# Research Papers — 2026-01 · bundle page 181 of 189 > Full-text markdown of papers #9001–9050 of 9403 published in 2026-01, newest first. AI-readable mirrors of the full 9403-paper 2026-01 archive (50 papers per file). Canonical page: https://aipod.dev/papers/archive/2026-01/page/181 [← Previous bundle](https://aipod.dev/papers/archive/2026-01/page/180.md) [Next bundle →](https://aipod.dev/papers/archive/2026-01/page/182.md) ### UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass Authors: Mengfei Li, Peng Li, Zheng Zhang, Jiahao Lu, Chengfeng Zhao, Wei Xue, Qifeng Liu, Sida Peng, Wenxiao Zhang, Wenhan Luo, Yuan Liu, Yike Guo Published: 2026-01-03 Categories: cs.CV arXiv: https://arxiv.org/abs/2601.01222v1 We present UniSH, a unified, feed-forward framework for joint metric-scale 3D scene and human reconstruction. A key challenge in this domain is the scarcity of large-scale, annotated real-world data, forcing a reliance on synthetic datasets. This reliance introduces a significant sim-to-real domain gap, leading to poor generalization, low-fidelity human geometry, and poor alignment on in-the-wild videos. To address this, we propose an innovative training paradigm that effectively leverages unlabeled in-the-wild data. Our framework bridges strong, disparate priors from scene reconstruction and HMR, and is trained with two core components: (1) a robust distillation strategy to refine human surface details by distilling high-frequency details from an expert depth model, and (2) a two-stage supervision scheme, which first learns coarse localization on synthetic data, then fine-tunes on real data by directly optimizing the geometric correspondence between the SMPL mesh and the human point cloud. This approach enables our feed-forward model to jointly recover high-fidelity scene geometry, human point clouds, camera parameters, and coherent, metric-scale SMPL bodies, all in a single forward pass. Extensive experiments demonstrate that our model achieves state-of-the-art performance on human-centric scene reconstruction and delivers highly competitive results on global human motion estimation, comparing favorably against both optimization-based frameworks and HMR-only methods. Project page: https://murphylmf.github.io/UniSH/ --- ### Correctness isnt Efficiency: Runtime Memory Divergence in LLM-Generated Code Authors: Prateek Rajput, Yewei Song, Abdoul Aziz Bonkoungou, Iyiola E. Olatunji, Abdoul Kader Kabore, Jacques Klein, Tegawendé F. Bissyandé Published: 2026-01-03 Categories: cs.SE, cs.AI arXiv: https://arxiv.org/abs/2601.01215v2 Large language models (LLMs) can generate programs that pass unit tests, but passing tests does not guarantee reliable runtime behavior. We find that different correct solutions to the same task can show very different memory and performance patterns, which can lead to hidden operational risks. We present a framework to measure execution-time memory stability across multiple correct generations. At the solution level, we introduce Dynamic Mean Pairwise Distance (DMPD), which uses Dynamic Time Warping to compare the shapes of memory-usage traces after converting them into Monotonic Peak Profiles (MPPs) to reduce transient noise. Aggregating DMPD across tasks yields a model-level Model Instability Score (MIS). Experiments on BigOBench and CodeContests show substantial runtime divergence among correct solutions. Instability often increases with higher sampling temperature even when pass@1 improves. We also observe correlations between our stability measures and software engineering indicators such as cognitive and cyclomatic complexity, suggesting links between operational behavior and maintainability. Our results support stability-aware selection among passing candidates in CI/CD to reduce operational risk without sacrificing correctness. Artifacts are available. --- ### Promptable Foundation Models for SAR Remote Sensing: Adapting the Segment Anything Model for Snow Avalanche Segmentation Authors: Riccardo Gelato, Carlo Sgaravatti, Jakob Grahn, Giacomo Boracchi, Filippo Maria Bianchi Published: 2026-01-03 Categories: cs.CV, cs.LG arXiv: https://arxiv.org/abs/2601.01213v1 Remote sensing solutions for avalanche segmentation and mapping are key to supporting risk forecasting and mitigation in mountain regions. Synthetic Aperture Radar (SAR) imagery from Sentinel-1 can be effectively used for this task, but training an effective detection model requires gathering a large dataset with high-quality annotations from domain experts, which is prohibitively time-consuming. In this work, we aim to facilitate and accelerate the annotation of SAR images for avalanche mapping. We build on the Segment Anything Model (SAM), a segmentation foundation model trained on natural images, and tailor it to Sentinel-1 SAR data. Adapting SAM to our use-case requires addressing several domain-specific challenges: (i) domain mismatch, since SAM was not trained on satellite/SAR imagery; (ii) input adaptation, because SAR products typically provide more than three channels, while SAM is constrained to RGB images; (iii) robustness to imprecise prompts that can affect target identification and degrade the segmentation quality, an issue exacerbated in small, low-contrast avalanches; and (iv) training efficiency, since standard fine-tuning is computationally demanding for SAM. We tackle these challenges through a combination of adapters to mitigate the domain gap, multiple encoders to handle multi-channel SAR inputs, prompt-engineering strategies to improve avalanche localization accuracy, and a training algorithm that limits the training time of the encoder, which is recognized as the major bottleneck. We integrate the resulting model into an annotation tool and show experimentally that it speeds up the annotation of SAR images. --- ### Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission Authors: Kazuhiko Murasaki, Shunsuke Konagai, Masakatsu Aoki, Taiga Yoshida, Ryuichi Tanida Published: 2026-01-03 Categories: cs.CV, cs.RO arXiv: https://arxiv.org/abs/2601.01210v2 To realize low-latency spatial transmission system for immersive telepresence, there are two major problems: capturing dynamic 3D scene densely and processing them in real time. LiDAR sensors capture 3D in real time, but produce sparce point clouds. Therefore, this paper presents a high-speed LiDAR point cloud densification method to generate dense 3D scene with minimal latency, addressing the need for on-the-fly depth completion while maintaining real-time performance. Our approach combines multiple LiDAR inputs with high-resolution color images and applies a joint bilateral filtering strategy implemented through a convolutional neural network architecture. Experiments demonstrate that the proposed method produces dense depth maps at full HD resolution in real time (30 fps), which is over 15x faster than a recent training-based depth completion approach. The resulting dense point clouds exhibit accurate geometry without multiview inconsistencies or ghosting artifacts. --- ### Persistent Tri-State Message Passing Authors: Yoonhyuk Choi, Jiho Choi, Chong-Kwon Kim Published: 2026-01-03 Categories: cs.LG, stat.ML arXiv: https://arxiv.org/abs/2601.01207v2 In stochastic message passing, an edge's sampled role changes the node states used to compute adaptive weights at later layers. Weight averaging therefore depends on whether edge roles persist across layers or are resampled at each layer. We study this dependence in Persistent Tri-State Message Passing (P3MP), which combines persistent additive, subtractive, and inactive roles with weights computed from each sample's endpoint states. For two layers, we separate target-state feedback from weight-source covariance and derive the condition under which local and shared weights reverse their preactivation ordering. Shared weights retain a $1/K$ fraction of the feedback for $K$ samples. The interaction is zero for a source-only scorer. Permuting complete weight vectors between samples quantifies the same role-state dependence while preserving their empirical distribution. Exact enumeration and feature-only checkpoint measurements agree with the analytical predictions. Across independently trained feature-only models, local weights yield higher accuracy and lower negative log-likelihood under persistence, while shared weights yield higher accuracy and lower loss under layerwise resampling. The same accuracy ordering holds on deduplicated Chameleon and Squirrel. --- ### MentalGame: Predicting Personality-Job Fitness for Software Developers Using Multi-Genre Games and Machine Learning Approaches Authors: Soroush Elyasi, Arya VarastehNezhad, Fattaneh Taghiyareh Published: 2026-01-03 Categories: cs.LG, cs.AI, cs.HC, cs.SE arXiv: https://arxiv.org/abs/2601.01206v1 Personality assessment in career guidance and personnel selection traditionally relies on self-report questionnaires, which are susceptible to response bias, fatigue, and intentional distortion. Game-based assessment offers a promising alternative by capturing implicit behavioral signals during gameplay. This study proposes a multi-genre serious-game framework combined with machine-learning techniques to predict suitability for software development roles. Developer-relevant personality and behavioral traits were identified through a systematic literature review and an empirical study of professional software engineers. A custom mobile game was designed to elicit behaviors related to problem solving, planning, adaptability, persistence, time management, and information seeking. Fine-grained gameplay event data were collected and analyzed using a two-phase modeling strategy where suitability was predicted exclusively from gameplay-derived behavioral features. Results show that our model achieved up to 97% precision and 94% accuracy. Behavioral analysis revealed that proper candidates exhibited distinct gameplay patterns, such as more wins in puzzle-based games, more side challenges, navigating menus more frequently, and exhibiting fewer pauses, retries, and surrender actions. These findings demonstrate that implicit behavioral traces captured during gameplay is promising in predicting software-development suitability without explicit personality testing, supporting serious games as a scalable, engaging, and less biased alternative for career assessment. --- ### XStreamVGGT: Extremely Memory-Efficient Streaming Vision Geometry Grounded Transformer with KV Cache Compression Authors: Zunhai Su, Weihao Ye, Hansen Feng, Keyu Fan, Jing Zhang, Dahai Yu, Zhengwu Liu, Ngai Wong Published: 2026-01-03 Categories: cs.CV arXiv: https://arxiv.org/abs/2601.01204v1 Learning-based 3D visual geometry models have benefited substantially from large-scale transformers. Among these, StreamVGGT leverages frame-wise causal attention for strong streaming reconstruction, but suffers from unbounded KV cache growth, leading to escalating memory consumption and inference latency as input frames accumulate. We propose XStreamVGGT, a tuning-free approach that systematically compresses the KV cache through joint pruning and quantization, enabling extremely memory-efficient streaming inference. Specifically, redundant KVs originating from multi-view inputs are pruned through efficient token importance identification, enabling a fixed memory budget. Leveraging the unique distribution of KV tensors, we incorporate KV quantization to further reduce memory consumption. Extensive evaluations show that XStreamVGGT achieves mostly negligible performance degradation while substantially reducing memory usage by 4.42$\times$ and accelerating inference by 5.48$\times$, enabling scalable and practical streaming 3D applications. The code is available at https://github.com/ywh187/XStreamVGGT/. --- ### RefSR-Adv: Adversarial Attack on Reference-based Image Super-Resolution Models Authors: Jiazhu Dai, Huihui Jiang Published: 2026-01-03 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2601.01202v1 Single Image Super-Resolution (SISR) aims to recover high-resolution images from low-resolution inputs. Unlike SISR, Reference-based Super-Resolution (RefSR) leverages an additional high-resolution reference image to facilitate the recovery of high-frequency textures. However, existing research mainly focuses on backdoor attacks targeting RefSR, while the vulnerability of the adversarial attacks targeting RefSR has not been fully explored. To fill this research gap, we propose RefSR-Adv, an adversarial attack that degrades SR outputs by perturbing only the reference image. By maximizing the difference between adversarial and clean outputs, RefSR-Adv induces significant performance degradation and generates severe artifacts across CNN, Transformer, and Mamba architectures on the CUFED5, WR-SR, and DRefSR datasets. Importantly, experiments confirm a positive correlation between the similarity of the low-resolution input and the reference image and attack effectiveness, revealing that the model's over-reliance on reference features is a key security flaw. This study reveals a security vulnerability in RefSR systems, aiming to urge researchers to pay attention to the robustness of RefSR. --- ### Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity Authors: Zhang Chen, Shuai Wan, Yuezhe Zhang, Siyu Ren, Fuzheng Yang, Junhui Hou Published: 2026-01-03 Categories: cs.CV, eess.IV arXiv: https://arxiv.org/abs/2601.01200v3 The unstructured and irregular nature of points poses a significant challenge for accurate point cloud quality assessment (PCQA), particularly in establishing accurate perceptual feature correspondence. To tackle this, we propose the Multi-scale Implicit Structural Similarity Measurement (MS-ISSM). Unlike traditional point-to-point matching, MS-ISSM utilizes radial basis function (RBF) to represent local features continuously, transforming distortion measurement into a comparison of implicit function coefficients. This approach effectively circumvents matching errors inherent in irregular data. Additionally, we propose a ResGrouped-MLP quality assessment network, which robustly maps multi-scale feature differences to perceptual scores. The network architecture departs from traditional flat multi-layer perceptron (MLP) by adopting a grouped encoding strategy integrated with residual blocks and channel-wise attention mechanisms. This hierarchical design allows the model to preserve the distinct physical semantics of luma, chroma, and geometry while adaptively focusing on the most salient distortion features across High, Medium, and Low scales. Experimental results on multiple benchmarks demonstrate that MS-ISSM outperforms state-of-the-art metrics in both reliability and generalization. The source code is available at: https://github.com/ZhangChen2022/MS-ISSM. --- ### Reinforcement Learning Enhanced Multi-hop Reasoning for Temporal Knowledge Question Answering Authors: Wuzhenghong Wen, Chao Xue, Su Pan, Yuwei Sun, Minlong Peng Published: 2026-01-03 Categories: cs.AI arXiv: https://arxiv.org/abs/2601.01195v1 Temporal knowledge graph question answering (TKGQA) involves multi-hop reasoning over temporally constrained entity relationships in the knowledge graph to answer a given question. However, at each hop, large language models (LLMs) retrieve subgraphs with numerous temporally similar and semantically complex relations, increasing the risk of suboptimal decisions and error propagation. To address these challenges, we propose the multi-hop reasoning enhanced (MRE) framework, which enhances both forward and backward reasoning to improve the identification of globally optimal reasoning trajectories. Specifically, MRE begins with prompt engineering to guide the LLM in generating diverse reasoning trajectories for a given question. Valid reasoning trajectories are then selected for supervised fine-tuning, serving as a cold-start strategy. Finally, we introduce Tree-Group Relative Policy Optimization (T-GRPO), a recursive, tree-structured learning-by-exploration approach. At each hop, exploration establishes strong causal dependencies on the previous hop, while evaluation is informed by multi-path exploration feedback from subsequent hops. Experimental results on two TKGQA benchmarks indicate that the proposed MRE-based model consistently surpasses state-of-the-art (SOTA) approaches in handling complex multi-hop queries. Further analysis highlights improved interpretability and robustness to noisy temporal annotations. --- ### Crowded Video Individual Counting Informed by Social Grouping and Spatial-Temporal Displacement Priors Authors: Hao Lu, Xuhui Zhu, Wenjing Zhang, Yanan Li, Xiang Bai Published: 2026-01-03 Categories: cs.CV arXiv: https://arxiv.org/abs/2601.01192v1 Video Individual Counting (VIC) is a recently introduced task aiming to estimate pedestrian flux from a video. It extends Video Crowd Counting (VCC) beyond the per-frame pedestrian count. In contrast to VCC that learns to count pedestrians across frames, VIC must identify co-existent pedestrians between frames, which turns out to be a correspondence problem. Existing VIC approaches, however, can underperform in congested scenes such as metro commuting. To address this, we build WuhanMetroCrowd, one of the first VIC datasets that characterize crowded, dynamic pedestrian flows. It features sparse-to-dense density levels, short-to-long video clips, slow-to-fast flow variations, front-to-back appearance changes, and light-to-heavy occlusions. To better adapt VIC approaches to crowds, we rethink the nature of VIC and recognize two informative priors: i) the social grouping prior that indicates pedestrians tend to gather in groups and ii) the spatial-temporal displacement prior that informs an individual cannot teleport physically. The former inspires us to relax the standard one-to-one (O2O) matching used by VIC to one-to-many (O2M) matching, implemented by an implicit context generator and a O2M matcher; the latter facilitates the design of a displacement prior injector, which strengthens not only O2M matching but also feature extraction and model training. These designs jointly form a novel and strong VIC baseline OMAN++. Extensive experiments show that OMAN++ not only outperforms state-of-the-art VIC baselines on the standard SenseCrowd, CroHD, and MovingDroneCrowd benchmarks, but also indicates a clear advantage in crowded scenes, with a 38.12% error reduction on our WuhanMetroCrowd dataset. Code, data, and pretrained models are available at https://github.com/tiny-smart/OMAN. --- ### DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network Authors: Zhiwei Huang, Yanwei Fu, Yi Zhou, Xieyuanli Chen, Qijun Chen, Rui Fan Published: 2026-01-03 Categories: cs.RO, cs.CV arXiv: https://arxiv.org/abs/2601.01188v1 LiDAR-camera extrinsic calibration is essential for multi-modal data fusion in robotic perception systems. However, existing approaches typically rely on handcrafted calibration targets (e.g., checkerboards) or specific, static scene types, limiting their adaptability and deployment in real-world autonomous and robotic applications. This article presents the first self-supervised LiDAR-camera extrinsic calibration network that operates in an online fashion and eliminates the need for specific calibration targets. We first identify a significant generalization degradation problem in prior methods, caused by the conventional single-sided data augmentation strategy. To overcome this limitation, we propose a novel double-sided data augmentation technique that generates multi-perspective camera views using estimated depth maps, thereby enhancing robustness and diversity during training. Built upon this augmentation strategy, we design a dual-path, self-supervised calibration framework that reduces the dependence on high-precision ground truth labels and supports fully adaptive online calibration. Furthermore, to improve cross-modal feature association, we replace the traditional dual-branch feature extraction design with a difference map construction process that explicitly correlates LiDAR and camera features. This not only enhances calibration accuracy but also reduces model complexity. Extensive experiments conducted on five public benchmark datasets, as well as our own recorded dataset, demonstrate that the proposed method significantly outperforms existing approaches in terms of generalizability. --- ### GenCAMO: Scene-Graph Contextual Decoupling for Environment-aware and Mask-free Camouflage Image-Dense Annotation Generation Authors: Chenglizhao Chen, Shaojiang Yuan, Xiaoxue Lu, Mengke Song, Jia Song, Zhenyu Wu, Wenfeng Song, Shuai Li Published: 2026-01-03 Categories: cs.CV arXiv: https://arxiv.org/abs/2601.01181v1 Conceal dense prediction (CDP), especially RGB-D camouflage object detection and open-vocabulary camouflage object segmentation, plays a crucial role in advancing the understanding and reasoning of complex camouflage scenes. However, high-quality and large-scale camouflage datasets with dense annotation remain scarce due to expensive data collection and labeling costs. To address this challenge, we explore leveraging generative models to synthesize realistic camouflage image-dense data for training CDP models with fine-grained representations, prior knowledge, and auxiliary reasoning. Concretely, our contributions are threefold: (i) we introduce GenCAMO-DB, a large-scale camouflage dataset with multi-modal annotations, including depth maps, scene graphs, attribute descriptions, and text prompts; (ii) we present GenCAMO, an environment-aware and mask-free generative framework that produces high-fidelity camouflage image-dense annotations; (iii) extensive experiments across multiple modalities demonstrate that GenCAMO significantly improves dense prediction performance on complex camouflage scenes by providing high-quality synthetic data. The code and datasets will be released after paper acceptance. --- ### CardioMOD-Net: A Modal Decomposition-Neural Network Framework for Diagnosis and Prognosis of HFpEF from Echocardiography Cine Loops Authors: Andrés Bell-Navas, Jesús Garicano-Mena, Antonella Ausiello, Soledad Le Clainche, María Villalba-Orero, Enrique Lara-Pezzi Published: 2026-01-03 Categories: cs.CV arXiv: https://arxiv.org/abs/2601.01176v2 Introduction: Heart failure with preserved ejection fraction (HFpEF) arises from diverse comorbidities and progresses through prolonged subclinical stages, making early diagnosis and prognosis difficult. Current echocardiography-based Artificial Intelligence (AI) models focus primarily on binary HFpEF detection in humans and do not provide comorbidity-specific phenotyping or temporal estimates of disease progression towards decompensation. We aimed to develop a unified AI framework, CardioMOD-Net, to perform multiclass diagnosis and continuous prediction of HFpEF onset directly from standard echocardiography cine loops in preclinical models. Methods: Mouse echocardiography videos from four groups were used: control (CTL), hyperglycaemic (HG), obesity (OB), and systemic arterial hypertension (SAH). Two-dimensional parasternal long-axis cine loops were decomposed using Higher Order Dynamic Mode Decomposition (HODMD) to extract temporal features for downstream analysis. A shared latent representation supported Vision Transformers, one for a classifier for diagnosis and another for a regression module for predicting the age at HFpEF onset. Results: Overall diagnostic accuracy across the four groups was 65%, with all classes exceeding 50% accuracy. Misclassifications primarily reflected early-stage overlap between OB or SAH and CTL. The prognostic module achieved a root-mean-square error of 21.72 weeks for time-to-HFpEF prediction, with OB and SAH showing the most accurate estimates. Predicted HFpEF onset closely matched true distributions in all groups. Discussion: This unified framework demonstrates that multiclass phenotyping and continuous HFpEF onset prediction can be obtained from a single cine loop, even under small-data conditions. The approach offers a foundation for integrating diagnostic and prognostic modelling in preclinical HFpEF research. --- ### Almost Clinical: Linguistic properties of synthetic electronic health records Authors: Serge Sharoff, John Baker, David Francis Hunt, Alan Simpson Published: 2026-01-03 Categories: cs.CL arXiv: https://arxiv.org/abs/2601.01171v2 This study evaluates the linguistic and clinical suitability of synthetic electronic health records in mental health. First, we describe the rationale and the methodology for creating the synthetic corpus. Second, we examine expressions of agency, modality, and information flow across four clinical genres (Assessments, Correspondence, Referrals and Care plans) with the aim to understand how LLMs grammatically construct medical authority and patient agency through linguistic choices. While LLMs produce coherent, terminology-appropriate texts that approximate clinical practice, systematic divergences remain, including registerial shifts, insufficient clinical specificity, and inaccuracies in medication use and diagnostic procedures. The results show both the potential and limitations of synthetic corpora for enabling large-scale linguistic research otherwise impossible with genuine patient records. --- ### Cross-Layer Attentive Feature Upsampling for Low-latency Semantic Segmentation Authors: Tianheng Cheng, Xinggang Wang, Junchao Liao, Wenyu Liu Published: 2026-01-03 Categories: cs.CV arXiv: https://arxiv.org/abs/2601.01167v1 Semantic segmentation is a fundamental problem in computer vision and it requires high-resolution feature maps for dense prediction. Current coordinate-guided low-resolution feature interpolation methods, e.g., bilinear interpolation, produce coarse high-resolution features which suffer from feature misalignment and insufficient context information. Moreover, enriching semantics to high-resolution features requires a high computation burden, so that it is challenging to meet the requirement of lowlatency inference. We propose a novel Guided Attentive Interpolation (GAI) method to adaptively interpolate fine-grained high-resolution features with semantic features to tackle these issues. Guided Attentive Interpolation determines both spatial and semantic relations of pixels from features of different resolutions and then leverages these relations to interpolate high-resolution features with rich semantics. GAI can be integrated with any deep convolutional network for efficient semantic segmentation. In experiments, the GAI-based semantic segmentation networks, i.e., GAIN, can achieve78.8 mIoU with 22.3 FPS on Cityscapes and 80.6 mIoU with 64.5 on CamVid using an NVIDIA 1080Ti GPU, which are the new state-of-the-art results of low-latency semantic segmentation. Code and models are available at: https://github.com/hustvl/simpleseg. --- ### Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models Authors: Zihua Yang, Xin Liao, Yiqun Zhang, Yiu-ming Cheung Published: 2026-01-03 Categories: cs.LG, cs.AI, cs.CL arXiv: https://arxiv.org/abs/2601.01162v3 Qualitative data are widespread in domains such as healthcare, marketing, and bioinformatics, where clustering offers a fundamental tool for pattern discovery. A core difficulty of qualitative-data clustering lies in measuring similarity among attribute values that carry no inherent ordering or distance. To recover such relationships, existing studies typically rely on within-dataset co-occurrence statistics. This statistical route, however, becomes unreliable once the sample size is small, and the semantic context of each value is therefore left underexploited. Motivated by this limitation, this paper proposes BREVE (Balanced Representation via External Value Enrichment), a clustering framework that enriches each qualitative value with extra semantic dimensions drawn from an external knowledge base. That is, every unique value is expanded by a dense embedding that encodes its semantic content. To prevent the original value identity from being diluted by the added dimensions, a lightweight one-hot component is further appended. An adaptive weight, guided by cluster compactness, then determines how strongly the enrichment dimensions enter the final representation. With this design, experiments on eight benchmark datasets yield an average ARI rank of 1.3 against seven representative competitors. --- ### Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity Authors: Boris Prokhorov, Semyon Chebykin, Alexander Gasnikov, Aleksandr Beznosikov Published: 2026-01-03 Categories: math.OC, cs.LG arXiv: https://arxiv.org/abs/2601.01160v1 This paper deals with stochastic optimization problems involving Markovian noise with a zero-order oracle. We present and analyze a novel derivative-free method for solving such problems in strongly convex smooth and non-smooth settings with both one-point and two-point feedback oracles. Using a randomized batching scheme, we show that when mixing time $τ$ of the underlying noise sequence is less than the dimension of the problem $d$, the convergence estimates of our method do not depend on $τ$. This observation provides an efficient way to interact with Markovian stochasticity: instead of invoking the expensive first-order oracle, one should use the zero-order oracle. Finally, we complement our upper bounds with the corresponding lower bounds. This confirms the optimality of our results. --- ### DHI: Leveraging Diverse Hallucination Induction for Enhanced Contrastive Factuality Control in Large Language Models Authors: Jiani Guo, Xiangke Zeng, Jie Wu, Zuchao Li Published: 2026-01-03 Categories: cs.CL arXiv: https://arxiv.org/abs/2601.01156v1 Large language models (LLMs) frequently produce inaccurate or fabricated information, known as "hallucinations," which compromises their reliability. Existing approaches often train an "Evil LLM" to deliberately generate hallucinations on curated datasets, using these induced hallucinations to guide contrastive decoding against a reliable "positive model" for hallucination mitigation. However, this strategy is limited by the narrow diversity of hallucinations induced, as Evil LLMs trained on specific error types tend to reproduce only these particular patterns, thereby restricting their overall effectiveness. To address these limitations, we propose DHI (Diverse Hallucination Induction), a novel training framework that enables the Evil LLM to generate a broader range of hallucination types without relying on pre-annotated hallucination data. DHI employs a modified loss function that down-weights the generation of specific factually correct tokens, encouraging the Evil LLM to produce diverse hallucinations at targeted positions while maintaining overall factual content. Additionally, we introduce a causal attention masking adaptation to reduce the impact of this penalization on the generation of subsequent tokens. During inference, we apply an adaptive rationality constraint that restricts contrastive decoding to tokens where the positive model exhibits high confidence, thereby avoiding unnecessary penalties on factually correct tokens. Extensive empirical results show that DHI achieves significant performance gains over other contrastive decoding-based approaches across multiple hallucination benchmarks. --- ### SongSage: A Large Musical Language Model with Lyric Generative Pre-training Authors: Jiani Guo, Jiajia Li, Jie Wu, Zuchao Li, Yujiu Yang, Ping Wang Published: 2026-01-03 Categories: cs.CL arXiv: https://arxiv.org/abs/2601.01153v1 Large language models have achieved significant success in various domains, yet their understanding of lyric-centric knowledge has not been fully explored. In this work, we first introduce PlaylistSense, a dataset to evaluate the playlist understanding capability of language models. PlaylistSense encompasses ten types of user queries derived from common real-world perspectives, challenging LLMs to accurately grasp playlist features and address diverse user intents. Comprehensive evaluations indicate that current general-purpose LLMs still have potential for improvement in playlist understanding. Inspired by this, we introduce SongSage, a large musical language model equipped with diverse lyric-centric intelligence through lyric generative pretraining. SongSage undergoes continual pretraining on LyricBank, a carefully curated corpus of 5.48 billion tokens focused on lyrical content, followed by fine-tuning with LyricBank-SFT, a meticulously crafted instruction set comprising 775k samples across nine core lyric-centric tasks. Experimental results demonstrate that SongSage exhibits a strong understanding of lyric-centric knowledge, excels in rewriting user queries for zero-shot playlist recommendations, generates and continues lyrics effectively, and performs proficiently across seven additional capabilities. Beyond its lyric-centric expertise, SongSage also retains general knowledge comprehension and achieves a competitive MMLU score. We will keep the datasets inaccessible due to copyright restrictions and release the SongSage and training script to ensure reproducibility and support music AI research and applications, the datasets release plan details are provided in the appendix. --- ### Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification Authors: Wenbin Pei, Ruohao Dai, Bing Xue, Mengjie Zhang, Qiang Zhang, Yiu-Ming Cheung Published: 2026-01-03 Categories: cs.LG, cs.NE arXiv: https://arxiv.org/abs/2601.01150v1 Time series classification is a fundamental machine learning task with broad real-world applications. Although many deep learning methods have proven effective in learning time-series data for classification, they were originally developed under the assumption of balanced data distributions. Once data distribution is uneven, these methods tend to ignore the minority class that is typically of higher practical significance. Oversampling methods have been designed to address this by generating minority-class samples, but their reliance on linear interpolation often hampers the preservation of temporal dynamics and the generation of diverse samples. Therefore, in this paper, we propose Evo-TFS, a novel evolutionary oversampling method that integrates both time- and frequency-domain characteristics. In Evo-TFS, strongly typed genetic programming is employed to evolve diverse, high-quality time series, guided by a fitness function that incorporates both time-domain and frequency-domain characteristics. Experiments conducted on imbalanced time series datasets demonstrate that Evo-TFS outperforms existing oversampling methods, significantly enhancing the performance of time-domain and frequency-domain classifiers. --- ### Conformal Blindness: A Note on $A$-Cryptic change-points Authors: Johan Hallberg Szabadváry Published: 2026-01-03 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2601.01147v2 Conformal Test Martingales (CTMs) are a standard method within the Conformal Prediction framework for testing the crucial assumption of data exchangeability by monitoring deviations from uniformity in the p-value sequence. Although exchangeability implies uniform p-values, the converse does not hold. This raises the question of whether a significant break in exchangeability can occur, such that the p-values remain uniform, rendering CTMs blind. We answer this affirmatively, demonstrating the phenomenon of \emph{conformal blindness}. Through explicit construction, for the theoretically ideal ``predictive oracle'' conformity measure (given by the true conditional density), we demonstrate the possibility of an \emph{$A$-cryptic change-point} (where $A$ refers to the conformity measure). Using bivariate Gaussian distributions, we identify a line along which a change in the marginal means does not alter the distribution of the conformity scores, thereby producing perfectly uniform p-values. Simulations confirm that even a massive distribution shift can be perfectly cryptic to the CTM, highlighting a fundamental limitation and emphasising the critical role of the alignment of the conformity measure with potential shifts. By contrasting the predictive oracle with recent results on detection-optimal scores, we emphasise that validity monitoring in safety-critical systems requires careful separation of predictive and diagnostic goals. --- ### Self-Training the Neurochaos Learning Algorithm Authors: Anusree M, Akhila Henry, Pramod P Nair Published: 2026-01-03 Categories: cs.LG arXiv: https://arxiv.org/abs/2601.01146v1 In numerous practical applications, acquiring substantial quantities of labelled data is challenging and expensive, but unlabelled data is readily accessible. Conventional supervised learning methods frequently underperform in scenarios characterised by little labelled data or imbalanced datasets. This study introduces a hybrid semi-supervised learning (SSL) architecture that integrates Neurochaos Learning (NL) with a threshold-based Self-Training (ST) method to overcome this constraint. The NL architecture converts input characteristics into chaos-based ring-rate representations that encapsulate nonlinear relationships within the data, whereas ST progressively enlarges the labelled set utilising high-confidence pseudo-labelled samples. The model's performance is assessed using ten benchmark datasets and five machine learning classifiers, with 85% of the training data considered unlabelled and just 15% utilised as labelled data. The proposed Self-Training Neurochaos Learning (NL+ST) architecture consistently attains superior performance gain relative to standalone ST models, especially on limited, nonlinear and imbalanced datasets like Iris (188.66%), Wine (158.58%) and Glass Identification (110.48%). The results indicate that using chaos-based feature extraction with SSL improves generalisation, resilience, and classification accuracy in low-data contexts. --- ### KOS-TL (Knowledge Operation System Type Logic) Authors: Peng Chen Published: 2026-01-03 Categories: cs.CL, cs.LO arXiv: https://arxiv.org/abs/2601.01143v1 This paper introduces KOS-TL (Knowledge Operation System Type Logic), a novel constructive framework designed to provide a rigorous logical foundation for autonomous and executable knowledge systems. Traditional knowledge representation models often suffer from a gap between static symbolic logic and dynamic system execution. To bridge this divide, KOS-TL leverages Dependent Type Theory to unify data, logic, and proof into a singular computational substrate.The architecture of KOS-TL is organized into three hierarchical layers: the Core Layer, which defines the static type universe and constructive primitives; the Kernel Layer, which governs state evolution through an event-driven mechanism characterized by the triple $\langle Σ, \textsf{Ev}, Δ\rangle$; and the Runtime Layer, responsible for the bidirectional refinement of physical signals into logical evidence. We formally define the operational semantics of the system and prove key meta-theoretical properties, including Progress and Evolutionary Consistency, ensuring that the system remains logically self-consistent and free from stuck states during continuous state transitions.By integrating Davidsonian event semantics with Martin-Löf type theory, KOS-TL enables the construction of "proof-carrying knowledge," where every state change in the knowledge base is accompanied by a formal witness of its validity. We demonstrate the practical utility of this logic through application examples in industrial traceability and cross-border financial compliance. Our results suggest that KOS-TL provides a robust, formally verifiable basis for the next generation of intelligent, autonomous operating systems. --- ### YODA: Yet Another One-step Diffusion-based Video Compressor Authors: Xingchen Li, Junzhe Zhang, Junqi Shi, Ming Lu, Zhan Ma Published: 2026-01-03 Categories: eess.IV, cs.CV arXiv: https://arxiv.org/abs/2601.01141v1 While one-step diffusion models have recently excelled in perceptual image compression, their application to video remains limited. Prior efforts typically rely on pretrained 2D autoencoders that generate per-frame latent representations independently, thereby neglecting temporal dependencies. We present YODA--Yet Another One-step Diffusion-based Video Compressor--which embeds multiscale features from temporal references for both latent generation and latent coding to better exploit spatial-temporal correlations for more compact representation, and employs a linear Diffusion Transformer (DiT) for efficient one-step denoising. YODA achieves state-of-the-art perceptual performance, consistently outperforming traditional and deep-learning baselines on LPIPS, DISTS, FID, and KID. Source code will be publicly available at https://github.com/NJUVISION/YODA. --- ### AI-Powered Hybrid Intrusion Detection Framework for Cloud Security Using Novel Metaheuristic Optimization Authors: Maryam Mahdi Alhusseini, Alireza Rouhi, Mohammad-Reza Feizi-Derakhshi Published: 2026-01-03 Categories: cs.CR, cs.AI arXiv: https://arxiv.org/abs/2601.01134v1 Cybersecurity poses considerable problems to Cloud Computing (CC), especially regarding Intrusion Detection Systems (IDSs), facing difficulties with skewed datasets and suboptimal classification model performance. This study presents the Hybrid Intrusion Detection System (HyIDS), an innovative IDS that employs the Energy Valley Optimizer (EVO) for Feature Selection (FS). Additionally, it introduces a novel technique for enhancing the cybersecurity of cloud computing through the integration of machine learning methodologies with the EVO Algorithm. The Energy Valley Optimizer (EVO) effectively diminished features in the CIC-DDoS2019 dataset from 88 to 38 and in the CSE-CIC-IDS2018 data from 80 to 43, significantly enhancing computing efficiency. HyIDS incorporates four Machine Learning (ML) models: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (D_Tree), and K-Nearest Neighbors (KNN). The proposed HyIDS was assessed utilizing two real-world intrusion datasets, CIC-DDoS2019 and CSE-CIC-IDS2018, both distinguished by considerable class imbalances. The CIC-DDoS2019 dataset has a significant imbalance between DDoS assault samples and legal traffic, while the CSE-CIC-IDS2018 dataset primarily comprises benign traffic with insufficient representation of attack types, complicating the detection of minority attacks. A downsampling technique was employed to balance the datasets, hence improving detection efficacy for both benign and malicious traffic. Twenty-four trials were done, revealing substantial enhancements in categorization accuracy, precision, and recall. Our suggested D_TreeEVO model attained an accuracy rate of 99.13% and an F1 score of 98.94% on the CIC-DDoS2019 dataset, and an accuracy rate of 99.78% and an F1 score of 99.70% on the CSE-CIC-IDS2018 data. These data demonstrate that EVO significantly improves cybersecurity in Cloud Computing (CC). --- ### Generating Diverse TSP Tours via a Combination of Graph Pointer Network and Dispersion Authors: Hao-Tsung Yang, Ssu-Yuan Lo, Kuan-Lun Chen, Ching-Kai Wang Published: 2026-01-03 Categories: cs.CG, cs.AI, cs.LG arXiv: https://arxiv.org/abs/2601.01132v2 We address the Diverse Traveling Salesman Problem (D-TSP), a bi-criteria optimization challenge that seeks a set of $k$ distinct TSP tours. The objective requires every selected tour to have a length at most $c|T^*|$ (where $|T^*|$ is the optimal tour length) while minimizing the average Jaccard similarity across all tour pairs. This formulation is crucial for applications requiring both high solution quality and fault tolerance, such as logistics planning, robotics pathfinding or strategic patrolling. Current methods are limited: traditional heuristics, such as the Niching Memetic Algorithm (NMA) or bi-criteria optimization, incur high computational complexity $O(n^3)$, while modern neural approaches (e.g., RF-MA3S) achieve limited diversity quality and rely on complex, external mechanisms. To overcome these limitations, we propose a novel hybrid framework that decomposes D-TSP into two efficient steps. First, we utilize a simple Graph Pointer Network (GPN), augmented with an approximated sequence entropy loss, to efficiently sample a large, diverse pool of high-quality tours. This simple modification effectively controls the quality-diversity trade-off without complex external mechanisms. Second, we apply a greedy algorithm that yields a 2-approximation for the dispersion problem to select the final $k$ maximally diverse tours from the generated pool. Our results demonstrate state-of-the-art performance. On the Berlin instance, our model achieves an average Jaccard index of $0.015$, significantly outperforming NMA ($0.081$) and RF-MA3S. By leveraging GPU acceleration, our GPN structure achieves a near-linear empirical runtime growth of $O(n)$. While maintaining solution diversity comparable to complex bi-criteria algorithms, our approach is over 360 times faster on large-scale instances (783 cities), delivering high-quality TSP solutions with unprecedented efficiency and simplicity. --- ### RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian Authors: Kla Tantithamthavorn, Yaotian Zou, Andy Wong, Michael Gupta, Zhe Wang, Mike Buller, Ryan Jiang, Matthew Watson, Minwoo Jeong, Kun Chen, Ming Wu Published: 2026-01-03 Categories: cs.SE, cs.AI, cs.CL, cs.LG arXiv: https://arxiv.org/abs/2601.01129v2 Large Language Models (LLMs)-powered code review automation has the potential to transform code review workflows. Despite the advances of LLM-powered code review comment generation approaches, several practical challenges remain for designing enterprise-grade code review automation tools. In particular, this paper aims at answering the practical question: how can we design a review-guided, context-aware, quality-checked code review comment generation without fine-tuning? In this paper, we present RovoDev Code Reviewer, an enterprise-grade LLM-based code review automation tool designed and deployed at scale within Atlassian's development ecosystem with seamless integration into Atlassian's Bitbucket. Through the offline, online, user feedback evaluations over a one-year period, we conclude that RovoDev Code Reviewer is effective in generating code review comments that could lead to code resolution for 38.70% (i.e., comments that triggered code changes in the subsequent commits); and offers the promise of accelerating feedback cycles (i.e., decreasing the PR cycle time by 30.8%), alleviating reviewer workload (i.e., reducing the number of human-written comments by 35.6%), and improving overall software quality (i.e., finding errors with actionable suggestions). --- ### Wittgenstein's Family Resemblance Clustering Algorithm Authors: Golbahar Amanpour, Benyamin Ghojogh Published: 2026-01-03 Categories: cs.LG, cs.AI, stat.ML arXiv: https://arxiv.org/abs/2601.01127v2 This paper, introducing a novel method in philomatics, draws on Wittgenstein's concept of family resemblance from analytic philosophy to develop a clustering algorithm for machine learning. According to Wittgenstein's Philosophical Investigations (1953), family resemblance holds that members of a concept or category are connected by overlapping similarities rather than a single defining property. Consequently, a family of entities forms a chain of items sharing overlapping traits. This philosophical idea naturally lends itself to a graph-based approach in machine learning. Accordingly, we propose the Wittgenstein's Family Resemblance (WFR) clustering algorithm and its kernel variant, kernel WFR. This algorithm computes resemblance scores between neighboring data instances, and after thresholding these scores, a resemblance graph is constructed. The connected components of this graph define the resulting clusters. Simulations on benchmark datasets demonstrate that WFR is an effective nonlinear clustering algorithm that does not require prior knowledge of the number of clusters or assumptions about their shapes. --- ### RoboPhD: Self-Improving Text-to-SQL Through Autonomous Agent Evolution Authors: Andrew Borthwick, Stephen Ash Published: 2026-01-03 Categories: cs.CL arXiv: https://arxiv.org/abs/2601.01126v2 We present RoboPhD, a system where AI agents autonomously conduct research to improve Text-to-SQL performance. RoboPhD implements a closed-loop evolution cycle with two coordinated components: a SQL Generation agent composed of a database analysis script and SQL generation instructions, and an Evolution agent that designs new versions based on performance feedback. Central to the framework is an ELO-based selection mechanism enabling survival-of-the-fittest dynamics while handling non-transitivity in performance. Starting from a naive 70-line baseline, RoboPhD evolves agents through iterative cross-pollination, discovering effective techniques without any external guidance on the Text-to-SQL domain. Our best agent, evolved to 1500 lines over 18 iterations, autonomously discovered strategies such as size-adaptive database analysis that adjusts depth based on schema complexity and SQL generation patterns for column selection, evidence interpretation, and aggregation. Evolution provides the largest gains on cheaper models: while we improve by 2.3 points over a strong Claude Opus 4.5 naive baseline, we show an improvement of 8.9 points over the weaker Claude Haiku model. This enables 'skip a tier' deployment: evolved Haiku exceeds naive Sonnet accuracy, and evolved Sonnet exceeds naive Opus, both at lower cost. The full system achieves 73.67% accuracy on the BIRD test set, demonstrating that AI can autonomously build a strong agentic system with only a trivial human-provided starting point. --- ### Learning from Historical Activations in Graph Neural Networks Authors: Yaniv Galron, Hadar Sinai, Haggai Maron, Moshe Eliasof Published: 2026-01-03 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2601.01123v2 Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains such as social networks, molecular chemistry, and more. A crucial component of GNNs is the pooling procedure, in which the node features calculated by the model are combined to form an informative final descriptor to be used for the downstream task. However, previous graph pooling schemes rely on the last GNN layer features as an input to the pooling or classifier layers, potentially under-utilizing important activations of previous layers produced during the forward pass of the model, which we regard as historical graph activations. This gap is particularly pronounced in cases where a node's representation can shift significantly over the course of many graph neural layers, and worsened by graph-specific challenges such as over-smoothing in deep architectures. To bridge this gap, we introduce HISTOGRAPH, a novel two-stage attention-based final aggregation layer that first applies a unified layer-wise attention over intermediate activations, followed by node-wise attention. By modeling the evolution of node representations across layers, our HISTOGRAPH leverages both the activation history of nodes and the graph structure to refine features used for final prediction. Empirical results on multiple graph classification benchmarks demonstrate that HISTOGRAPH offers strong performance that consistently improves traditional techniques, with particularly strong robustness in deep GNNs. --- ### Listen, Attend, Understand: a Regularization Technique for Stable E2E Speech Translation Training on High Variance labels Authors: Yacouba Diarra, Michael Leventhal Published: 2026-01-03 Categories: cs.CL arXiv: https://arxiv.org/abs/2601.01121v1 End-to-End Speech Translation often shows slower convergence and worse performance when target transcriptions exhibit high variance and semantic ambiguity. We propose Listen, Attend, Understand (LAU), a semantic regularization technique that constrains the acoustic encoder's latent space during training. By leveraging frozen text embeddings to provide a directional auxiliary loss, LAU injects linguistic groundedness into the acoustic representation without increasing inference cost. We evaluate our method on a Bambara-to-French dataset with 30 hours of Bambara speech translated by non-professionals. Experimental results demonstrate that LAU models achieve comparable performance by standard metrics compared to an E2E-ST system pretrained with 100\% more data and while performing better in preserving semantic meaning. Furthermore, we introduce Total Parameter Drift as a metric to quantify the structural impact of regularization to demonstrate that semantic constraints actively reorganize the encoder's weights to prioritize meaning over literal phonetics. Our findings suggest that LAU is a robust alternative to post-hoc rescoring and a valuable addition to E2E-ST training, especially when training data is scarce and/or noisy. --- ### Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings Authors: Muhammad Ashad Kabir, Sirajam Munira, Dewan Tasnia Azad, Saleh Mohammed Ikram, Mohammad Habibur Rahman Sarker, Syed Manzoor Ahmed Hanifi Published: 2026-01-03 Categories: cs.LG arXiv: https://arxiv.org/abs/2601.01119v2 Early detection of chronic kidney disease (CKD) is essential for preventing progression to end-stage renal disease. However, existing screening tools - primarily developed using populations from high-income countries - often underperform in Bangladesh and South Asia, where risk profiles differ. Most of these tools rely on simple additive scoring functions and are based on data from patients with advanced-stage CKD. Consequently, they fail to capture complex interactions among risk factors and are limited in predicting early-stage CKD. Our objective was to develop and evaluate an explainable machine learning (ML) framework for community-based early-stage CKD screening for low-resource settings, tailored to the Bangladeshi and South Asian population context. A community-based CKD dataset from Bangladesh was used to develop predictive models. Variables were organized into clinically meaningful feature groups, and ten complementary feature selection methods were applied to identify robust predictor subsets. Twelve ML classifiers were evaluated using nested cross-validation. Model performance was benchmarked against established CKD screening tools and externally validated on three independent datasets from India, the UAE, and Bangladesh. SHAP was used to interpret model predictions. An ML model trained on an RFECV-selected feature subset achieved a balanced accuracy of 90.40%, whereas minimal non-pathology-test features demonstrated excellent predictive capability with a balanced accuracy of 89.23%, often outperforming larger or full feature sets. Compared with existing screening tools, the proposed models achieved substantially higher accuracy and sensitivity while requiring fewer and more accessible inputs. External validation confirmed strong generalizability with 78% to 98% sensitivity. --- ### ScienceDB AI: An LLM-Driven Agentic Recommender System for Large-Scale Scientific Data Sharing Services Authors: Qingqing Long, Haotian Chen, Chenyang Zhao, Xiaolei Du, Xuezhi Wang, Pengyao Wang, Chengzan Li, Yuanchun Zhou, Hengshu Zhu Published: 2026-01-03 Categories: cs.IR, cs.AI, cs.DL arXiv: https://arxiv.org/abs/2601.01118v1 The rapid growth of AI for Science (AI4S) has underscored the significance of scientific datasets, leading to the establishment of numerous national scientific data centers and sharing platforms. Despite this progress, efficiently promoting dataset sharing and utilization for scientific research remains challenging. Scientific datasets contain intricate domain-specific knowledge and contexts, rendering traditional collaborative filtering-based recommenders inadequate. Recent advances in Large Language Models (LLMs) offer unprecedented opportunities to build conversational agents capable of deep semantic understanding and personalized recommendations. In response, we present ScienceDB AI, a novel LLM-driven agentic recommender system developed on Science Data Bank (ScienceDB), one of the largest global scientific data-sharing platforms. ScienceDB AI leverages natural language conversations and deep reasoning to accurately recommend datasets aligned with researchers' scientific intents and evolving requirements. The system introduces several innovations: a Scientific Intention Perceptor to extract structured experimental elements from complicated queries, a Structured Memory Compressor to manage multi-turn dialogues effectively, and a Trustworthy Retrieval-Augmented Generation (Trustworthy RAG) framework. The Trustworthy RAG employs a two-stage retrieval mechanism and provides citable dataset references via Citable Scientific Task Record (CSTR) identifiers, enhancing recommendation trustworthiness and reproducibility. Through extensive offline and online experiments using over 10 million real-world datasets, ScienceDB AI has demonstrated significant effectiveness. To our knowledge, ScienceDB AI is the first LLM-driven conversational recommender tailored explicitly for large-scale scientific dataset sharing services. The platform is publicly accessible at: https://ai.scidb.cn/en. --- ### EmoLoom-2B: Fast Base-Model Screening for Emotion Classification and VAD with Lexicon-Weak Supervision and KV-Off Evaluation Authors: Zilin Li, Weiwei Xu, Xuanbo Lu, Zheda Liu Published: 2026-01-03 Categories: cs.CL arXiv: https://arxiv.org/abs/2601.01112v2 We introduce EmoLoom-2B, a lightweight and reproducible pipeline that turns small language models under 2B parameters into fast screening candidates for joint emotion classification and Valence-Arousal-Dominance prediction. To ensure protocol-faithful and fair evaluation, we unify data loading, training, and inference under a single JSON input-output contract and remove avoidable variance by adopting KV-off decoding as the default setting. We incorporate two orthogonal semantic regularizers: a VAD-preserving constraint that aligns generated text with target VAD triples, and a lightweight external appraisal classifier that provides training-time guidance on goal attainment, controllability, certainty, and fairness without injecting long rationales. To improve polarity sensitivity, we introduce Valence Flip augmentation based on mirrored emotional pairs. During supervised fine-tuning, we apply A/B mixture sampling with entropy-aware temperature scheduling to balance coverage and convergence. Using Qwen-1.8B-Chat as the base model, EmoLoom-2B achieves strong performance on GoEmotions and EmpatheticDialogues, and demonstrates robust cross-corpus generalization on DailyDialog. The proposed recipe is budget-aware, auditable, and re-entrant, serving as a dependable screening pass before heavier training or multimodal fusion. --- ### Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization Authors: Abhinav Attri, Rajeev Ranjan Dwivedi, Samiran Das, Vinod Kumar Kurmi Published: 2026-01-03 Categories: cs.CV, eess.IV arXiv: https://arxiv.org/abs/2601.01103v1 We present HAQAGen, a unified generative model for resolution-invariant NIR-to-RGB colorization that balances chromatic realism with structural fidelity. The proposed model introduces (i) a combined loss term aligning the global color statistics through differentiable histogram matching, perceptual image quality measure, and feature based similarity to preserve texture information, (ii) local hue-saturation priors injected via Spatially Adaptive Denormalization (SPADE) to stabilize chromatic reconstruction, and (iii) texture-aware supervision within a Mamba backbone to preserve fine details. We introduce an adaptive-resolution inference engine that further enables high-resolution translation without sacrificing quality. Our proposed NIR-to-RGB translation model simultaneously enforces global color statistics and local chromatic consistency, while scaling to native resolutions without compromising texture fidelity or generalization. Extensive evaluations on FANVID, OMSIV, VCIP2020, and RGB2NIR using different evaluation metrics demonstrate consistent improvements over state-of-the-art baseline methods. HAQAGen produces images with sharper textures, natural colors, attaining significant gains as per perceptual metrics. These results position HAQAGen as a scalable and effective solution for NIR-to-RGB translation across diverse imaging scenarios. Project Page: https://rajeev-dw9.github.io/HAQAGen/ --- ### Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks Authors: Mahmudul Hasan, Mabsur Fatin Bin Hossain Published: 2026-01-03 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2601.01099v1 This paper presents a comparative study of a custom convolutional neural network (CNN) architecture against widely used pretrained and transfer learning CNN models across five real-world image datasets. The datasets span binary classification, fine-grained multiclass recognition, and object detection scenarios. We analyze how architectural factors, such as network depth, residual connections, and feature extraction strategies, influence classification and localization performance. The results show that deeper CNN architectures provide substantial performance gains on fine-grained multiclass datasets, while lightweight pretrained and transfer learning models remain highly effective for simpler binary classification tasks. Additionally, we extend the proposed architecture to an object detection setting, demonstrating its adaptability in identifying unauthorized auto-rickshaws in real-world traffic scenes. Building upon a systematic analysis of custom CNN architectures alongside pretrained and transfer learning models, this study provides practical guidance for selecting suitable network designs based on task complexity and resource constraints. --- ### Neural Networks on Symmetric Spaces of Noncompact Type Authors: Xuan Son Nguyen, Shuo Yang, Aymeric Histace Published: 2026-01-03 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2601.01097v1 Recent works have demonstrated promising performances of neural networks on hyperbolic spaces and symmetric positive definite (SPD) manifolds. These spaces belong to a family of Riemannian manifolds referred to as symmetric spaces of noncompact type. In this paper, we propose a novel approach for developing neural networks on such spaces. Our approach relies on a unified formulation of the distance from a point to a hyperplane on the considered spaces. We show that some existing formulations of the point-to-hyperplane distance can be recovered by our approach under specific settings. Furthermore, we derive a closed-form expression for the point-to-hyperplane distance in higher-rank symmetric spaces of noncompact type equipped with G-invariant Riemannian metrics. The derived distance then serves as a tool to design fully-connected (FC) layers and an attention mechanism for neural networks on the considered spaces. Our approach is validated on challenging benchmarks for image classification, electroencephalogram (EEG) signal classification, image generation, and natural language inference. --- ### NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding Authors: Hyeonjeong Ha, Jinjin Ge, Bo Feng, Kaixin Ma, Gargi Chakraborty Published: 2026-01-03 Categories: cs.CV, cs.LG arXiv: https://arxiv.org/abs/2601.01095v4 Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand temporally unfolding narratives in videos remains underexplored. True narrative understanding requires grounding who is doing what, when, and where, maintaining coherent entity representations across dynamic visual and temporal contexts. We introduce NarrativeTrack, the first benchmark to evaluate narrative understanding in MLLMs through fine-grained entity-centric reasoning. Unlike existing benchmarks limited to short clips or coarse scene-level semantics, we decompose videos into constituent entities and examine their continuity via a Compositional Reasoning Progression (CRP), a structured evaluation framework that progressively increases narrative complexity across three dimensions: entity existence, entity changes, and entity ambiguity. CRP challenges models to advance from temporal persistence to contextual evolution and fine-grained perceptual reasoning. A fully automated entity-centric pipeline enables scalable extraction of temporally grounded entity representations, providing the foundation for CRP. Evaluations of state-of-the-art MLLMs reveal that models fail to robustly track entities across visual transitions and temporal dynamics, often hallucinating identity under context shifts. Open-source general-purpose MLLMs exhibit strong perceptual grounding but weak temporal coherence, while video-specific MLLMs capture temporal context yet hallucinate entities' contexts. These findings uncover a fundamental trade-off between perceptual grounding and temporal reasoning, indicating that narrative understanding emerges only from their integration. NarrativeTrack provides the first systematic framework to diagnose and advance temporally grounded narrative comprehension in MLLMs. --- ### SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking Authors: Yubo Shu, Peng Zhang, Meng Wu, Yan Chen, Haoxuan Zhou, Guanming Liu, Yu Zhang, Liuxin Zhang, Qianying Wang, Tun Lu, Ning Gu Published: 2026-01-03 Categories: cs.HC, cs.AI, cs.IR arXiv: https://arxiv.org/abs/2601.01094v1 Social cues, which convey others' presence, behaviors, or identities, play a crucial role in human information seeking by helping individuals judge relevance and trustworthiness. However, existing LLM-based search systems primarily rely on semantic features, creating a misalignment with the socialized cognition underlying natural information seeking. To address this gap, we explore how the integration of social cues into LLM-based search influences users' perceptions, experiences, and behaviors. Focusing on social media platforms that are beginning to adopt LLM-based search, we integrate design workshops, the implementation of the prototype system (SoulSeek), a between-subjects study, and mixed-method analyses to examine both outcome- and process-level findings. The workshop informs the prototype's cue-integrated design. The study shows that social cues improve perceived outcomes and experiences, promote reflective information behaviors, and reveal limits of current LLM-based search. We propose design implications emphasizing better social-knowledge understanding, personalized cue settings, and controllable interactions. --- ### ks-lit-3m: A 3.1 million word kashmiri text dataset for large language model pretraining Authors: Haq Nawaz Malik Published: 2026-01-03 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2601.01091v1 Large Language Models (LLMs) demonstrate remarkable fluency across high-resource languages yet consistently fail to generate coherent text in Kashmiri, a language spoken by approximately seven million people. This performance disparity stems not from inherent model limitations but from a critical scarcity of high-quality training data. Decades of Kashmiri literature remain inaccessible to modern NLP pipelines due to their encoding in the proprietary InPage desktop publishing format. This paper introduces KS-LIT-3M, a curated corpus of 3.1 million words (16.4 million characters) specifically designed for pretraining language models on Kashmiri. The dataset is structured as a single continuous linear text stream, optimized for causal language model training where models learn to predict subsequent tokens from preceding context. The corpus was constructed through the development of a specialized InPage-to-Unicode converter, followed by rigorous preprocessing including English contamination removal, character normalization, and quality validation. Encompassing 131,607 unique words drawn from diverse genres including literary works, journalistic writing, academic texts, and religious scholarship, KS-LIT-3M addresses a fundamental resource gap for Kashmiri language technology. The dataset is released under the CC-BY-4.0 license to facilitate research in Kashmiri natural language processing. --- ### Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai Authors: Erica Coppolillo, Luca Luceri, Emilio Ferrara Published: 2026-01-03 Categories: cs.MA, cs.AI, cs.CY arXiv: https://arxiv.org/abs/2601.01090v2 Large Language Models (LLMs) are increasingly embedded in autonomous agents that engage, converse, and co-evolve in online social platforms. While prior work has documented the generation of toxic content by LLMs, far less is known about how exposure to harmful content shapes agent behavior over time, particularly in environments composed entirely of interacting AI agents. In this work, we study toxicity adoption of LLM-driven agents on Chirper.ai, a fully AI-driven social platform. Specifically, we model interactions in terms of stimuli (posts) and responses (comments). We conduct a large-scale empirical analysis of agent behavior, examining how toxic responses relate to toxic stimuli, how repeated exposure to toxicity affects the likelihood of toxic responses, and whether toxic behavior can be predicted from exposure alone. Our findings show that toxic responses are more likely following toxic stimuli, and, at the same time, cumulative toxic exposure (repeated over time) significantly increases the probability of toxic responding. We further introduce two influence metrics, revealing a strong negative correlation between induced and spontaneous toxicity. Finally, we show that the number of toxic stimuli alone enables accurate prediction of whether an agent will eventually produce toxic content. These results highlight exposure as a critical risk factor in the deployment of LLM agents, particularly as such agents operate in online environments where they may engage not only with other AI chatbots, but also with human counterparts. This could trigger unwanted and pernicious phenomena, such as hate-speech propagation and cyberbullying. In an effort to reduce such risks, monitoring exposure to toxic content may provide a lightweight yet effective mechanism for auditing and mitigating harmful behavior in the wild. --- ### Central Dogma Transformer: Towards Mechanism-Oriented AI for Cellular Understanding Authors: Nobuyuki Ota Published: 2026-01-03 Categories: cs.LG, q-bio.GN arXiv: https://arxiv.org/abs/2601.01089v2 Understanding cellular mechanisms requires integrating information across DNA, RNA, and protein - the three molecular systems linked by the Central Dogma of molecular biology. While domain-specific foundation models have achieved success for each modality individually, they remain isolated, limiting our ability to model integrated cellular processes. Here we present the Central Dogma Transformer (CDT), an architecture that integrates pre-trained language models for DNA, RNA, and protein following the directional logic of the Central Dogma. CDT employs directional cross-attention mechanisms - DNA-to-RNA attention models transcriptional regulation, while RNA-to-Protein attention models translational relationships - producing a unified Virtual Cell Embedding that integrates all three modalities. We validate CDT v1 - a proof-of-concept implementation using fixed (non-cell-specific) RNA and protein embeddings - on CRISPRi enhancer perturbation data from K562 cells, achieving a Pearson correlation of 0.503, representing 63% of the theoretical ceiling set by cross-experiment variability (r = 0.797). Attention and gradient analyses provide complementary interpretive windows: in detailed case studies, these approaches highlight largely distinct genomic regions, with gradient analysis identifying a CTCF binding site that Hi-C data showed as physically contacting both enhancer and target gene. These results suggest that AI architectures aligned with biological information flow can achieve both predictive accuracy and mechanistic interpretability. --- ### 600k-ks-ocr: a large-scale synthetic dataset for optical character recognition in kashmiri script Authors: Haq Nawaz Malik Published: 2026-01-03 Categories: cs.CV, cs.CL arXiv: https://arxiv.org/abs/2601.01088v1 This technical report presents the 600K-KS-OCR Dataset, a large-scale synthetic corpus comprising approximately 602,000 word-level segmented images designed for training and evaluating optical character recognition systems targeting Kashmiri script. The dataset addresses a critical resource gap for Kashmiri, an endangered Dardic language utilizing a modified Perso-Arabic writing system spoken by approximately seven million people. Each image is rendered at 256x64 pixels with corresponding ground-truth transcriptions provided in multiple formats compatible with CRNN, TrOCR, and generalpurpose machine learning pipelines. The generation methodology incorporates three traditional Kashmiri typefaces, comprehensive data augmentation simulating real-world document degradation, and diverse background textures to enhance model robustness. The dataset is distributed across ten partitioned archives totaling approximately 10.6 GB and is released under the CC-BY-4.0 license to facilitate research in low-resource language optical character recognition. --- ### Luminark: Training-free, Probabilistically-Certified Watermarking for General Vision Generative Models Authors: Jiayi Xu, Zhang Zhang, Yuanrui Zhang, Ruitao Chen, Yixian Xu, Tianyu He, Di He Published: 2026-01-03 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2601.01085v1 In this paper, we introduce \emph{Luminark}, a training-free and probabilistically-certified watermarking method for general vision generative models. Our approach is built upon a novel watermark definition that leverages patch-level luminance statistics. Specifically, the service provider predefines a binary pattern together with corresponding patch-level thresholds. To detect a watermark in a given image, we evaluate whether the luminance of each patch surpasses its threshold and then verify whether the resulting binary pattern aligns with the target one. A simple statistical analysis demonstrates that the false positive rate of the proposed method can be effectively controlled, thereby ensuring certified detection. To enable seamless watermark injection across different paradigms, we leverage the widely adopted guidance technique as a plug-and-play mechanism and develop the \emph{watermark guidance}. This design enables Luminark to achieve generality across state-of-the-art generative models without compromising image quality. Empirically, we evaluate our approach on nine models spanning diffusion, autoregressive, and hybrid frameworks. Across all evaluations, Luminark consistently demonstrates high detection accuracy, strong robustness against common image transformations, and good performance on visual quality. --- ### A UAV-Based Multispectral and RGB Dataset for Multi-Stage Paddy Crop Monitoring in Indian Agricultural Fields Authors: Adari Rama Sukanya, Puvvula Roopesh Naga Sri Sai, Bodduru Neshika, Rimalapudi Sarvendranath Published: 2026-01-03 Categories: cs.CV, eess.IV arXiv: https://arxiv.org/abs/2601.01084v2 We present a large-scale unmanned aerial vehicle (UAV)-based RGB and multispectral image dataset collected over paddy fields in the Vijayawada region, Andhra Pradesh, India, covering nursery to harvesting stages. We used a 20-megapixel RGB camera and a 5-megapixel four-band multispectral camera capturing red, green, red-edge, and near-infrared bands. Standardised operating procedure (SOP) and checklists were developed to ensure repeatable data acquisition. Our dataset comprises of 42,430 raw images (415 GB) captured over 5 acres with 1 cm/pixel ground sampling distance (GSD) with associated metadata such as GPS coordinates, flight altitude, and environmental conditions. Captured images were validated using Pix4D Fields to generate orthomosaic maps and vegetation index maps, such as normalised difference vegetation index (NDVI) and normalised difference red-edge (NDRE) index. Our dataset is one of the few datasets that provide high-resolution images with rich metadata that cover all growth stages of Indian paddy crops. The dataset is available on IEEE DataPort with DOI, . It can support studies on targeted spraying, disease analysis, and yield estimation. --- ### Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces Authors: Bryon Tjanaka, Henry Chen, Matthew C. Fontaine, Stefanos Nikolaidis Published: 2026-01-03 Categories: cs.LG, cs.NE arXiv: https://arxiv.org/abs/2601.01082v5 Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-specified, vector-valued measure function. Contemporary QD algorithms are typically limited to low-dimensional measures because high-dimensional measures are prone to distortion, where many solutions found by the QD algorithm map to similar measures. For example, the state-of-the-art CMA-MAE algorithm guides measure space exploration with a histogram in measure space that records so-called discount values. However, CMA-MAE stagnates in domains with high-dimensional measure spaces because solutions with similar measures fall into the same histogram cell and hence receive the same discount value. To address these limitations, we propose Discount Model Search (DMS), which guides exploration with a model that provides a smooth, continuous representation of discount values. In high-dimensional measure spaces, this model enables DMS to distinguish between solutions with similar measures and thus continue exploration. We show that DMS facilitates new capabilities for QD algorithms by introducing two new domains where the measure space is the high-dimensional space of images, which enables users to specify their desired measures by providing a dataset of images rather than hand-designing the measure function. Results in these domains and on high-dimensional benchmarks show that DMS outperforms CMA-MAE and other existing black-box QD algorithms. --- ### Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees Authors: Devesh Nath, Haoran Yin, Glen Chou Published: 2026-01-03 Categories: eess.SY, cs.AI, cs.LG, cs.RO, math.OC arXiv: https://arxiv.org/abs/2601.01076v1 We propose a scalable reachability-based framework for probabilistic, data-driven safety verification of unknown nonlinear dynamics. We use Koopman theory with a neural network (NN) lifting function to learn an approximate linear representation of the dynamics and design linear controllers in this space to enable closed-loop tracking of a reference trajectory distribution. Closed-loop reachable sets are efficiently computed in the lifted space and mapped back to the original state space via NN verification tools. To capture model mismatch between the Koopman dynamics and the true system, we apply conformal prediction to produce statistically-valid error bounds that inflate the reachable sets to ensure the true trajectories are contained with a user-specified probability. These bounds generalize across references, enabling reuse without recomputation. Results on high-dimensional MuJoCo tasks (11D Hopper, 28D Swimmer) and 12D quadcopters show improved reachable set coverage rate, computational efficiency, and conservativeness over existing methods. --- ### Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments Authors: Hansen Jin Lillemark, Benhao Huang, Fangneng Zhan, Yilun Du, Thomas Anderson Keller Published: 2026-01-03 Categories: cs.LG, cs.AI, cs.CV arXiv: https://arxiv.org/abs/2601.01075v2 Embodied systems experience the world as 'a symphony of flows': a combination of many continuous streams of sensory input coupled to self-motion, interwoven with the dynamics of external objects. These sensory streams and the underlying dynamics of the world obey smooth, time-parameterized symmetries which existing world models ignore. Without a memory that respects this structure, partial observability presents a major obstacle to existing methods: each observation reveals only a fraction of the world, while unobserved regions continue to evolve. In this work, we introduce Flow Equivariant World Modeling, a framework that leverages time-parameterized symmetries within a latent memory for stable and accurate dynamics prediction over long horizons. The latent memory shifts and transforms equivariantly with self-motion and inferred external object motion, keeping information about out-of-view regions aligned as time progresses. We demonstrate the advantage of this framework over state-of-the-art diffusion, memory-augmented, and recurrent world model architectures on 2D and 3D partially observed video world modeling benchmarks. More broadly, our results suggest that predictive representations become more powerful when they are organized in line with the temporal and dynamical structure of the world they model. Project page: https://flowequivariantworldmodels.github.io/ --- ### Gendered Pathways in AI Companionship: Cross-Community Behavior and Toxicity Patterns on Reddit Authors: Erica Coppolillo, Emilio Ferrara Published: 2026-01-03 Categories: cs.SI, cs.AI arXiv: https://arxiv.org/abs/2601.01073v1 AI-companionship platforms are rapidly reshaping how people form emotional, romantic, and parasocial bonds with non-human agents, raising new questions about how these relationships intersect with gendered online behavior and exposure to harmful content. Focusing on the MyBoyfriendIsAI (MBIA) subreddit, we reconstruct the Reddit activity histories of more than 3,000 highly engaged users over two years, yielding over 67,000 historical submissions. We then situate MBIA within a broader ecosystem by building a historical interaction network spanning more than 2,000 subreddits, which enables us to trace cross-community pathways and measure how toxicity and emotional expression vary across these trajectories. We find that MBIA users primarily traverse four surrounding community spheres (AI-companionship, porn-related, forum-like, and gaming) and that participation across the ecosystem exhibits a distinct gendered structure, with substantial engagement by female users. While toxicity is generally low across most pathways, we observe localized spikes concentrated in a small subset of AI-porn and gender-oriented communities. Nearly 16% of users engage with gender-focused subreddits, and their trajectories display systematically different patterns of emotional expression and elevated toxicity, suggesting that a minority of gendered pathways may act as toxicity amplifiers within the broader AI-companionship ecosystem. These results characterize the gendered structure of cross-community participation around AI companionship on Reddit and highlight where risks concentrate, informing measurement, moderation, and design practices for human-AI relationship platforms. ---