nodule detection in chest CT is crucial for early lung cancer diagnosis, yet existing deep learning approaches face challenges when deployed in clinical settings with limited annotated data. While curriculum learning has shown promise in improving model training, traditional static curriculum strategies fail in data-scarce scenarios. We propose Scale Adaptive Curriculum Learning (SACL), a novel training strategy that dynamically adjusts curriculum design based on available data scale. SACL introduces three key mechanisms:(1) adaptive epoch scheduling, (2) hard sample injection, and (3) scale-aware optimization. We evaluate SACL on the LUNA25 dataset using YOLOv11 as the base detector. Experimental results demonstrate that while SACL achieves comparable performance to static curriculum learning on the full dataset in mAP50, it shows significant advantages under data-limited conditions with 4.6%, 3.5%, and 2.0% improvements over baseline at 10%, 20%, and 50% of training data respectively. By enabling robust training across varying data scales without architectural modifications, SACL provides a practical solution for healthcare institutions to develop effective lung nodule detection systems despite limited annotation resources.

---

### DC4GS: Directional Consistency-Driven Adaptive Density Control for 3D Gaussian Splatting

Authors: Moonsoo Jeong, Dongbeen Kim, Minseong Kim, Sungkil Lee
Published: 2025-10-30
Categories: cs.CV
arXiv: https://arxiv.org/abs/2510.26921v1

We present a Directional Consistency (DC)-driven Adaptive Density Control (ADC) for 3D Gaussian Splatting (DC4GS). Whereas the conventional ADC bases its primitive splitting on the magnitudes of positional gradients, we further incorporate the DC of the gradients into ADC, and realize it through the angular coherence of the gradients. Our DC better captures local structural complexities in ADC, avoiding redundant splitting. When splitting is required, we again utilize the DC to define optimal split positions so that sub-primitives best align with the local structures than the conventional random placement. As a consequence, our DC4GS greatly reduces the number of primitives (up to 30% in our experiments) than the existing ADC, and also enhances reconstruction fidelity greatly.

---

### Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence

Authors: Zachary Ravichandran, Fernando Cladera, Ankit Prabhu, Jason Hughes, Carlos Nieto-Granda, Varun Murali, Camillo Taylor, George J. Pappas, Vijay Kumar
Published: 2025-10-30
Categories: cs.RO, cs.AI
arXiv: https://arxiv.org/abs/2510.26915v2

While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large language models (LLMs) and vision language models (VLMs), opens the possibility of teams that infer mission-relevant semantics and subtasks given high-level natural language specifications and environmental context. However, current LLM- and VLM-enabled teaming methods typically assume well-structured and known environments, limiting performance in complex real-world settings. We address these limitations via SPINE-HT, a framework that grounds the reasoning abilities of LLMs in the evolving context of a heterogeneous robot team through a three-stage process. Given mission specifications and team capabilities in natural language, an LLM infers necessary subtasks. These subtasks are validated for feasibility and then assigned to robots based on capabilities such as traversability or perception. Finally, the framework uses feedback (semantic maps and task outcomes) acquired by the robots to update subtasks online. In simulation experiments with closed-loop perception and control, our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches. In real-world experiments with a Clearpath Jackal, a Clearpath Husky, a Boston Dynamics Spot, and a high-altitude UAV, our method achieves an 87.5% success rate in missions requiring reasoning about robot capabilities and refining subtasks with online feedback. More information is provided at: https://zacravichandran.github.io/SPINE-HT.

---