OpenRaiser/Intern-Atlas huggingface dataset:
Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction
Venue: arXiv cs.NI 2026
Year: 2026

Network traffic prediction is essential for automating modern network management. It is a difficult time series forecasting (TSF) problem that has been addressed by Deep Learning (DL) models due to their ability to capture complex patterns. Advances in forecasting, from sophisticated transformer architectures to simple linear models, have improved performance across diverse prediction tasks. However, given the variability of network traffic across network environments and traffic series timescales, it is essential to identify effective deployment choices and modeling directions for network traffic prediction. This study systematically identify and evaluates twelve advanced TSF models -- including transformer-based and traditional DL approaches, each with unique advantages for network traffic prediction -- against three statistical baselines on four real traffic datasets, across multiple time scales and horizons, assessing performance, robustness to anomalies, data gaps, external factors, data efficiency, and resource efficiency in terms of time, memory, and energy. Results highlight performance regimes, efficiency thresholds, and promising architectures that balance accuracy and efficiency, demonstrating robustness to traffic challenges and suggesting new directions beyond traditional RNNs.
Exact Constructive Digit-by-Digit Algorithms for Integer $e$-th Root Extraction
Venue: arXiv cs.SC 2026
Year: 2026

We present a unified constructive digit-by-digit framework for exact root extraction using only integer arithmetic. The core contribution is a complete correctness theory for the fractional square root algorithm, proving that each computed decimal digit is exact and final, together with a sharp truncation error bound of $10^{-k}$ after $k$ digits. We further develop an invariant-based framework for computing the integer $e$-th root $\lfloor N^{1/e} \rfloor$ of a non-negative integer $N$ for arbitrary fixed exponents $e \ge 2$, derived directly from the binomial theorem. This method generalizes the classical long-division square root algorithm, preserves a constructive remainder invariant throughout the computation, and provides an exact decision procedure for perfect $e$-th power detection. We also explain why exact digit-by-digit fractional extraction with non-revisable digits is structurally possible only for square roots ($e=2$), whereas higher-order roots ($e \ge 3$) exhibit nonlinear coupling that prevents digit stability under scaling. All proofs are carried out in a constructive, algorithmic manner consistent with Bishop-style constructive mathematics, yielding explicit algorithmic witnesses, decidable predicates, and guaranteed termination. The resulting algorithms require no division or floating-point operations and are well suited to symbolic computation, verified exact arithmetic, educational exposition, and digital hardware implementation.
Analysis of Various Manipulator Configurations Based on Multi-Objective Black-Box Optimization
Venue: arXiv cs.RO 2026
Year: 2026

Various 6-degree-of-freedom (DOF) and 7-DOF manipulators have been developed to date. Over a long history, their joint configurations and link length ratios have been determined empirically. In recent years, the development of robotic foundation models has become increasingly active, leading to the continuous proposal of various manipulators to support these models. However, none of these manipulators share exactly the same structure, as the order of joints and the ratio of link lengths differ among robots. Therefore, in order to discuss the optimal structure of a manipulator, we performed multi-objective optimization from the perspectives of end-effector reachability and joint torque. We analyze where existing manipulator structures stand within the sampling results of the optimization and provide insights for future manipulator design.
CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory
Venue: arXiv cs.IR 2026
Year: 2026

Information retrieval (IR) in dynamic data streams is a crucial task, as shifts in data distribution degrade the performance of AI-powered IR systems. To mitigate this issue, memory-based continual learning has been widely adopted for IR. However, existing methods rely on a fixed set of queries with ground-truth documents, which limits generalization to unseen data, making them impractical for real-world applications. To enable more effective learning with unseen topics of a new corpus without ground-truth labels, we propose CREAM, a self-supervised framework for memory-based continual retrieval. CREAM captures the evolving semantics of streaming queries and documents into dynamically structured soft memory and leverages it to adapt to both seen and unseen topics in an unsupervised setting. We realize this through three key techniques: fine-grained similarity estimation, regularized cluster prototyping, and stratified coreset sampling. Experiments on two benchmark datasets demonstrate that CREAM exhibits superior adaptability and retrieval accuracy, outperforming the strongest method in a label-free setting by 27.79% in Success@5 and 44.5% in Recall@10 on average, and achieving performance comparable to or even exceeding that of supervised methods.
CRoPE: Efficient Parametrization of Rotary Positional Embedding
Venue: arXiv cs.LG 2026
Year: 2026

Rotary positional embedding has become the state-of-the-art approach to encode position information in transformer-based models. While it is often succinctly expressed in complex linear algebra, we note that the actual implementation of $Q/K/V$-projections is not equivalent to a complex linear transformation. We argue that complex linear transformation is a more natural parametrization and saves near 50\% parameters within the attention block. We show empirically that removing such redundancy has negligible impact on the model performance both in sample and out of sample. Our modification achieves more efficient parameter usage, as well as a cleaner interpretation of the representation space.
EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commerce
Venue: arXiv cs.CL 2026
Year: 2026

Large Language Model (LLM)-based agents are increasingly deployed in e-commerce applications to assist customer services in tasks such as product inquiries, recommendations, and order management. Existing benchmarks primarily evaluate whether these agents successfully complete the final task, overlooking the intermediate reasoning stages that are crucial for effective decision-making. To address this gap, we propose EComStage, a unified benchmark for evaluating agent-capable LLMs across the comprehensive stage-wise reasoning process: Perception (understanding user intent), Planning (formulating an action plan), and Action (executing the decision). EComStage evaluates LLMs through seven separate representative tasks spanning diverse e-commerce scenarios, with all samples human-annotated and quality-checked. Unlike prior benchmarks that focus only on customer-oriented interactions, EComStage also evaluates merchant-oriented scenarios, including promotion management, content review, and operational support relevant to real-world applications. We evaluate a wide range of over 30 LLMs, spanning from 1B to over 200B parameters, including open-source models and closed-source APIs, revealing stage/orientation-specific strengths and weaknesses. Our results provide fine-grained, actionable insights for designing and optimizing LLM-based agents in real-world e-commerce settings.
AnyDepth: Depth Estimation Made Easy
Venue: arXiv cs.CV 2026
Year: 2026

Monocular depth estimation aims to recover the depth information of 3D scenes from 2D images. Recent work has made significant progress, but its reliance on large-scale datasets and complex decoders has limited its efficiency and generalization ability. In this paper, we propose a lightweight and data-centric framework for zero-shot monocular depth estimation. We first adopt DINOv3 as the visual encoder to obtain high-quality dense features. Secondly, to address the inherent drawbacks of the complex structure of the DPT, we design the Simple Depth Transformer (SDT), a compact transformer-based decoder. Compared to the DPT, it uses a single-path feature fusion and upsampling process to reduce the computational overhead of cross-scale feature fusion, achieving higher accuracy while reducing the number of parameters by approximately 85%-89%. Furthermore, we propose a quality-based filtering strategy to filter out harmful samples, thereby reducing dataset size while improving overall training quality. Extensive experiments on five benchmarks demonstrate that our framework surpasses the DPT in accuracy. This work highlights the importance of balancing model design and data quality for achieving efficient and generalizable zero-shot depth estimation. Code: https://github.com/AIGeeksGroup/AnyDepth. Website: https://aigeeksgroup.github.io/AnyDepth.
UniSRCodec: Unified and Low-Bitrate Single Codebook Codec with Sub-Band Reconstruction
Venue: arXiv cs.SD 2026
Year: 2026

Neural Audio Codecs (NACs) can reduce transmission overhead by performing compact compression and reconstruction, which also aim to bridge the gap between continuous and discrete signals. Existing NACs can be divided into two categories: multi-codebook and single-codebook codecs. Multi-codebook codecs face challenges such as structural complexity and difficulty in adapting to downstream tasks, while single-codebook codecs, though structurally simpler, suffer from low-fidelity, ineffective modeling of unified audio, and an inability to support modeling of high-frequency audio. We propose the UniSRCodec, a single-codebook codec capable of supporting high sampling rate, low-bandwidth, high fidelity, and unified. We analyze the inefficiency of waveform-based compression and introduce the time and frequency compression method using the Mel-spectrogram, and cooperate with a Vocoder to recover the phase information of the original audio. Moreover, we propose a sub-band reconstruction technique to achieve high-quality compression across both low and high frequency bands. Subjective and objective experimental results demonstrate that UniSRCodec achieves state-of-the-art (SOTA) performance among cross-domain single-codebook codecs with only a token rate of 40, and its reconstruction quality is comparable to that of certain multi-codebook methods. Our demo page is available at https://wxzyd123.github.io/unisrcodec.
M-SEVIQ: A Multi-band Stereo Event Visual-Inertial Quadruped-based Dataset for Perception under Rapid Motion and Challenging Illumination
Venue: arXiv cs.RO 2026
Year: 2026

Agile locomotion in legged robots poses significant challenges for visual perception. Traditional frame-based cameras often fail in these scenarios for producing blurred images, particularly under low-light conditions. In contrast, event cameras capture changes in brightness asynchronously, offering low latency, high temporal resolution, and high dynamic range. These advantages make them suitable for robust perception during rapid motion and under challenging illumination. However, existing event camera datasets exhibit limitations in stereo configurations and multi-band sensing domains under various illumination conditions. To address this gap, we present M-SEVIQ, a multi-band stereo event visual and inertial quadruped dataset collected using a Unitree Go2 equipped with stereo event cameras, a frame-based camera, an inertial measurement unit (IMU), and joint encoders. This dataset contains more than 30 real-world sequences captured across different velocity levels, illumination wavelengths, and lighting conditions. In addition, comprehensive calibration data, including intrinsic, extrinsic, and temporal alignments, are provided to facilitate accurate sensor fusion and benchmarking. Our M-SEVIQ can be used to support research in agile robot perception, sensor fusion, semantic segmentation and multi-modal vision in challenging environments.
StableDPT: Temporal Stable Monocular Video Depth Estimation
Venue: arXiv cs.CV 2026
Year: 2026

Applying single image Monocular Depth Estimation (MDE) models to video sequences introduces significant temporal instability and flickering artifacts. We propose a novel approach that adapts any state-of-the-art image-based (depth) estimation model for video processing by integrating a new temporal module - trainable on a single GPU in a few days. Our architecture StableDPT builds upon an off-the-shelf Vision Transformer (ViT) encoder and enhances the Dense Prediction Transformer (DPT) head. The core of our contribution lies in the temporal layers within the head, which use an efficient cross-attention mechanism to integrate information from keyframes sampled across the entire video sequence. This allows the model to capture global context and inter-frame relationships leading to more accurate and temporally stable depth predictions. Furthermore, we propose a novel inference strategy for processing videos of arbitrary length avoiding the scale misalignment and redundant computations associated with overlapping windows used in other methods. Evaluations on multiple benchmark datasets demonstrate improved temporal consistency, competitive state-of-the-art performance and on top 2x faster processing in real-world scenarios.
Reinforcement Learning for Follow-the-Leader Robotic Endoscopic Navigation via Synthetic Data
Venue: arXiv cs.RO 2026
Year: 2026

Autonomous navigation is crucial for both medical and industrial endoscopic robots, enabling safe and efficient exploration of narrow tubular environments without continuous human intervention, where avoiding contact with the inner walls has been a longstanding challenge for prior approaches. We present a follow-the-leader endoscopic robot based on a flexible continuum structure designed to minimize contact between the endoscope body and intestinal walls, thereby reducing patient discomfort. To achieve this objective, we propose a vision-based deep reinforcement learning framework guided by monocular depth estimation. A realistic intestinal simulation environment was constructed in \textit{NVIDIA Omniverse} to train and evaluate autonomous navigation strategies. Furthermore, thousands of synthetic intraluminal images were generated using NVIDIA Replicator to fine-tune the Depth Anything model, enabling dense three-dimensional perception of the intestinal environment with a single monocular camera. Subsequently, we introduce a geometry-aware reward and penalty mechanism to enable accurate lumen tracking. Compared with the original Depth Anything model, our method improves $δ_{1}$ depth accuracy by 39.2% and reduces the navigation J-index by 0.67 relative to the second-best method, demonstrating the robustness and effectiveness of the proposed approach.
Case Count Metric for Comparative Analysis of Entity Resolution Results
Venue: arXiv cs.DB 2026
Year: 2026

This paper describes a new process and software system, the Case Count Metric System (CCMS), for systematically comparing and analyzing the outcomes of two different ER clustering processes acting on the same dataset when the true linking (labeling) is not known. The CCMS produces a set of counts that describe how the clusters produced by the first process are transformed by the second process based on four possible transformation scenarios. The transformations are that a cluster formed in the first process either remains unchanged, merges into a larger cluster, is partitioned into smaller clusters, or otherwise overlaps with multiple clusters formed in the second process. The CCMS produces a count for each of these cases, accounting for every cluster formed in the first process. In addition, when run in analysis mode, the CCMS program can assist the user in evaluating these changes by displaying the details for all changes or only for certain types of changes. The paper includes a detailed description of the CCMS process and program and examples of how the CCMS has been applied in university and industry research.
SketchThinker-R1: Towards Efficient Sketch-Style Reasoning in Large Multimodal Models
Venue: arXiv cs.CV 2026
Year: 2026

Despite the empirical success of extensive, step-by-step reasoning in large multimodal models, long reasoning processes inevitably incur substantial computational overhead, i.e., in terms of higher token costs and increased response time, which undermines inference efficiency. In contrast, humans often employ sketch-style reasoning: a concise, goal-directed cognitive process that prioritizes salient information and enables efficient problem-solving. Inspired by this cognitive efficiency, we propose SketchThinker-R1, which incentivizes sketch-style reasoning ability in large multimodal models. Our method consists of three primary stages. In the Sketch-Mode Cold Start stage, we convert standard long reasoning process into sketch-style reasoning and finetune base multimodal model, instilling initial sketch-style reasoning capability. Next, we train SketchJudge Reward Model, which explicitly evaluates thinking process of model and assigns higher scores to sketch-style reasoning. Finally, we conduct Sketch-Thinking Reinforcement Learning under supervision of SketchJudge to further generalize sketch-style reasoning ability. Experimental evaluation on four benchmarks reveals that our SketchThinker-R1 achieves over 64% reduction in reasoning token cost without compromising final answer accuracy. Qualitative analysis further shows that sketch-style reasoning focuses more on key cues during problem solving.
Resolution deficits drive simulator sickness and compromise reading performance in virtual environments
Venue: arXiv cs.HC 2026
Year: 2026

Extended reality (XR) is evolving into a general-purpose computing platform, yet its adoption for productivity is hindered by visual fatigue and simulator sickness. While these symptoms are often attributed to latency or motion conflicts, the precise impact of textual clarity on physiological comfort remains undefined. Here we show that sub-optimal effective resolution, the clarity that reaches the eye after the full display-optics-rendering pipeline, is a primary driver of simulator sickness during reading tasks in both virtual reality and video see-through environments. By systematically manipulating end-to-end effective resolution on a unified logMAR scale, we measured reading psychophysics and sickness symptoms in a controlled within-subjects study. We find that reading performance and user comfort degrade exponentially as resolution drops below 0 logMAR (normal visual acuity). Notably, our results reveal 0 logMAR as a key physiological tipping point: resolutions better than this threshold yield naked-eye-level performance with minimal sickness, whereas poorer resolutions trigger rapid, non-linear increases in nausea and oculomotor strain. These findings suggest that the cognitive and perceptual effort required to resolve blurry text directly compromises user comfort, establishing human-eye resolution as a critical baseline for the design of future ergonomic XR systems.
A Practical 73/50 Approximation for Contiguous Monotone Moldable Job Scheduling
Venue: arXiv cs.DS 2026
Year: 2026

In moldable job scheduling, we are provided $m$ identical machines and $n$ jobs that can be executed on a variable number of machines. The execution time of each job depends on the number of machines assigned to execute that job. For the specific problem of monotone moldable job scheduling, jobs are assumed to have a processing time that is non-increasing in the number of machines. The previous best-known algorithms are: (1) a polynomial-time approximation scheme with time complexity $Ω(n^{g(1/\varepsilon)})$, where $g(\cdot)$ is a super-exponential function [Jansen and Thöle '08; Jansen and Land '18], (2) a fully polynomial approximation scheme for the case of $m \geq 8\frac{n}{\varepsilon}$ [Jansen and Land '18], and (3) a $\frac{3}{2}$ approximation with time complexity $O(nm\log(mn))$ [Wu, Zhang, and Chen '23]. We present a new practically efficient algorithm with an approximation ratio of $\approx (1.4593 + \varepsilon)$ and a time complexity of $O(nm \log \frac{1}{\varepsilon})$. Our result also applies to the contiguous variant of the problem. In addition to our theoretical results, we implement the presented algorithm and show that the practical performance is significantly better than the theoretical worst-case approximation ratio.
Context-aware Privacy Bounds for Linear Queries
Venue: arXiv cs.IT 2026
Year: 2026

Linear queries, as the basis of broad analysis tasks, are often released through privacy mechanisms based on differential privacy (DP), the most popular framework for privacy protection. However, DP adopts a context-free definition that operates independently of the data-generating distribution. In this paper, we revisit the privacy analysis of the Laplace mechanism through the lens of pointwise maximal leakage (PML). We demonstrate that the distribution-agnostic definition of the DP framework often mandates excessive noise. To address this, we incorporate an assumption about the prior distribution by lower-bounding the probability of any single record belonging to any specific class. With this assumption, we derive a tight, context-aware leakage bound for general linear queries, and prove that our derived bound is strictly tighter than the standard DP guarantee and converges to the DP guarantee as this probability lower bound approaches zero. Numerical evaluations demonstrate that by exploiting this prior knowledge, the required noise scale can be reduced while maintaining privacy guarantees.
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection
Venue: arXiv cs.AI 2026
Year: 2026

Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcement learning have proven effective for training such agents, their performance is heavily constrained by the scarcity of high-quality, goal-oriented domain-specific training data. To address this challenge, we propose SimRPD, a three-stage framework for training recruitment proactive dialogue agents. First, we develop a high-fidelity user simulator to synthesize large-scale conversational data through multi-turn online dialogue. Then we introduce a multi-dimensional evaluation framework based on Chain-of-Intention (CoI) to comprehensively assess the simulator and effectively select high-quality data, incorporating both global-level and instance-level metrics. Finally, we train the recruitment proactive dialogue agent on the selected dataset. Experiments in a real-world recruitment scenario demonstrate that SimRPD outperforms existing simulator-based data selection strategies, highlighting its practical value for industrial deployment and its potential applicability to other business-oriented dialogue scenarios.