# Research Papers — 2025-12 · bundle page 115 of 175 > Full-text markdown of papers #5701–5750 of 8703 published in 2025-12, newest first. AI-readable mirrors of the full 8703-paper 2025-12 archive (50 papers per file). Canonical page: https://aipod.dev/papers/archive/2025-12/page/115 [← Previous bundle](https://aipod.dev/papers/archive/2025-12/page/114.md) [Next bundle →](https://aipod.dev/papers/archive/2025-12/page/116.md) ### Perception-Inspired Color Space Design for Photo White Balance Editing Authors: Yang Cheng, Ziteng Cui, Shenghan Su, Lin Gu, Zenghui Zhang Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09383v2 White balance (WB) is a key step in the image signal processor (ISP) pipeline that mitigates color casts caused by varying illumination and restores the scene's true colors. Currently, sRGB-based WB editing for post-ISP WB correction is widely used to address color constancy failures in the ISP pipeline when the original camera RAW is unavailable. However, additive color models (e.g., sRGB) are inherently limited by fixed nonlinear transformations and entangled color channels, which often impede their generalization to complex lighting conditions. To address these challenges, we propose a novel framework for WB correction that leverages a perception-inspired Learnable HSI (LHSI) color space. Built upon a cylindrical color model that naturally separates luminance from chromatic components, our framework further introduces dedicated parameters to enhance this disentanglement and learnable mapping to adaptively refine the flexibility. Moreover, a new Mamba-based network is introduced, which is tailored to the characteristics of the proposed LHSI color space. Experimental results on benchmark datasets demonstrate the superiority of our method, highlighting the potential of perception-inspired color space design in computational photography. The source code is available at https://github.com/YangCheng58/WB_Color_Space. --- ### Personalized Federated Distillation Assisted Vehicle Edge Caching Strategy Authors: Xun Li, Qiong Wu, Pingyi Fan, Kezhi Wang, Wen Chen, Cui Zhang Published: 2025-12-10 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.09378v2 Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge nodes. It is crucial to accurately predict the content that VUs are interested in without exposing their privacy. Traditional federated learning (FL) can protect user privacy by sharing models rather than raw data. However, the training of FL requires frequent model transmission, which can result in significant communication overhead. Additionally, vehicles may leave the road side unit (RSU) coverage area before training is completed, leading to training failures. To address these issues, in this paper, we propose a personalized federated distillation assisted vehicle edge caching strategy. The simulation results demonstrate that the proposed vehicle edge caching strategy has good robustness to variations in vehicle speed, significantly reducing communication overhead. --- ### Rates and architectures for learning geometrically non-trivial operators Authors: T. Mitchell Roddenberry, Leo Tzou, Ivan Dokmanić, Maarten V. de Hoop, Richard G. Baraniuk Published: 2025-12-10 Categories: cs.LG, cs.CV, eess.IV, math.DG arXiv: https://arxiv.org/abs/2512.09376v1 Deep learning methods have proven capable of recovering operators between high-dimensional spaces, such as solution maps of PDEs and similar objects in mathematical physics, from very few training samples. This phenomenon of data-efficiency has been proven for certain classes of elliptic operators with simple geometry, i.e., operators that do not change the domain of the function or propagate singularities. However, scientific machine learning is commonly used for problems that do involve the propagation of singularities in a priori unknown ways, such as waves, advection, and fluid dynamics. In light of this, we expand the learning theory to include double fibration transforms--geometric integral operators that include generalized Radon and geodesic ray transforms. We prove that this class of operators does not suffer from the curse of dimensionality: the error decays superalgebraically, that is, faster than any fixed power of the reciprocal of the number of training samples. Furthermore, we investigate architectures that explicitly encode the geometry of these transforms, demonstrating that an architecture reminiscent of cross-attention based on levelset methods yields a parameterization that is universal, stable, and learns double fibration transforms from very few training examples. Our results contribute to a rapidly-growing line of theoretical work on learning operators for scientific machine learning. --- ### Log NeRF: Comparing Spaces for Learning Radiance Fields Authors: Sihe Chen, Luv Verma, Bruce A. Maxwell Published: 2025-12-10 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2512.09375v1 Neural Radiance Fields (NeRF) have achieved remarkable results in novel view synthesis, typically using sRGB images for supervision. However, little attention has been paid to the color space in which the network is learning the radiance field representation. Inspired by the BiIlluminant Dichromatic Reflection (BIDR) model, which suggests that a logarithmic transformation simplifies the separation of illumination and reflectance, we hypothesize that log RGB space enables NeRF to learn a more compact and effective representation of scene appearance. To test this, we captured approximately 30 videos using a GoPro camera, ensuring linear data recovery through inverse encoding. We trained NeRF models under various color space interpretations linear, sRGB, GPLog, and log RGB by converting each network output to a common color space before rendering and loss computation, enforcing representation learning in different color spaces. Quantitative and qualitative evaluations demonstrate that using a log RGB color space consistently improves rendering quality, exhibits greater robustness across scenes, and performs particularly well in low light conditions while using the same bit-depth input images. Further analysis across different network sizes and NeRF variants confirms the generalization and stability of the log space advantage. --- ### FUSER: Feed-Forward MUltiview 3D Registration Transformer and SE(3)$^N$ Diffusion Refinement Authors: Haobo Jiang, Jin Xie, Jian Yang, Liang Yu, Jianmin Zheng Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09373v3 Registration of multiview point clouds conventionally relies on extensive pairwise matching to build a pose graph for global synchronization, which is computationally expensive and inherently ill-posed without holistic geometric constraints. This paper proposes FUSER, the first feed-forward multiview registration transformer that jointly processes all scans in a unified, compact latent space to directly predict global poses without any pairwise estimation. To maintain tractability, FUSER encodes each scan into low-resolution superpoint features via a sparse 3D CNN that preserves absolute translation cues, and performs efficient intra- and inter-scan reasoning through a Geometric Alternating Attention module. Particularly, we transfer 2D attention priors from off-the-shelf foundation models to enhance 3D feature interaction and geometric consistency. Building upon FUSER, we further introduce FUSER-DF, an SE(3)$^N$ diffusion refinement framework to correct FUSER's estimates via denoising in the joint SE(3)$^N$ space. FUSER acts as a surrogate multiview registration model to construct the denoiser, and a prior-conditioned SE(3)$^N$ variational lower bound is derived for denoising supervision. Extensive experiments on 3DMatch, ScanNet and ArkitScenes demonstrate that our approach achieves the superior registration accuracy and outstanding computational efficiency. --- ### Encoder-Free Knowledge-Graph Reasoning with LLMs via Hyperdimensional Path Retrieval Authors: Yezi Liu, William Youngwoo Chung, Hanning Chen, Calvin Yeung, Mohsen Imani Published: 2025-12-10 Categories: cs.LG, cs.CL arXiv: https://arxiv.org/abs/2512.09369v2 Recent progress in large language models (LLMs) has made knowledge-grounded reasoning increasingly practical, yet KG-based QA systems often pay a steep price in efficiency and transparency. In typical pipelines, symbolic paths are scored by neural encoders or repeatedly re-ranked by multiple LLM calls, which inflates latency and GPU cost and makes the decision process hard to audit. We introduce PathHD, an encoder-free framework for knowledge-graph reasoning that couples hyperdimensional computing (HDC) with a single LLM call per query. Given a query, PathHD represents relation paths as block-diagonal GHRR hypervectors, retrieves candidate paths using a calibrated blockwise cosine similarity with Top-K pruning, and then performs a one-shot LLM adjudication that outputs the final answer together with supporting, citeable paths. The design is enabled by three technical components: (i) an order-sensitive, non-commutative binding operator for composing multi-hop paths, (ii) a robust similarity calibration that stabilizes hypervector retrieval, and (iii) an adjudication stage that preserves interpretability while avoiding per-path LLM scoring. Across WebQSP, CWQ, and GrailQA, PathHD matches or improves Hits@1 compared to strong neural baselines while using only one LLM call per query, reduces end-to-end latency by $40-60\%$, and lowers GPU memory by $3-5\times$ due to encoder-free retrieval. Overall, the results suggest that carefully engineered HDC path representations can serve as an effective substrate for efficient and faithful KG-LLM reasoning, achieving a strong accuracy-efficiency-interpretability trade-off. --- ### CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning Authors: Mingyuan Li, Chunyu Liu, Zhuojun Li, Xiao Liu, Guangsheng Yu, Bo Du, Jun Shen, Qiang Wu Published: 2025-12-10 Categories: cs.LG, stat.ME arXiv: https://arxiv.org/abs/2512.09368v2 Traffic accidents result in millions of injuries and fatalities globally, with a significant number occurring at intersections each year. Traffic Signal Control (TSC) is an effective strategy for enhancing safety at these urban junctures. Despite the growing popularity of Reinforcement Learning (RL) methods in optimizing TSC, these methods often prioritize driving efficiency over safety, thus failing to address the critical balance between these two aspects. Additionally, these methods usually need more interpretability. CounterFactual (CF) learning is a promising approach for various causal analysis fields. In this study, we introduce a novel framework to improve RL for safety aspects in TSC. This framework introduces a novel method based on CF learning to address the question: ``What if, when an unsafe event occurs, we backtrack to perform alternative actions, and will this unsafe event still occur in the subsequent period?'' To answer this question, we propose a new structure causal model to predict the result after executing different actions, and we propose a new CF module that integrates with additional ``X'' modules to promote safe RL practices. Our new algorithm, CFLight, which is derived from this framework, effectively tackles challenging safety events and significantly improves safety at intersections through a near-zero collision control strategy. Through extensive numerical experiments on both real-world and synthetic datasets, we demonstrate that CFLight reduces collisions and improves overall traffic performance compared to conventional RL methods and the recent safe RL model. Moreover, our method represents a generalized and safe framework for RL methods, opening possibilities for applications in other domains. The data and code are available in the github https://github.com/AdvancedAI-ComplexSystem/SmartCity/tree/main/CFLight. --- ### Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback Authors: Dimitra Maoutsa Published: 2025-12-10 Categories: q-bio.NC, cond-mat.dis-nn, cs.LG, physics.bio-ph arXiv: https://arxiv.org/abs/2512.09366v2 Biological neural networks learn complex behaviors from sparse, delayed feedback using local synaptic plasticity, yet the mechanisms enabling structured credit assignment remain elusive. In contrast, artificial recurrent networks solving similar tasks typically rely on biologically implausible global learning rules or hand-crafted local updates. The space of local plasticity rules capable of supporting learning from delayed reinforcement remains largely unexplored. Here, we present a meta-learning framework that discovers local learning rules for structured credit assignment in recurrent networks trained with sparse feedback. Our approach interleaves local neo-Hebbian-like updates during task execution with an outer loop that optimizes plasticity parameters via \textbf{tangent-propagation through learning}. The resulting three-factor learning rules enable long-timescale credit assignment using only local information and delayed rewards, offering new insights into biologically grounded mechanisms for learning in recurrent circuits. --- ### KGOT: Unified Knowledge Graph and Optimal Transport Pseudo-Labeling for Molecule-Protein Interaction Prediction Authors: Jiayu Qin, Zhengquan Luo, Guy Tadmor, Changyou Chen, David Zeevi, Zhiqiang Xu Published: 2025-12-10 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.09365v1 Predicting molecule-protein interactions (MPIs) is a fundamental task in computational biology, with crucial applications in drug discovery and molecular function annotation. However, existing MPI models face two major challenges. First, the scarcity of labeled molecule-protein pairs significantly limits model performance, as available datasets capture only a small fraction of biological relevant interactions. Second, most methods rely solely on molecular and protein features, ignoring broader biological context such as genes, metabolic pathways, and functional annotations that could provide essential complementary information. To address these limitations, our framework first aggregates diverse biological datasets, including molecular, protein, genes and pathway-level interactions, and then develop an optimal transport-based approach to generate high-quality pseudo-labels for unlabeled molecule-protein pairs, leveraging the underlying distribution of known interactions to guide label assignment. By treating pseudo-labeling as a mechanism for bridging disparate biological modalities, our approach enables the effective use of heterogeneous data to enhance MPI prediction. We evaluate our framework on multiple MPI datasets including virtual screening tasks and protein retrieval tasks, demonstrating substantial improvements over state-of-the-art methods in prediction accuracies and zero shot ability across unseen interactions. Beyond MPI prediction, our approach provides a new paradigm for leveraging diverse biological data sources to tackle problems traditionally constrained by single- or bi-modal learning, paving the way for future advances in computational biology and drug discovery. --- ### ASSIST-3D: Adapted Scene Synthesis for Class-Agnostic 3D Instance Segmentation Authors: Shengchao Zhou, Jiehong Lin, Jiahui Liu, Shizhen Zhao, Chirui Chang, Xiaojuan Qi Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09364v1 Class-agnostic 3D instance segmentation tackles the challenging task of segmenting all object instances, including previously unseen ones, without semantic class reliance. Current methods struggle with generalization due to the scarce annotated 3D scene data or noisy 2D segmentations. While synthetic data generation offers a promising solution, existing 3D scene synthesis methods fail to simultaneously satisfy geometry diversity, context complexity, and layout reasonability, each essential for this task. To address these needs, we propose an Adapted 3D Scene Synthesis pipeline for class-agnostic 3D Instance SegmenTation, termed as ASSIST-3D, to synthesize proper data for model generalization enhancement. Specifically, ASSIST-3D features three key innovations, including 1) Heterogeneous Object Selection from extensive 3D CAD asset collections, incorporating randomness in object sampling to maximize geometric and contextual diversity; 2) Scene Layout Generation through LLM-guided spatial reasoning combined with depth-first search for reasonable object placements; and 3) Realistic Point Cloud Construction via multi-view RGB-D image rendering and fusion from the synthetic scenes, closely mimicking real-world sensor data acquisition. Experiments on ScanNetV2, ScanNet++, and S3DIS benchmarks demonstrate that models trained with ASSIST-3D-generated data significantly outperform existing methods. Further comparisons underscore the superiority of our purpose-built pipeline over existing 3D scene synthesis approaches. --- ### StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation Authors: Ke Xing, Xiaojie Jin, Longfei Li, Yuyang Yin, Hanwen Liang, Guixun Luo, Chen Fang, Jue Wang, Konstantinos N. Plataniotis, Yao Zhao, Yunchao Wei Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09363v2 The growing adoption of XR devices has fueled strong demand for high-quality stereo video, yet its production remains costly and artifact-prone. To address this challenge, we present StereoWorld, an end-to-end framework that repurposes a pretrained video generator for high-fidelity monocular-to-stereo video generation. Our framework jointly conditions the model on the monocular video input while explicitly supervising the generation with a geometry-aware regularization to ensure 3D structural fidelity. A spatio-temporal tiling scheme is further integrated to enable efficient, high-resolution synthesis. To enable large-scale training and evaluation, we curate a high-definition stereo video dataset containing over 11M frames aligned to natural human interpupillary distance (IPD). Extensive experiments demonstrate that StereoWorld substantially outperforms prior methods, generating stereo videos with superior visual fidelity and geometric consistency. The project webpage is available at https://ke-xing.github.io/StereoWorld/. --- ### A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches Authors: Boge Lyu, Qianye Yin, Iris Denise Tommelein, Hanyang Liu, Karnamohit Ranka, Karthik Yeluripati, Junzhe Shi Published: 2025-12-10 Categories: cs.LG, econ.EM arXiv: https://arxiv.org/abs/2512.09360v1 The persistent volatility of construction material prices poses significant risks to cost estimation, budgeting, and project delivery, underscoring the urgent need for granular and scalable forecasting methods. This study develops a forecasting framework that leverages the Construction Specifications Institute (CSI) MasterFormat as the target data structure, enabling predictions at the six-digit section level and supporting detailed cost projections across a wide spectrum of building materials. To enhance predictive accuracy, the framework integrates explanatory variables such as raw material prices, commodity indexes, and macroeconomic indicators. Four time-series models, Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Vector Error Correction Model (VECM), and Chronos-Bolt, were evaluated under both baseline configurations (using CSI data only) and extended versions with explanatory variables. Results demonstrate that incorporating explanatory variables significantly improves predictive performance across all models. Among the tested approaches, the LSTM model consistently achieved the highest accuracy, with RMSE values as low as 1.390 and MAPE values of 0.957, representing improvements of up to 59\% over the traditional statistical time-series model, ARIMA. Validation across multiple CSI divisions confirmed the framework's scalability, while Division 06 (Wood, Plastics, and Composites) is presented in detail as a demonstration case. This research offers a robust methodology that enables owners and contractors to improve budgeting practices and achieve more reliable cost estimation at the Definitive level. --- ### Minimization of Functions on Dually Flat Spaces Using Geodesic Descent Based on Dual Connections Authors: Gaku Omiya, Fumiyasu Komaki Published: 2025-12-10 Categories: stat.CO, stat.ML arXiv: https://arxiv.org/abs/2512.09358v1 We propose geodesic-based optimization methods on dually flat spaces, where the geometric structure of the parameter manifold is closely related to the form of the objective function. A primary application is maximum likelihood estimation in statistical models, especially exponential families, whose model manifolds are dually flat. We show that an m-geodesic update, which directly optimizes the log-likelihood, can theoretically reach the maximum likelihood estimator in a single step. In contrast, an e-geodesic update has a practical advantage in cases where the parameter space is geodesically complete, allowing optimization without explicitly handling parameter constraints. We establish the theoretical properties of the proposed methods and validate their effectiveness through numerical experiments. --- ### Branching Strategies Based on Subgraph GNNs: A Study on Theoretical Promise versus Practical Reality Authors: Junru Zhou, Yicheng Wang, Pan Li Published: 2025-12-10 Categories: cs.LG, cs.AI, math.NA arXiv: https://arxiv.org/abs/2512.09355v1 Graph Neural Networks (GNNs) have emerged as a promising approach for ``learning to branch'' in Mixed-Integer Linear Programming (MILP). While standard Message-Passing GNNs (MPNNs) are efficient, they theoretically lack the expressive power to fully represent MILP structures. Conversely, higher-order GNNs (like 2-FGNNs) are expressive but computationally prohibitive. In this work, we investigate Subgraph GNNs as a theoretical middle ground. Crucially, while previous work (Chen et al., 2025) demonstrated that GNNs with 3-WL expressive power can approximate Strong Branching, we prove a sharper result: node-anchored Subgraph GNNs whose expressive power is strictly lower than 3-WL (Zhang et al., 2023) are sufficient to approximate Strong Branching scores. However, our extensive empirical evaluation on four benchmark datasets reveals a stark contrast between theory and practice. While node-anchored Subgraph GNNs theoretically offer superior branching decisions, their $O(n)$ complexity overhead results in significant memory bottlenecks and slower solving times than MPNNs and heuristics. Our results indicate that for MILP branching, the computational cost of expressive GNNs currently outweighs their gains in decision quality, suggesting that future research must focus on efficiency-preserving expressivity. --- ### Video-QTR: Query-Driven Temporal Reasoning Framework for Lightweight Video Understanding Authors: Xinkui Zhao, Zuxin Wang, Yifan Zhang, Guanjie Cheng, Yueshen Xu, Shuiguang Deng, Chang Liu, Naibo Wang, Jianwei Yin Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09354v1 The rapid development of multimodal large-language models (MLLMs) has significantly expanded the scope of visual language reasoning, enabling unified systems to interpret and describe complex visual content. However, applying these models to long-video understanding remains computationally intensive. Dense frame encoding generates excessive visual tokens, leading to high memory consumption, redundant computation, and limited scalability in real-world applications. This inefficiency highlights a key limitation of the traditional process-then-reason paradigm, which analyzes visual streams exhaustively before semantic reasoning. To address this challenge, we introduce Video-QTR (Query-Driven Temporal Reasoning), a lightweight framework that redefines video comprehension as a query-guided reasoning process. Instead of encoding every frame, Video-QTR dynamically allocates perceptual resources based on the semantic intent of the query, creating an adaptive feedback loop between reasoning and perception. Extensive experiments across five benchmarks: MSVD-QA, Activity Net-QA, Movie Chat, and Video MME demonstrate that Video-QTR achieves state-of-the-art performance while reducing input frame consumption by up to 73%. These results confirm that query-driven temporal reasoning provides an efficient and scalable solution for video understanding. --- ### TextGuider: Training-Free Guidance for Text Rendering via Attention Alignment Authors: Kanghyun Baek, Sangyub Lee, Jin Young Choi, Jaewoo Song, Daemin Park, Jooyoung Choi, Chaehun Shin, Bohyung Han, Sungroh Yoon Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09350v2 Despite recent advances, diffusion-based text-to-image models still struggle with accurate text rendering. Several studies have proposed fine-tuning or training-free refinement methods for accurate text rendering. However, the critical issue of text omission, where the desired text is partially or entirely missing, remains largely overlooked. In this work, we propose TextGuider, a novel training-free method that encourages accurate and complete text appearance by aligning textual content tokens and text regions in the image. Specifically, we analyze attention patterns in Multi-Modal Diffusion Transformer(MM-DiT) models, particularly for text-related tokens intended to be rendered in the image. Leveraging this observation, we apply latent guidance during the early stage of denoising steps based on two loss functions that we introduce. Our method achieves state-of-the-art performance in test-time text rendering, with significant gains in recall and strong results in OCR accuracy and CLIP score. --- ### Development and Testing for Perception Based Autonomous Landing of a Long-Range QuadPlane Authors: Ashik E Rasul, Humaira Tasnim, Ji Yu Kim, Young Hyun Lim, Scott Schmitz, Bruce W. Jo, Hyung-Jin Yoon Published: 2025-12-10 Categories: cs.RO, cs.CV arXiv: https://arxiv.org/abs/2512.09343v2 QuadPlanes combine the range efficiency of fixed-wing aircraft with the maneuverability of multi-rotor platforms for long-range autonomous missions. In GPS-denied or cluttered urban environments, perception-based landing is vital for reliable operation. Unlike structured landing zones, real-world sites are unstructured and highly variable, requiring strong generalization capabilities from the perception system. Deep neural networks (DNNs) provide a scalable solution for learning landing site features across diverse visual and environmental conditions. While perception-driven landing has been shown in simulation, real-world deployment introduces significant challenges. Payload and volume constraints limit high-performance edge AI devices like the NVIDIA Jetson Orin Nano, which are crucial for real-time detection and control. Accurate pose estimation during descent is necessary, especially in the absence of GPS, and relies on dependable visual-inertial odometry. Achieving this with limited edge AI resources requires careful optimization of the entire deployment framework. The flight characteristics of large QuadPlanes further complicate the problem. These aircraft exhibit high inertia, reduced thrust vectoring, and slow response times further complicate stable landing maneuvers. This work presents a lightweight QuadPlane system for efficient vision-based autonomous landing and visual-inertial odometry, specifically developed for long-range QuadPlane operations such as aerial monitoring. It describes the hardware platform, sensor configuration, and embedded computing architecture designed to meet demanding real-time, physical constraints. This establishes a foundation for deploying autonomous landing in dynamic, unstructured, GPS-denied environments. --- ### Visual Categorization Across Minds and Models: Cognitive Analysis of Human Labeling and Neuro-Symbolic Integration Authors: Chethana Prasad Kabgere Published: 2025-12-10 Categories: cs.AI, cs.CV, cs.LG arXiv: https://arxiv.org/abs/2512.09340v1 Understanding how humans and AI systems interpret ambiguous visual stimuli offers critical insight into the nature of perception, reasoning, and decision-making. This paper examines image labeling performance across human participants and deep neural networks, focusing on low-resolution, perceptually degraded stimuli. Drawing from computational cognitive science, cognitive architectures, and connectionist-symbolic hybrid models, we contrast human strategies such as analogical reasoning, shape-based recognition, and confidence modulation with AI's feature-based processing. Grounded in Marr's tri-level hypothesis, Simon's bounded rationality, and Thagard's frameworks of representation and emotion, we analyze participant responses in relation to Grad-CAM visualizations of model attention. Human behavior is further interpreted through cognitive principles modeled in ACT-R and Soar, revealing layered and heuristic decision strategies under uncertainty. Our findings highlight key parallels and divergences between biological and artificial systems in representation, inference, and confidence calibration. The analysis motivates future neuro-symbolic architectures that unify structured symbolic reasoning with connectionist representations. Such architectures, informed by principles of embodiment, explainability, and cognitive alignment, offer a path toward AI systems that are not only performant but also interpretable and cognitively grounded. --- ### Relightable and Dynamic Gaussian Avatar Reconstruction from Monocular Video Authors: Seonghwa Choi, Moonkyeong Choi, Mingyu Jang, Jaekyung Kim, Jianfei Cai, Wen-Huang Cheng, Sanghoon Lee Published: 2025-12-10 Categories: cs.CV, cs.MM arXiv: https://arxiv.org/abs/2512.09335v2 Modeling relightable and animatable human avatars from monocular video is a long-standing and challenging task. Recently, Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) methods have been employed to reconstruct the avatars. However, they often produce unsatisfactory photo-realistic results because of insufficient geometrical details related to body motion, such as clothing wrinkles. In this paper, we propose a 3DGS-based human avatar modeling framework, termed as Relightable and Dynamic Gaussian Avatar (RnD-Avatar), that presents accurate pose-variant deformation for high-fidelity geometrical details. To achieve this, we introduce dynamic skinning weights that define the human avatar's articulation based on pose while also learning additional deformations induced by body motion. We also introduce a novel regularization to capture fine geometric details under sparse visual cues. Furthermore, we present a new multi-view dataset with varied lighting conditions to evaluate relight. Our framework enables realistic rendering of novel poses and views while supporting photo-realistic lighting effects under arbitrary lighting conditions. Our method achieves state-of-the-art performance in novel view synthesis, novel pose rendering, and relighting. --- ### Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems Authors: Yutong Du, Zicheng Liu, Bo Wu, Jingwei Kou, Hang Li, Changyou Li, Yali Zong, Bo Qi Published: 2025-12-10 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.09333v1 This paper presents an improved physics-driven neural network (IPDNN) framework for solving electromagnetic inverse scattering problems (ISPs). A new Gaussian-localized oscillation-suppressing window (GLOW) activation function is introduced to stabilize convergence and enable a lightweight yet accurate network architecture. A dynamic scatter subregion identification strategy is further developed to adaptively refine the computational domain, preventing missed detections and reducing computational cost. Moreover, transfer learning is incorporated to extend the solver's applicability to practical scenarios, integrating the physical interpretability of iterative algorithms with the real-time inference capability of neural networks. Numerical simulations and experimental results demonstrate that the proposed solver achieves superior reconstruction accuracy, robustness, and efficiency compared with existing state-of-the-art methods. --- ### Self Distillation Fine-Tuning of Protein Language Models Improves Versatility in Protein Design Authors: Amin Tavakoli, Raswanth Murugan, Ozan Gokdemir, Arvind Ramanathan, Frances Arnold, Anima Anandkumar Published: 2025-12-10 Categories: cs.LG, cs.CE arXiv: https://arxiv.org/abs/2512.09329v1 Supervised fine-tuning (SFT) is a standard approach for adapting large language models to specialized domains, yet its application to protein sequence modeling and protein language models (PLMs) remains ad hoc. This is in part because high-quality annotated data are far more difficult to obtain for proteins than for natural language. We present a simple and general recipe for fast SFT of PLMs, designed to improve the fidelity, reliability, and novelty of generated protein sequences. Unlike existing approaches that require costly precompiled experimental datasets for SFT, our method leverages the PLM itself, integrating a lightweight curation pipeline with domain-specific filters to construct high-quality training data. These filters can independently refine a PLM's output and identify candidates for in vitro evaluation; when combined with SFT, they enable PLMs to generate more stable and functional enzymes, while expanding exploration into protein sequence space beyond natural variants. Although our approach is agnostic to both the choice of protein language model (PLM) and the protein system, we demonstrate its effectiveness with a genome-scale PLM (GenSLM) applied to the tryptophan synthase enzyme family. The supervised fine-tuned model generates sequences that are not only more novel but also display improved characteristics across both targeted design constraints and emergent protein property measures. --- ### UniLS: End-to-End Audio-Driven Avatars for Unified Listening and Speaking Authors: Xuangeng Chu, Ruicong Liu, Yifei Huang, Yun Liu, Yichen Peng, Bo Zheng Published: 2025-12-10 Categories: cs.CV, cs.SD arXiv: https://arxiv.org/abs/2512.09327v2 Generating lifelike conversational avatars requires modeling not just isolated speakers, but the dynamic, reciprocal interaction of speaking and listening. However, modeling the listener is exceptionally challenging: direct audio-driven training fails, producing stiff, static listening motions. This failure stems from a fundamental imbalance: the speaker's motion is strongly driven by speech audio, while the listener's motion primarily follows an internal motion prior and is only loosely guided by external speech. This challenge has led most methods to focus on speak-only generation. The only prior attempt at joint generation relies on extra speaker's motion to produce the listener. This design is not end-to-end, thereby hindering the real-time applicability. To address this limitation, we present UniLS, the first end-to-end framework for generating unified speak-listen expressions, driven by only dual-track audio. Our method introduces a novel two-stage training paradigm. Stage 1 first learns the internal motion prior by training an audio-free autoregressive generator, capturing the spontaneous dynamics of natural facial motion. Stage 2 then introduces the dual-track audio, fine-tuning the generator to modulate the learned motion prior based on external speech cues. Extensive evaluations show UniLS achieves state-of-the-art speaking accuracy. More importantly, it delivers up to 44.1\% improvement in listening metrics, generating significantly more diverse and natural listening expressions. This effectively mitigates the stiffness problem and provides a practical, high-fidelity audio-driven solution for interactive digital humans. Code and demos are available at https://xg-chu.site/project_unils/. --- ### Self-Supervised Learning with Gaussian Processes Authors: Yunshan Duan, Sinead Williamson Published: 2025-12-10 Categories: cs.LG, stat.ME arXiv: https://arxiv.org/abs/2512.09322v1 Self supervised learning (SSL) is a machine learning paradigm where models learn to understand the underlying structure of data without explicit supervision from labeled samples. The acquired representations from SSL have demonstrated useful for many downstream tasks including clustering, and linear classification, etc. To ensure smoothness of the representation space, most SSL methods rely on the ability to generate pairs of observations that are similar to a given instance. However, generating these pairs may be challenging for many types of data. Moreover, these methods lack consideration of uncertainty quantification and can perform poorly in out-of-sample prediction settings. To address these limitations, we propose Gaussian process self supervised learning (GPSSL), a novel approach that utilizes Gaussian processes (GP) models on representation learning. GP priors are imposed on the representations, and we obtain a generalized Bayesian posterior minimizing a loss function that encourages informative representations. The covariance function inherent in GPs naturally pulls representations of similar units together, serving as an alternative to using explicitly defined positive samples. We show that GPSSL is closely related to both kernel PCA and VICReg, a popular neural network-based SSL method, but unlike both allows for posterior uncertainties that can be propagated to downstream tasks. Experiments on various datasets, considering classification and regression tasks, demonstrate that GPSSL outperforms traditional methods in terms of accuracy, uncertainty quantification, and error control. --- ### Efficiency-Aware Computational Intelligence for Resource-Constrained Manufacturing Toward Edge-Ready Deployment Authors: Qianyu Zhou Published: 2025-12-10 Categories: cs.CE, cs.AI arXiv: https://arxiv.org/abs/2512.09319v1 Industrial cyber physical systems operate under heterogeneous sensing, stochastic dynamics, and shifting process conditions, producing data that are often incomplete, unlabeled, imbalanced, and domain shifted. High-fidelity datasets remain costly, confidential, and slow to obtain, while edge devices face strict limits on latency, bandwidth, and energy. These factors restrict the practicality of centralized deep learning, hinder the development of reliable digital twins, and increase the risk of error escape in safety-critical applications. Motivated by these challenges, this dissertation develops an efficiency grounded computational framework that enables data lean, physics-aware, and deployment ready intelligence for modern manufacturing environments. The research advances methods that collectively address core bottlenecks across multimodal and multiscale industrial scenarios. Generative strategies mitigate data scarcity and imbalance, while semi-supervised learning integrates unlabeled information to reduce annotation and simulation demands. Physics-informed representation learning strengthens interpretability and improves condition monitoring under small-data regimes. Spatially aware graph-based surrogate modeling provides efficient approximation of complex processes, and an edge cloud collaborative compression scheme supports real-time signal analytics under resource constraints. The dissertation also extends visual understanding through zero-shot vision language reasoning augmented by domain specific retrieval, enabling generalizable assessment in previously unseen scenarios. Together, these developments establish a unified paradigm of data efficient and resource aware intelligence that bridges laboratory learning with industrial deployment, supporting reliable decision-making across diverse manufacturing systems. --- ### Simultaneous Genetic Evolution of Neural Networks for Optimal SFC Embedding Authors: Theviyanthan Krishnamohan, Lauritz Thamsen, Paul Harvey Published: 2025-12-10 Categories: cs.NE, cs.AI arXiv: https://arxiv.org/abs/2512.09318v1 The reliance of organisations on computer networks is enabled by network programmability, which is typically achieved through Service Function Chaining. These chains virtualise network functions, link them, and programmatically embed them on networking infrastructure. Optimal embedding of Service Function Chains is an NP-hard problem, with three sub-problems, chain composition, virtual network function embedding, and link embedding, that have to be optimised simultaneously, rather than sequentially, for optimal results. Genetic Algorithms have been employed for this, but existing approaches either do not optimise all three sub-problems or do not optimise all three sub-problems simultaneously. We propose a Genetic Algorithm-based approach called GENESIS, which evolves three sine-function-activated Neural Networks, and funnels their output to a Gaussian distribution and an A* algorithm to optimise all three sub-problems simultaneously. We evaluate GENESIS on an emulator across 48 different data centre scenarios and compare its performance to two state-of-the-art Genetic Algorithms and one greedy algorithm. GENESIS produces an optimal solution for 100% of the scenarios, whereas the second-best method optimises only 71% of the scenarios. Moreover, GENESIS is the fastest among all Genetic Algorithms, averaging 15.84 minutes, compared to an average of 38.62 minutes for the second-best Genetic Algorithm. --- ### Functional Percolation: Criticality of Form and Function Authors: Galen J. Wilkerson Published: 2025-12-10 Categories: physics.soc-ph, cond-mat.stat-mech, cs.AI, physics.comp-ph arXiv: https://arxiv.org/abs/2512.09317v5 Understanding how network structure constrains and enables information processing is a central problem in the statistical mechanics of interacting systems. Here we study random networks across the structural percolation transition and analyze how connectivity governs realizable input-output transformations under cascade dynamics. Using Erdos-Renyi networks as a minimal ensemble, we examine structural, functional, and information-theoretic observables as functions of mean degree. We find that the emergence of the giant connected component coincides with a sharp transition in realizable information processing: complex input-output response functions become accessible, functional diversity increases rapidly, output entropy rises, and directed information flow, quantified by transfer entropy, extends beyond local neighborhoods. We term this coincidence of structural, functional, and informational transitions functional percolation, referring to a sharp expansion of the space of realizable input-output functions at the percolation threshold. Near criticality, networks exhibit a Pareto-optimal tradeoff between functional complexity and diversity, suggesting that percolation criticality may provide a general organizing principle of information processing capacity in systems with local interactions and propagating influences. --- ### Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Authors: Yuan Ma, Junlin Hou, Chao Zhang, Yukun Zhou, Zongyuan Ge, Haoran Xie, Lie Ju Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09315v1 Learning from noisy labels remains a major challenge in medical image analysis, where annotation demands expert knowledge and substantial inter-observer variability often leads to inconsistent or erroneous labels. Despite extensive research on learning with noisy labels (LNL), the robustness of existing methods in medical imaging has not been systematically assessed. To address this gap, we introduce LNMBench, a comprehensive benchmark for Label Noise in Medical imaging. LNMBench encompasses \textbf{10} representative methods evaluated across 7 datasets, 6 imaging modalities, and 3 noise patterns, establishing a unified and reproducible framework for robustness evaluation under realistic conditions. Comprehensive experiments reveal that the performance of existing LNL methods degrades substantially under high and real-world noise, highlighting the persistent challenges of class imbalance and domain variability in medical data. Motivated by these findings, we further propose a simple yet effective improvement to enhance model robustness under such conditions. The LNMBench codebase is publicly released to facilitate standardized evaluation, promote reproducible research, and provide practical insights for developing noise-resilient algorithms in both research and real-world medical applications.The codebase is publicly available on https://github.com/myyy777/LNMBench. --- ### Hetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT Devices Authors: Yuki Oda, Yuta Ono, Hiroshi Nakamura, Hideki Takase Published: 2025-12-10 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2512.09313v1 The continuous scaling of deep neural networks has fundamentally transformed machine learning, with larger models demonstrating improved performance across diverse tasks. This growth in model size has dramatically increased the computational resources required for the training process. Consequently, distributed approaches, such as Federated Learning and Split Learning, have become essential paradigms for scalable deployment. However, existing Split Learning approaches assume client homogeneity and uniform split points across all participants. This critically limits their applicability to real-world IoT systems where devices exhibit heterogeneity in computational resources. To address this limitation, this paper proposes Hetero-SplitEE, a novel method that enables heterogeneous IoT devices to train a shared deep neural network in parallel collaboratively. By integrating heterogeneous early exits into hierarchical training, our approach allows each client to select distinct split points (cut layers) tailored to its computational capacity. In addition, we propose two cooperative training strategies, the Sequential strategy and the Averaging strategy, to facilitate this collaboration among clients with different split points. The Sequential strategy trains clients sequentially with a shared server model to reduce computational overhead. The Averaging strategy enables parallel client training with periodic cross-layer aggregation. Extensive experiments on CIFAR-10, CIFAR-100, and STL-10 datasets using ResNet-18 demonstrate that our method maintains competitive accuracy while efficiently supporting diverse computational constraints, enabling practical deployment of collaborative deep learning in heterogeneous IoT ecosystems. --- ### Transformer-Driven Multimodal Fusion for Explainable Suspiciousness Estimation in Visual Surveillance Authors: Kuldeep Singh Yadav, Lalan Kumar Published: 2025-12-10 Categories: cs.CV, cs.CR arXiv: https://arxiv.org/abs/2512.09311v1 Suspiciousness estimation is critical for proactive threat detection and ensuring public safety in complex environments. This work introduces a large-scale annotated dataset, USE50k, along with a computationally efficient vision-based framework for real-time suspiciousness analysis. The USE50k dataset contains 65,500 images captured from diverse and uncontrolled environments, such as airports, railway stations, restaurants, parks, and other public areas, covering a broad spectrum of cues including weapons, fire, crowd density, abnormal facial expressions, and unusual body postures. Building on this dataset, we present DeepUSEvision, a lightweight and modular system integrating three key components, i.e., a Suspicious Object Detector based on an enhanced YOLOv12 architecture, dual Deep Convolutional Neural Networks (DCNN-I and DCNN-II) for facial expression and body-language recognition using image and landmark features, and a transformer-based Discriminator Network that adaptively fuses multimodal outputs to yield an interpretable suspiciousness score. Extensive experiments confirm the superior accuracy, robustness, and interpretability of the proposed framework compared to state-of-the-art approaches. Collectively, the USE50k dataset and the DeepUSEvision framework establish a strong and scalable foundation for intelligent surveillance and real-time risk assessment in safety-critical applications. --- ### A Distributed Framework for Privacy-Enhanced Vision Transformers on the Edge Authors: Zihao Ding, Mufeng Zhu, Zhongze Tang, Sheng Wei, Yao Liu Published: 2025-12-10 Categories: cs.DC, cs.CR, cs.CV arXiv: https://arxiv.org/abs/2512.09309v1 Nowadays, visual intelligence tools have become ubiquitous, offering all kinds of convenience and possibilities. However, these tools have high computational requirements that exceed the capabilities of resource-constrained mobile and wearable devices. While offloading visual data to the cloud is a common solution, it introduces significant privacy vulnerabilities during transmission and server-side computation. To address this, we propose a novel distributed, hierarchical offloading framework for Vision Transformers (ViTs) that addresses these privacy challenges by design. Our approach uses a local trusted edge device, such as a mobile phone or an Nvidia Jetson, as the edge orchestrator. This orchestrator partitions the user's visual data into smaller portions and distributes them across multiple independent cloud servers. By design, no single external server possesses the complete image, preventing comprehensive data reconstruction. The final data merging and aggregation computation occurs exclusively on the user's trusted edge device. We apply our framework to the Segment Anything Model (SAM) as a practical case study, which demonstrates that our method substantially enhances content privacy over traditional cloud-based approaches. Evaluations show our framework maintains near-baseline segmentation performance while substantially reducing the risk of content reconstruction and user data exposure. Our framework provides a scalable, privacy-preserving solution for vision tasks in the edge-cloud continuum. --- ### From SAM to DINOv2: Towards Distilling Foundation Models to Lightweight Baselines for Generalized Polyp Segmentation Authors: Shivanshu Agnihotri, Snehashis Majhi, Deepak Ranjan Nayak, Debesh Jha Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09307v1 Accurate polyp segmentation during colonoscopy is critical for the early detection of colorectal cancer and still remains challenging due to significant size, shape, and color variations, and the camouflaged nature of polyps. While lightweight baseline models such as U-Net, U-Net++, and PraNet offer advantages in terms of easy deployment and low computational cost, they struggle to deal with the above issues, leading to limited segmentation performance. In contrast, large-scale vision foundation models such as SAM, DINOv2, OneFormer, and Mask2Former have exhibited impressive generalization performance across natural image domains. However, their direct transfer to medical imaging tasks (e.g., colonoscopic polyp segmentation) is not straightforward, primarily due to the scarcity of large-scale datasets and lack of domain-specific knowledge. To bridge this gap, we propose a novel distillation framework, Polyp-DiFoM, that transfers the rich representations of foundation models into lightweight segmentation baselines, allowing efficient and accurate deployment in clinical settings. In particular, we infuse semantic priors from the foundation models into canonical architectures such as U-Net and U-Net++ and further perform frequency domain encoding for enhanced distillation, corroborating their generalization capability. Extensive experiments are performed across five benchmark datasets, such as Kvasir-SEG, CVC-ClinicDB, ETIS, ColonDB, and CVC-300. Notably, Polyp-DiFoM consistently outperforms respective baseline models significantly, as well as the state-of-the-art model, with nearly 9 times reduced computation overhead. The code is available at https://github.com/lostinrepo/PolypDiFoM. --- ### VABench: A Comprehensive Benchmark for Audio-Video Generation Authors: Daili Hua, Xizhi Wang, Bohan Zeng, Xinyi Huang, Hao Liang, Junbo Niu, Xinlong Chen, Quanqing Xu, Wentao Zhang Published: 2025-12-10 Categories: cs.CV, cs.SD arXiv: https://arxiv.org/abs/2512.09299v2 Recent advances in video generation have been remarkable, enabling models to produce visually compelling videos with synchronized audio. While existing video generation benchmarks provide comprehensive metrics for visual quality, they lack convincing evaluations for audio-video generation, especially for models aiming to generate synchronized audio-video outputs. To address this gap, we introduce VABench, a comprehensive and multi-dimensional benchmark framework designed to systematically evaluate the capabilities of synchronous audio-video generation. VABench encompasses three primary task types: text-to-audio-video (T2AV), image-to-audio-video (I2AV), and stereo audio-video generation. It further establishes two major evaluation modules covering 15 dimensions. These dimensions specifically assess pairwise similarities (text-video, text-audio, video-audio), audio-video synchronization, lip-speech consistency, and carefully curated audio and video question-answering (QA) pairs, among others. Furthermore, VABench covers seven major content categories: animals, human sounds, music, environmental sounds, synchronous physical sounds, complex scenes, and virtual worlds. We provide a systematic analysis and visualization of the evaluation results, aiming to establish a new standard for assessing video generation models with synchronous audio capabilities and to promote the comprehensive advancement of the field. --- ### Traffic Scene Small Target Detection Method Based on YOLOv8n-SPTS Model for Autonomous Driving Authors: Songhan Wu Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09296v1 This paper focuses on the key issue in autonomous driving: small target recognition in dynamic perception. Existing algorithms suffer from poor detection performance due to missing small target information, scale imbalance, and occlusion. We propose an improved YOLOv8n-SPTS model, which enhances the detection accuracy of small traffic targets through three key innovations: First, optimizing the feature extraction module. In the Backbone Bottleneck structure of YOLOv8n, 4 traditional convolution modules are replaced with Space-to-Depth Convolution (SPD-Conv) modules. This module retains fine-grained information through space-to-depth conversion, reduces information loss, and enhances the ability to capture features of low-resolution small targets. Second, enhancing feature fusion capability. The Spatial Pyramid Pooling - Fast Cross Stage Partial Connection (SPPFCSPC) module is introduced to replace the original SPPF module, integrating the multi-scale feature extraction from Spatial Pyramid Pooling (SPP) and the feature fusion mechanism of Cross Stage Partial Connection (CSP), thereby improving the model's contextual understanding of complex scenes and multi-scale feature expression ability. Third, designing a dedicated detection structure for small targets. A Triple-Stage Feature Pyramid (TSFP) structure is proposed, which adds a 160*160 small target detection head to the original detection heads to fully utilize high-resolution features in shallow layers; meanwhile, redundant large target detection heads are removed to balance computational efficiency. Comparative experiments on the VisDrone2019-DET dataset show that YOLOv8n-SPTS model ranks first in precision (61.9%), recall (48.3%), mAP@0.5 (52.6%), and mAP@0.5:0.95 (32.6%). Visualization results verify that the miss rate of small targets such as pedestrians and bicycles in occluded and dense scenes is significantly reduced. --- ### Distributional Shrinkage II: Higher-Order Scores Encode Brenier Map Authors: Tengyuan Liang Published: 2025-12-10 Categories: math.ST, cs.LG, math.PR, stat.ML arXiv: https://arxiv.org/abs/2512.09295v3 Consider the additive Gaussian model $Y = X + σZ$, where $X \sim P$ is an unknown signal, $Z \sim N(0,1)$ is independent of $X$, and $σ> 0$ is known. Let $Q$ denote the law of $Y$. We construct a hierarchy of denoisers $T_0, T_1, \ldots, T_\infty \colon \mathbb{R} \to \mathbb{R}$ that depend only on higher-order score functions $q^{(m)}/q$, $m \geq 1$, of $Q$ and require no knowledge of the law $P$. The $K$-th order denoiser $T_K$ involves scores up to order $2K{-}1$ and satisfies $W_r(T_K \sharp Q, P) = O(σ^{2(K+1)})$ for every $r \geq 1$; in the limit, $T_\infty$ recovers the monotone optimal transport map (Brenier map) pushing $Q$ onto $P$. We provide a complete characterization of the combinatorial structure governing this hierarchy through partial Bell polynomial recursions, making precise how higher-order score functions encode the Brenier map. We further establish rates of convergence for estimating these scores from $n$ i.i.d.\ draws from $Q$ under two complementary strategies: (i) plug-in kernel density estimation, and (ii) higher-order score matching. The construction reveals a precise interplay among higher-order Fisher-type information, optimal transport, and the combinatorics of integer partitions. --- ### Identifying Bias in Machine-generated Text Detection Authors: Kevin Stowe, Svetlana Afanaseva, Rodolfo Raimundo, Yitao Sun, Kailash Patil Published: 2025-12-10 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2512.09292v2 The meteoric rise in text generation capability has been accompanied by parallel growth in interest in machine-generated text detection: the capability to identify whether a given text was generated using a model or written by a person. While detection models show strong performance, they have the capacity to cause significant negative impacts. We explore potential biases in English machine-generated text detection systems. We curate a dataset of student essays and assess 16 different detection systems for bias across four attributes: gender, race/ethnicity, English-language learner (ELL) status, and economic status. We evaluate these attributes using regression-based models to determine the significance and power of the effects, as well as performing subgroup analysis. We find that while biases are generally inconsistent across systems, there are several key issues: several models tend to classify disadvantaged groups as machine-generated, ELL essays are more likely to be classified as machine-generated, economically disadvantaged students' essays are less likely to be classified as machine-generated, and non-White ELL essays are disproportionately classified as machine-generated relative to their White counterparts. Finally, we perform human annotation and find that while humans perform generally poorly at the detection task, they show no significant biases on the studied attributes. --- ### MelanomaNet: Explainable Deep Learning for Skin Lesion Classification Authors: Sukhrobbek Ilyosbekov Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09289v1 Automated skin lesion classification using deep learning has shown remarkable accuracy, yet clinical adoption remains limited due to the "black box" nature of these models. We present MelanomaNet, an explainable deep learning system for multi-class skin lesion classification that addresses this gap through four complementary interpretability mechanisms. Our approach combines an EfficientNet V2 backbone with GradCAM++ attention visualization, automated ABCDE clinical criterion extraction, Fast Concept Activation Vectors (FastCAV) for concept-based explanations, and Monte Carlo Dropout uncertainty quantification. We evaluate our system on the ISIC 2019 dataset containing 25,331 dermoscopic images across 9 diagnostic categories. Our model achieves 85.61% accuracy with a weighted F1 score of 0.8564, while providing clinically meaningful explanations that align model attention with established dermatological assessment criteria. The uncertainty quantification module decomposes prediction confidence into epistemic and aleatoric components, enabling automatic flagging of unreliable predictions for clinical review. Our results demonstrate that high classification performance can be achieved alongside comprehensive interpretability, potentially facilitating greater trust and adoption in clinical dermatology workflows. The source code is available at https://github.com/suxrobgm/explainable-melanoma --- ### FoundIR-v2: Optimizing Pre-Training Data Mixtures for Image Restoration Foundation Model Authors: Xiang Chen, Jinshan Pan, Jiangxin Dong, Jian Yang, Jinhui Tang Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09282v1 Recent studies have witnessed significant advances in image restoration foundation models driven by improvements in the scale and quality of pre-training data. In this work, we find that the data mixture proportions from different restoration tasks are also a critical factor directly determining the overall performance of all-in-one image restoration models. To this end, we propose a high-capacity diffusion-based image restoration foundation model, FoundIR-v2, which adopts a data equilibrium scheduling paradigm to dynamically optimize the proportions of mixed training datasets from different tasks. By leveraging the data mixing law, our method ensures a balanced dataset composition, enabling the model to achieve consistent generalization and comprehensive performance across diverse tasks. Furthermore, we introduce an effective Mixture-of-Experts (MoE)-driven scheduler into generative pre-training to flexibly allocate task-adaptive diffusion priors for each restoration task, accounting for the distinct degradation forms and levels exhibited by different tasks. Extensive experiments demonstrate that our method can address over 50 sub-tasks across a broader scope of real-world scenarios and achieves favorable performance against state-of-the-art approaches. --- ### LoGoColor: Local-Global 3D Colorization for 360° Scenes Authors: Yeonjin Chang, Juhwan Cho, Seunghyeon Seo, Wonsik Shin, Nojun Kwak Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09278v2 Single-channel 3D reconstruction is widely used in fields such as robotics and medical imaging. While these methods are good at reconstructing 3D geometry, their outputs are typically uncolored 3D models, making 3D colorization necessary for visualization. Recent 3D colorization studies address this problem by distilling 2D image colorization models. However, these approaches suffer from an inherent inconsistency of 2D image models. This results in colors being averaged during training, leading to monotonous and oversimplified results, particularly in complex 360° scenes. In contrast, we aim to preserve color diversity by generating a new set of consistently colorized training views, thereby suppressing the averaging process. Nevertheless, mitigating the averaging process introduces a new challenge: ensuring strict multi-view consistency across these colorized views. To achieve this, we propose \ourmethod, a pipeline designed to preserve color diversity by eliminating this guidance-averaging process with a `Local-Global' approach: we partition the scene into subscenes and explicitly tackle both inter-subscene and intra-subscene consistency using a fine-tuned multi-view diffusion model. We demonstrate our method achieves quantitatively and qualitatively more consistent and plausible 3D colorization on complex 360° scenes than existing methods. --- ### Dynamic Facial Expressions Analysis Based Parkinson's Disease Auxiliary Diagnosis Authors: Xiaochen Huang, Xiaochen Bi, Cuihua Lv, Xin Wang, Haoyan Zhang, Wenjing Jiang, Xin Ma, Yibin Li Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09276v1 Parkinson's disease (PD), a prevalent neurodegenerative disorder, significantly affects patients' daily functioning and social interactions. To facilitate a more efficient and accessible diagnostic approach for PD, we propose a dynamic facial expression analysis-based PD auxiliary diagnosis method. This method targets hypomimia, a characteristic clinical symptom of PD, by analyzing two manifestations: reduced facial expressivity and facial rigidity, thereby facilitating the diagnosis process. We develop a multimodal facial expression analysis network to extract expression intensity features during patients' performance of various facial expressions. This network leverages the CLIP architecture to integrate visual and textual features while preserving the temporal dynamics of facial expressions. Subsequently, the expression intensity features are processed and input into an LSTM-based classification network for PD diagnosis. Our method achieves an accuracy of 93.1%, outperforming other in-vitro PD diagnostic approaches. This technique offers a more convenient detection method for potential PD patients, improving their diagnostic experience. --- ### Impact of Positional Encoding: Clean and Adversarial Rademacher Complexity for Transformers under In-Context Regression Authors: Weiyi He, Yue Xing Published: 2025-12-10 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2512.09275v2 Positional encoding (PE) is a core architectural component of Transformers, yet its impact on the Transformer's generalization and robustness remains unclear. In this work, we provide the first generalization analysis for a single-layer Transformer under in-context regression that explicitly accounts for a completely trainable PE module. Our result shows that PE systematically enlarges the generalization gap. Extending to the adversarial setting, we derive the adversarial Rademacher generalization bound. We find that the gap between models with and without PE is magnified under attack, demonstrating that PE amplifies the vulnerability of models. Our bounds are empirically validated by a simulation study. Together, this work establishes a new framework for understanding the clean and adversarial generalization in ICL with PE. --- ### LongT2IBench: A Benchmark for Evaluating Long Text-to-Image Generation with Graph-structured Annotations Authors: Zhichao Yang, Tianjiao Gu, Jianjie Wang, Feiyu Lin, Xiangfei Sheng, Pengfei Chen, Leida Li Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09271v1 The increasing popularity of long Text-to-Image (T2I) generation has created an urgent need for automatic and interpretable models that can evaluate the image-text alignment in long prompt scenarios. However, the existing T2I alignment benchmarks predominantly focus on short prompt scenarios and only provide MOS or Likert scale annotations. This inherent limitation hinders the development of long T2I evaluators, particularly in terms of the interpretability of alignment. In this study, we contribute LongT2IBench, which comprises 14K long text-image pairs accompanied by graph-structured human annotations. Given the detail-intensive nature of long prompts, we first design a Generate-Refine-Qualify annotation protocol to convert them into textual graph structures that encompass entities, attributes, and relations. Through this transformation, fine-grained alignment annotations are achieved based on these granular elements. Finally, the graph-structed annotations are converted into alignment scores and interpretations to facilitate the design of T2I evaluation models. Based on LongT2IBench, we further propose LongT2IExpert, a LongT2I evaluator that enables multi-modal large language models (MLLMs) to provide both quantitative scores and structured interpretations through an instruction-tuning process with Hierarchical Alignment Chain-of-Thought (CoT). Extensive experiments and comparisons demonstrate the superiority of the proposed LongT2IExpert in alignment evaluation and interpretation. Data and code have been released in https://welldky.github.io/LongT2IBench-Homepage/. --- ### MoRel: Long-Range Flicker-Free 4D Motion Modeling via Anchor Relay-based Bidirectional Blending with Hierarchical Densification Authors: Sangwoon Kwak, Weeyoung Kwon, Jun Young Jeong, Geonho Kim, Won-Sik Cheong, Jihyong Oh Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09270v2 Recent advances in 4D Gaussian Splatting (4DGS) have extended the high-speed rendering capability of 3D Gaussian Splatting (3DGS) into the temporal domain, enabling real-time rendering of dynamic scenes. However, one of the major remaining challenges lies in modeling long-range motion-contained dynamic videos, where a naive extension of existing methods leads to severe memory explosion, temporal flickering, and failure to handle appearing or disappearing occlusions over time. To address these challenges, we propose a novel 4DGS framework characterized by an Anchor Relay-based Bidirectional Blending (ARBB) mechanism, named MoRel, which enables temporally consistent and memory-efficient modeling of long-range dynamic scenes. Our method progressively constructs locally canonical anchor spaces at key-frame time index and models inter-frame deformations at the anchor level, enhancing temporal coherence. By learning bidirectional deformations between KfA and adaptively blending them through learnable opacity control, our approach mitigates temporal discontinuities and flickering artifacts. We further introduce a Feature-variance-guided Hierarchical Densification (FHD) scheme that effectively densifies KfA's while keeping rendering quality, based on an assigned level of feature-variance. To effectively evaluate our model's capability to handle real-world long-range 4D motion, we newly compose long-range 4D motion-contained dataset, called SelfCap$_{\text{LR}}$. It has larger average dynamic motion magnitude, captured at spatially wider spaces, compared to previous dynamic video datasets. Overall, our MoRel achieves temporally coherent and flicker-free long-range 4D reconstruction while maintaining bounded memory usage, demonstrating both scalability and efficiency in dynamic Gaussian-based representations. --- ### Goal inference with Rao-Blackwellized Particle Filters Authors: Yixuan Wang, Dan P. Guralnik, Warren E. Dixon Published: 2025-12-10 Categories: cs.LG, cs.IR, eess.SY arXiv: https://arxiv.org/abs/2512.09269v2 Inferring the eventual goal of a mobile agent from noisy observations of its trajectory is a fundamental estimation problem. We initiate the study of such intent inference using a variant of a Rao-Blackwellized Particle Filter (RBPF), subject to the assumption that the agent's intent manifests through closed-loop behavior with a state-of-the-art provable practical stability property. Leveraging the assumed closed-form agent dynamics, the RBPF analytically marginalizes the linear-Gaussian substructure and updates particle weights only, improving sample efficiency over a standard particle filter. Two difference estimators are introduced: a Gaussian mixture model using the RBPF weights and a reduced version confining the mixture to the effective sample. We quantify how well the adversary can recover the agent's intent using information-theoretic leakage metrics and provide computable lower bounds on the Kullback-Leibler (KL) divergence between the true intent distribution and RBPF estimates via Gaussian-mixture KL bounds. We also provide a bound on the difference in performance between the two estimators, highlighting the fact that the reduced estimator performs almost as well as the complete one. Experiments illustrate fast and accurate intent recovery for compliant agents, motivating future work on designing intent-obfuscating controllers. --- ### Contrastive Learning for Semi-Supervised Deep Regression with Generalized Ordinal Rankings from Spectral Seriation Authors: Ce Wang, Weihang Dai, Hanru Bai, Xiaomeng Li Published: 2025-12-10 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.09267v1 Contrastive learning methods enforce label distance relationships in feature space to improve representation capability for regression models. However, these methods highly depend on label information to correctly recover ordinal relationships of features, limiting their applications to semi-supervised regression. In this work, we extend contrastive regression methods to allow unlabeled data to be used in the semi-supervised setting, thereby reducing the dependence on costly annotations. Particularly we construct the feature similarity matrix with both labeled and unlabeled samples in a mini-batch to reflect inter-sample relationships, and an accurate ordinal ranking of involved unlabeled samples can be recovered through spectral seriation algorithms if the level of error is within certain bounds. The introduction of labeled samples above provides regularization of the ordinal ranking with guidance from the ground-truth label information, making the ranking more reliable. To reduce feature perturbations, we further utilize the dynamic programming algorithm to select robust features for the matrix construction. The recovered ordinal relationship is then used for contrastive learning on unlabeled samples, and we thus allow more data to be used for feature representation learning, thereby achieving more robust results. The ordinal rankings can also be used to supervise predictions on unlabeled samples, serving as an additional training signal. We provide theoretical guarantees and empirical verification through experiments on various datasets, demonstrating that our method can surpass existing state-of-the-art semi-supervised deep regression methods. Our code have been released on https://github.com/xmed-lab/CLSS. --- ### Robust and Sparse Estimation of Unbounded Density Ratio under Heavy Contamination Authors: Ryosuke Nagumo, Hironori Fujisawa Published: 2025-12-10 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2512.09266v1 We examine the non-asymptotic properties of robust density ratio estimation (DRE) in contaminated settings. Weighted DRE is the most promising among existing methods, exhibiting doubly strong robustness from an asymptotic perspective. This study demonstrates that Weighted DRE achieves sparse consistency even under heavy contamination within a non-asymptotic framework. This method addresses two significant challenges in density ratio estimation and robust estimation. For density ratio estimation, we provide the non-asymptotic properties of estimating unbounded density ratios under the assumption that the weighted density ratio function is bounded. For robust estimation, we introduce a non-asymptotic framework for doubly strong robustness under heavy contamination, assuming that at least one of the following conditions holds: (i) contamination ratios are small, and (ii) outliers have small weighted values. This work provides the first non-asymptotic analysis of strong robustness under heavy contamination. --- ### FBA$^2$D: Frequency-based Black-box Attack for AI-generated Image Detection Authors: Xiaojing Chen, Dan Li, Lijun Peng, Jun YanŁetter, Zhiqing Guo, Junyang Chen, Xiao Lan, Zhongjie Ba, Yunfeng DiaoŁetter Published: 2025-12-10 Categories: cs.CR, cs.AI arXiv: https://arxiv.org/abs/2512.09264v1 The prosperous development of Artificial Intelligence-Generated Content (AIGC) has brought people's anxiety about the spread of false information on social media. Designing detectors for filtering is an effective defense method, but most detectors will be compromised by adversarial samples. Currently, most studies exposing AIGC security issues assume information on model structure and data distribution. In real applications, attackers query and interfere with models that provide services in the form of application programming interfaces (APIs), which constitutes the black-box decision-based attack paradigm. However, to the best of our knowledge, decision-based attacks on AIGC detectors remain unexplored. In this study, we propose \textbf{FBA$^2$D}: a frequency-based black-box attack method for AIGC detection to fill the research gap. Motivated by frequency-domain discrepancies between generated and real images, we develop a decision-based attack that leverages the Discrete Cosine Transform (DCT) for fine-grained spectral partitioning and selects frequency bands as query subspaces, improving both query efficiency and image quality. Moreover, attacks on AIGC detectors should mitigate initialization failures, preserve image quality, and operate under strict query budgets. To address these issues, we adopt an ``adversarial example soup'' method, averaging candidates from successive surrogate iterations and using the result as the initialization to accelerate the query-based attack. The empirical study on the Synthetic LSUN dataset and GenImage dataset demonstrate the effectiveness of our prosed method. This study shows the urgency of addressing practical AIGC security problems. --- ### From Forecast to Action: Uncertainty-Aware UAV Deployment for Ocean Drifter Recovery Authors: Jingeun Kim, Yong-Hyuk Kim, Yourim Yoon Published: 2025-12-10 Categories: cs.NE arXiv: https://arxiv.org/abs/2512.09260v2 We present a novel predict-then-optimize framework for maritime search operations that integrates trajectory forecasting with UAV deployment optimization-an end-to-end approach not addressed in prior work. A large language model predicts the drifter's trajectory, and spatial uncertainty is modeled using Gaussian-based particle sampling. Unlike traditional static deployment methods, we dynamically adapt UAV detection radii based on distance and optimize their placement using meta-heuristic algorithms. Experiments on real-world data from the Korean coastline demonstrate that our method, particularly the repair mechanism designed for this problem, significantly outperforms the random search baselines. This work introduces a practical and robust integration of trajectory prediction and spatial optimization for intelligent maritime rescue. --- ### ROI-Packing: Efficient Region-Based Compression for Machine Vision Authors: Md Eimran Hossain Eimon, Alena Krause, Ashan Perera, Juan Merlos, Hari Kalva, Velibor Adzic, Borko Furht Published: 2025-12-10 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.09258v1 This paper introduces ROI-Packing, an efficient image compression method tailored specifically for machine vision. By prioritizing regions of interest (ROI) critical to end-task accuracy and packing them efficiently while discarding less relevant data, ROI-Packing achieves significant compression efficiency without requiring retraining or fine-tuning of end-task models. Comprehensive evaluations across five datasets and two popular tasks-object detection and instance segmentation-demonstrate up to a 44.10% reduction in bitrate without compromising end-task accuracy, along with an 8.88 % improvement in accuracy at the same bitrate compared to the state-of-the-art Versatile Video Coding (VVC) codec standardized by the Moving Picture Experts Group (MPEG). --- ### Debiased Bayesian Inference for High-dimensional Regression Models Authors: Qihui Chen, Zheng Fang, Ruixuan Liu Published: 2025-12-10 Categories: econ.EM, math.ST, stat.CO, stat.ME, stat.ML arXiv: https://arxiv.org/abs/2512.09257v1 There has been significant progress in Bayesian inference based on sparsity-inducing (e.g., spike-and-slab and horseshoe-type) priors for high-dimensional regression models. The resulting posteriors, however, in general do not possess desirable frequentist properties, and the credible sets thus cannot serve as valid confidence sets even asymptotically. We introduce a novel debiasing approach that corrects the bias for the entire Bayesian posterior distribution. We establish a new Bernstein-von Mises theorem that guarantees the frequentist validity of the debiased posterior. We demonstrate the practical performance of our proposal through Monte Carlo simulations and two empirical applications in economics. --- ### GLACIA: Instance-Aware Positional Reasoning for Glacial Lake Segmentation via Multimodal Large Language Model Authors: Lalit Maurya, Saurabh Kaushik, Beth Tellman Published: 2025-12-10 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2512.09251v1 Glacial lake monitoring bears great significance in mitigating the anticipated risk of Glacial Lake Outburst Floods. However, existing segmentation methods based on convolutional neural networks (CNNs) and Vision Transformers (ViTs), remain constrained to pixel-level predictions, lacking high-level global scene semantics and human-interpretable reasoning. To address this, we introduce GLACIA (\textbf{G}lacial \textbf{LA}ke segmentation with \textbf{C}ontextual \textbf{I}nstance \textbf{A}wareness), the first framework that integrates large language models with segmentation capabilities to produce both accurate segmentation masks and corresponding spatial reasoning outputs. We construct the Glacial Lake Position Reasoning (GLake-Pos) dataset pipeline, which provides diverse, spatially grounded question-answer pairs designed to overcome the lack of instance-aware positional reasoning data in remote sensing. Comparative evaluation demonstrate that GLACIA (mIoU: 87.30) surpasses state-of-the-art method based on CNNs (mIoU: 78.55 - 79.01), ViTs (mIoU: 69.27 - 81.75), Geo-foundation models (mIoU: 76.37 - 87.10), and reasoning based segmentation methods (mIoU: 60.12 - 75.66). Our approach enables intuitive disaster preparedness and informed policy-making in the context of rapidly changing glacial environments by facilitating natural language interaction, thereby supporting more efficient and interpretable decision-making. The code is released on https://github.com/lalitmaurya47/GLACIA ---