OpenRaiser/Intern-Atlas huggingface dataset:
S3-CLIP: Video Super Resolution for Person-ReID
Venue: arXiv cs.CV 2026
Year: 2026

Tracklet quality is often treated as an afterthought in most person re-identification (ReID) methods, with the majority of research presenting architectural modifications to foundational models. Such approaches neglect an important limitation, posing challenges when deploying ReID systems in real-world, difficult scenarios. In this paper, we introduce S3-CLIP, a video super-resolution-based CLIP-ReID framework developed for the VReID-XFD challenge at WACV 2026. The proposed method integrates recent advances in super-resolution networks with task-driven super-resolution pipelines, adapting them to the video-based person re-identification setting. To the best of our knowledge, this work represents the first systematic investigation of video super-resolution as a means of enhancing tracklet quality for person ReID, particularly under challenging cross-view conditions. Experimental results demonstrate performance competitive with the baseline, achieving 37.52% mAP in aerial-to-ground and 29.16% mAP in ground-to-aerial scenarios. In the ground-to-aerial setting, S3-CLIP achieves substantial gains in ranking accuracy, improving Rank-1, Rank-5, and Rank-10 performance by 11.24%, 13.48%, and 17.98%, respectively.
Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge
Venue: arXiv cs.CL 2026
Year: 2026

Large language models often solve complex reasoning tasks more effectively with Chain-of-Thought (CoT), but at the cost of long, low-bandwidth token sequences. Humans, by contrast, often reason softly by maintaining a distribution over plausible next steps. Motivated by this, we propose Multiplex Thinking, a stochastic soft reasoning mechanism that, at each thinking step, samples K candidate tokens and aggregates their embeddings into a single continuous multiplex token. This preserves the vocabulary embedding prior and the sampling dynamics of standard discrete generation, while inducing a tractable probability distribution over multiplex rollouts. Consequently, multiplex trajectories can be directly optimized with on-policy reinforcement learning (RL). Importantly, Multiplex Thinking is self-adaptive: when the model is confident, the multiplex token is nearly discrete and behaves like standard CoT; when it is uncertain, it compactly represents multiple plausible next steps without increasing sequence length. Across challenging math reasoning benchmarks, Multiplex Thinking consistently outperforms strong discrete CoT and RL baselines from Pass@1 through Pass@1024, while producing shorter sequences. The code and checkpoints are available at https://github.com/GMLR-Penn/Multiplex-Thinking.
Motion Attribution for Video Generation
Venue: arXiv cs.CV 2026
Year: 2026

Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. We present Motive (MOTIon attribution for Video gEneration), a motion-centric, gradient-based data attribution framework that scales to modern, large, high-quality video datasets and models. We use this to study which fine-tuning clips improve or degrade temporal dynamics. Motive isolates temporal dynamics from static appearance via motion-weighted loss masks, yielding efficient and scalable motion-specific influence computation. On text-to-video models, Motive identifies clips that strongly affect motion and guides data curation that improves temporal consistency and physical plausibility. With Motive-selected high-influence data, our method improves both motion smoothness and dynamic degree on VBench, achieving a 74.1% human preference win rate compared with the pretrained base model. To our knowledge, this is the first framework to attribute motion rather than visual appearance in video generative models and to use it to curate fine-tuning data.
Modeling LLM Agent Reviewer Dynamics in Elo-Ranked Review System
Venue: arXiv cs.CL 2026
Year: 2026

In this work, we explore the Large Language Model (LLM) agent reviewer dynamics in an Elo-ranked review system using real-world conference paper submissions. Multiple LLM agent reviewers with different personas are engage in multi round review interactions moderated by an Area Chair. We compare a baseline setting with conditions that incorporate Elo ratings and reviewer memory. Our simulation results showcase several interesting findings, including how incorporating Elo improves Area Chair decision accuracy, as well as reviewers' adaptive review strategy that exploits our Elo system without improving review effort. Our code is available at https://github.com/hsiangwei0903/EloReview.
RAVEN: Erasing Invisible Watermarks via Novel View Synthesis
Venue: arXiv cs.CV 2026
Year: 2026

Invisible watermarking has become a critical mechanism for authenticating AI-generated image content, with major platforms deploying watermarking schemes at scale. However, evaluating the vulnerability of these schemes against sophisticated removal attacks remains essential to assess their reliability and guide robust design. In this work, we expose a fundamental vulnerability in invisible watermarks by reformulating watermark removal as a view synthesis problem. Our key insight is that generating a perceptually consistent alternative view of the same semantic content, akin to re-observing a scene from a shifted perspective, naturally removes the embedded watermark while preserving visual fidelity. This reveals a critical gap: watermarks robust to pixel-space and frequency-domain attacks remain vulnerable to semantic-preserving viewpoint transformations. We introduce a zero-shot diffusion-based framework that applies controlled geometric transformations in latent space, augmented with view-guided correspondence attention to maintain structural consistency during reconstruction. Operating on frozen pre-trained models without detector access or watermark knowledge, our method achieves state-of-the-art watermark suppression across 15 watermarking methods--outperforming 14 baseline attacks while maintaining superior perceptual quality across multiple datasets.
LAUDE: LLM-Assisted Unit Test Generation and Debugging of Hardware DEsigns
Venue: arXiv cs.SE 2026
Year: 2026

Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level. Thus developing unit tests targeting various design features requires deep understanding of the design functionality and creativity. When one or more unit tests expose a design failure, the debugging engineer needs to diagnose, localize, and debug the failure to ensure design correctness, which is often a painstaking and intense process. In this work, we introduce LAUDE, a unified unit-test generation and debugging framework for hardware designs that cross-pollinates the semantic understanding of the design source code with the Chain-of-Thought (CoT) reasoning capabilities of foundational Large-Language Models (LLMs). LAUDE integrates prompt engineering and design execution information to enhance its unit test generation accuracy and code debuggability. We apply LAUDE with closed- and open-source LLMs to a large corpus of buggy hardware design codes derived from the VerilogEval dataset, where generated unit tests detected bugs in up to 100% and 93% of combinational and sequential designs and debugged up to 93% and 84% of combinational and sequential designs, respectively.
Stimulating Higher Order Thinking in Mechatronics by Comparing PID and Fuzzy Control
Venue: arXiv cs.CY 2026
Year: 2026

Many studies have found active learning, either in the form of in-class exercises or projects, to be superior to traditional lectures. However, these forms of hands-on learning do not always lead students to reach the higher order thinking skills associated with the highest levels of Bloom's Taxonomy (analysis, synthesis, and evaluation). Assignments that expect students to follow a prescribed approach to reach a well-defined solution contribute to a lack of higher order thinking at the college level. Professional engineers often face complex and ambiguous problems that require design decisions for which there is no straightforward answer. To strengthen the higher order thinking skills demanded by such problems, we developed a project in a semester-long mechatronics course in which students must evaluate two automatic control methodologies without being given explicit performance criteria or experimental procedures. Specifically, the project involves determining the superior control method for leader-follower behavior, where a ground vehicle autonomously follows a lead vehicle. Laboratory exercises throughout the semester expose students to the skills required for the project, including using sensors and actuators, programming proportional-integral-derivative (PID) and fuzzy controllers, and applying computer vision to detect an object signature. In the final course project, students go beyond implementing individual controllers and create their own evaluation criteria and experiments to make a design decision between PID and fuzzy control. We implemented this approach over three semesters and found that students value working on a real-world, open-ended problem, develop creative performance criteria and evaluation methods that demonstrate higher order thinking, and discover that comparative studies are nontrivial due to the many factors influencing performance.
The Illusion of Friendship: Why Generative AI Demands Unprecedented Ethical Vigilance
Venue: arXiv cs.CY 2026
Year: 2026

GenAI systems are increasingly used for drafting, summarisation, and decision support, offering substantial gains in productivity and reduced cognitive load. However, the same natural language fluency that makes these systems useful can also blur the boundary between tool and companion. This boundary confusion may encourage some users to experience GenAI as empathic, benevolent, and relationally persistent. Emerging reports suggest that some users may form emotionally significant attachments to conversational agents, in some cases with harmful consequences, including dependency and impaired judgment. This paper develops a philosophical and ethical argument for why the resulting illusion of friendship is both understandable and can be ethically risky. Drawing on classical accounts of friendship, the paper explains why users may understandably interpret sustained supportive interaction as friend like. It then advances a counterargument that despite relational appearances, GenAI lacks moral agency: consciousness, intention, and accountability and therefore does not qualify as a true friend. To demystify the illusion, the paper presents a mechanism level explanation of how transformer based GenAI generates responses often producing emotionally resonant language without inner states or commitments. Finally, the paper proposes a safeguard framework for safe and responsible GenAI use to reduce possible anthropomorphic cues generated by the GenAI systems. The central contribution is to demystify the illusion of friendship and explain the computational background so that we can shift the emotional attachment with GenAI towards necessary human responsibility and thereby understand how institutions, designers, and users can preserve GenAI's benefits while mitigating over reliance and emotional misattribution.
Silenced by Design Censorship, Governance, and the Politics of Access in Generative AI Refusal Behavior
Venue: arXiv cs.CY 2026
Year: 2026

This paper examines refusal behavior in generative AI systems through a governance lens. Drawing on historical frameworks of censorship and contemporary design logics, it argues that refusal is not a neutral safeguard but a site of power, shaped by institutional risk management and opaque decision-making. The analysis concludes with user-centered recommendations for ethical refusal design. Keywords: Generative AI governance, AI refusal behavior, Censorship, Ethical design
TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
Venue: arXiv cs.CV 2026
Year: 2026

Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject-driven generation). While the sparse Mixture-of-Experts (MoE) paradigm is a promising solution, its gating networks remain task-agnostic, operating based on local features, unaware of global task intent. This task-agnostic nature prevents meaningful specialization and fails to resolve the underlying task interference. In this paper, we propose a novel framework to inject semantic intent into MoE routing. We introduce a Hierarchical Task Semantic Annotation scheme to create structured task descriptors (e.g., scope, type, preservation). We then design Predictive Alignment Regularization to align internal routing decisions with the task's high-level semantics. This regularization evolves the gating network from a task-agnostic executor to a dispatch center. Our model effectively mitigates task interference, outperforming dense baselines in fidelity and quality, and our analysis shows that experts naturally develop clear and semantically correlated specializations.
XGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validation
Venue: arXiv cs.LG 2026
Year: 2026

This study develops a robust machine learning framework for one-step-ahead forecasting of daily log-returns in the Nepal Stock Exchange (NEPSE) Index using the XGBoost regressor. A comprehensive feature set is engineered, including lagged log-returns (up to 30 days) and established technical indicators such as short- and medium-term rolling volatility measures and the 14-period Relative Strength Index. Hyperparameter optimization is performed using Optuna with time-series cross-validation on the initial training segment. Out-of-sample performance is rigorously assessed via walk-forward validation under both expanding and fixed-length rolling window schemes across multiple lag configurations, simulating real-world deployment and avoiding lookahead bias. Predictive accuracy is evaluated using root mean squared error, mean absolute error, coefficient of determination (R-squared), and directional accuracy on both log-returns and reconstructed closing prices. Empirical results show that the optimal configuration, an expanding window with 20 lags, outperforms tuned ARIMA and Ridge regression benchmarks, achieving the lowest log-return RMSE (0.013450) and MAE (0.009814) alongside a directional accuracy of 65.15%. While the R-squared remains modest, consistent with the noisy nature of financial returns, primary emphasis is placed on relative error reduction and directional prediction. Feature importance analysis and visual inspection further enhance interpretability. These findings demonstrate the effectiveness of gradient boosting ensembles in modeling nonlinear dynamics in volatile emerging market time series and establish a reproducible benchmark for NEPSE Index forecasting.
Navigating Ideation Space: Decomposed Conceptual Representations for Positioning Scientific Ideas
Venue: arXiv cs.IR 2026
Year: 2026

Scientific discovery is a cumulative process and requires new ideas to be situated within an ever-expanding landscape of existing knowledge. An emerging and critical challenge is how to identify conceptually relevant prior work from rapidly growing literature, and assess how a new idea differentiates from existing research. Current embedding approaches typically conflate distinct conceptual aspects into single representations and cannot support fine-grained literature retrieval; meanwhile, LLM-based evaluators are subject to sycophancy biases, failing to provide discriminative novelty assessment. To tackle these challenges, we introduce the Ideation Space, a structured representation that decomposes scientific knowledge into three distinct dimensions, i.e., research problem, methodology, and core findings, each learned through contrastive training. This framework enables principled measurement of conceptual distance between ideas, and modeling of ideation transitions that capture the logical connections within a proposed idea. Building upon this representation, we propose a Hierarchical Sub-Space Retrieval framework for efficient, targeted literature retrieval, and a Decomposed Novelty Assessment algorithm that identifies which aspects of an idea are novel. Extensive experiments demonstrate substantial improvements, where our approach achieves Recall@30 of 0.329 (16.7% over baselines), our ideation transition retrieval reaches Hit Rate@30 of 0.643, and novelty assessment attains 0.37 correlation with expert judgments. In summary, our work provides a promising paradigm for future research on accelerating and evaluating scientific discovery.
Fine Grained Evaluation of LLMs-as-Judges
Venue: arXiv cs.IR 2026
Year: 2026

A good deal of recent research has focused on how Large Language Models (LLMs) may be used as `judges' in place of humans to evaluate the quality of the output produced by various text / image processing systems. Within this broader context, a number of studies have investigated the specific question of how effectively LLMs can be used as relevance assessors for the standard ad hoc task in Information Retrieval (IR). We extend these studies by looking at additional questions. Most importantly, we use a Wikipedia based test collection created by the INEX initiative, and prompt LLMs to not only judge whether documents are relevant / non-relevant, but to highlight relevant passages in documents that it regards as useful. The human relevance assessors involved in creating this collection were given analogous instructions, i.e., they were asked to highlight all passages within a document that respond to the information need expressed in a query. This enables us to evaluate the quality of LLMs as judges not only at the document level, but to also quantify how often these `judges' are right for the right reasons. Our findings suggest that LLMs-as-judges work best under human supervision.
Fairness risk and its privacy-enabled solution in AI-driven robotic applications
Venue: arXiv cs.RO 2026
Year: 2026

Complex decision-making by autonomous machines and algorithms could underpin the foundations of future society. Generative AI is emerging as a powerful engine for such transitions. However, we show that Generative AI-driven developments pose a critical pitfall: fairness concerns. In robotic applications, although intuitions about fairness are common, a precise and implementable definition that captures user utility and inherent data randomness is missing. Here we provide a utility-aware fairness metric for robotic decision making and analyze fairness jointly with user-data privacy, deriving conditions under which privacy budgets govern fairness metrics. This yields a unified framework that formalizes and quantifies fairness and its interplay with privacy, which is tested in a robot navigation task. In view of the fact that under legal requirements, most robotic systems will enforce user privacy, the approach shows surprisingly that such privacy budgets can be jointly used to meet fairness targets. Addressing fairness concerns in the creative combined consideration of privacy is a step towards ethical use of AI and strengthens trust in autonomous robots deployed in everyday environments.
Leveraging learning analytics to enhance immersive teacher simulations: Challenges and opportunities
Venue: arXiv cs.HC 2026
Year: 2026

This chapter examines how data analytics can be leveraged to enhance immersive teacher simulations, situating this inquiry within the broader learning sciences discourse on embodied cognition, data-informed feedback, and teacher professional learning. It explores both conceptual foundations and empirical cases to illustrate how analytics serve as mediational tools that connect immersive experiences with reflective teaching practice. The chapter unfolds in multiple sections: (1) The Innovation Journey: An Overview of Immersive Teacher Simulations outlines the evolution from traditional simulations to XR-based environments, highlighting the need for professional decision-making under realistic constraints. (2) Innovation in Existing Research and Practice situates teacher analytics within the trajectory from descriptive observation to multimodal and predictive modeling. (3) Study Approach and Design details how multimodal data-discourse, behavior, and gaze-from the TeacherGen@i simulation were collected and organized to reveal cognitive distribution of pedagogical discourse and interaction patterns. (4) Findings present the cognitive distribution of preservice teachers' pedagogical discourse and the sequential interaction patterns that emerge in exchange, illustrating how multimodal analytics make pedagogical reasoning processes visible within immersive simulations. (5) Understanding Innovative Practices in Teacher Education examines teaching analytics to enhance immersive teacher simulation based on the findings of the study. (6) Key Takeaways of the Innovation Journey identifies research challenges and design implications for scalable, analytics-enhanced teacher education. Together, these sections position immersive teacher simulations as a pivotal testbed for aligning learning analytics, professional learning, and next-generation immersive learning environment design.
Variance-Penalized MC-Dropout as a Learned Smoothing Prior for Brain Tumour Segmentation
Venue: arXiv cs.CV 2026
Year: 2026

Brain tumor segmentation is essential for diagnosis and treatment planning, yet many CNN and U-Net based approaches produce noisy boundaries in regions of tumor infiltration. We introduce UAMSA-UNet, an Uncertainty-Aware Multi-Scale Attention-based Bayesian U-Net that in- stead leverages Monte Carlo Dropout to learn a data-driven smoothing prior over its predictions, while fusing multi-scale features and attention maps to capture both fine details and global context. Our smoothing-regularized loss augments binary cross-entropy with a variance penalty across stochas- tic forward passes, discouraging spurious fluctuations and yielding spatially coherent masks. On BraTS2023, UAMSA- UNet improves Dice Similarity Coefficient by up to 3.3% and mean IoU by up to 2.7% over U-Net; on BraTS2024, it delivers up to 4.5% Dice and 4.0% IoU gains over the best baseline. Remarkably, it also reduces FLOPs by 42.5% rel- ative to U-Net++ while maintaining higher accuracy. These results demonstrate that, by combining multi-scale attention with a learned smoothing prior, UAMSA-UNet achieves both better segmentation quality and computational efficiency, and provides a flexible foundation for future integration with transformer-based modules for further enhanced segmenta- tion results.
An Almost-Optimal Upper Bound on the Push Number of the Torus Puzzle
Venue: arXiv cs.DS 2026
Year: 2026

We study the Torus Puzzle, a solitaire game in which the elements of an input $m \times n$ matrix need to be rearranged into a target configuration via a sequence of unit rotations (i.e., circular shifts) of rows and/or columns. Amano et al.\ proposed a more permissive variant of the above puzzle, where each row and column rotation can shift the involved elements by any amount of positions. The number of rotations needed to solve the puzzle in the original and in the permissive variants of the puzzle are respectively known as the \emph{push number} and the \emph{drag number}, where the latter is always smaller than or equal to the former and admits an existential lower bound of $Ω(mn)$. While this lower bound is matched by an $O(mn)$ upper bound, the push number is not so well understood. Indeed, to the best of our knowledge, only an $O(mn \cdot \max\{ m, n \})$ upper bound is currently known. In this paper, we provide an algorithm that solves the Torus Puzzle using $O(mn \cdot \log \max \{m, n\})$ unit rotations in a model that is more restricted than that of the original puzzle. This implies a corresponding upper bound on the push number and reduces the gap between the known upper and lower bounds from $Θ(\max\{m,n\})$ to $Θ(\log \max\{m, n\})$.