OpenRaiser/Intern-Atlas huggingface dataset:
TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data
Venue: arXiv cs.AI 2025
Year: 2025

Complex reasoning over tabular data is crucial in real-world data analysis, yet large language models (LLMs) often underperform due to complex queries, noisy data, and limited numerical capabilities. To address these issues, we propose TabDSR, a framework consisting of: (1) a query decomposer that breaks down complex questions, (2) a table sanitizer that cleans and filters noisy tables, and (3) a program-of-thoughts (PoT)-based reasoner that generates executable code to derive the final answer from the sanitized table. To ensure unbiased evaluation and mitigate data leakage, we introduce a new dataset, CalTab151, specifically designed for complex numerical reasoning over tables. Experimental results demonstrate that TabDSR consistently outperforms existing methods, achieving state-of-the-art (SOTA) performance with 8.79%, 6.08%, and 19.87% accuracy improvement on TAT-QA, TableBench, and TabDSR, respectively. Moreover, our framework integrates seamlessly with mainstream LLMs, providing a robust solution for complex tabular numerical reasoning. These findings highlight the effectiveness of our framework in enhancing LLM performance for complex tabular numerical reasoning. Data and code are available upon request.
LACY: A Vision-Language Model-based Language-Action Cycle for Self-Improving Robotic Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Learning generalizable policies for robotic manipulation increasingly relies on large-scale models that map language instructions to actions (L2A). However, this one-way paradigm often produces policies that execute tasks without deeper contextual understanding, limiting their ability to generalize or explain their behavior. We argue that the complementary skill of mapping actions back to language (A2L) is essential for developing more holistic grounding. An agent capable of both acting and explaining its actions can form richer internal representations and unlock new paradigms for self-supervised learning. We introduce LACY (Language-Action Cycle), a unified framework that learns such bidirectional mappings within a single vision-language model. LACY is jointly trained on three synergistic tasks: generating parameterized actions from language (L2A), explaining observed actions in language (A2L), and verifying semantic consistency between two language descriptions (L2C). This enables a self-improving cycle that autonomously generates and filters new training data through an active augmentation strategy targeting low-confidence cases, thereby improving the model without additional human labels. Experiments on pick-and-place tasks in both simulation and the real world show that LACY improves task success rates by 56.46% on average and yields more robust language-action grounding for robotic manipulation. Project page: https://vla2026.github.io/LACY/
When Modalities Conflict: How Unimodal Reasoning Uncertainty Governs Preference Dynamics in MLLMs
Venue: arXiv cs.AI 2025
Year: 2025

Multimodal large language models (MLLMs) must resolve conflicts when different modalities provide contradictory information, a process we term modality following. Prior work measured this behavior only with coarse dataset-level statistics, overlooking the influence of model's confidence in unimodal reasoning. In this paper, we introduce a new framework that decomposes modality following into two fundamental factors: relative reasoning uncertainty (the case-specific confidence gap between unimodal predictions) and inherent modality preference( a model's stable bias when uncertainties are balanced). To validate this framework, we construct a controllable dataset that systematically varies the reasoning difficulty of visual and textual inputs. Using entropy as a fine-grained uncertainty metric, we uncover a universal law: the probability of following a modality decreases monotonically as its relative uncertainty increases. At the relative difficulty level where the model tends to follow both modalities with comparable probability what we call the balance point, a practical indicator of the model's inherent preference. Unlike traditional macro-level ratios, this measure offers a more principled and less confounded way to characterize modality bias, disentangling it from unimodal capabilities and dataset artifacts. Further, by probing layer-wise predictions, we reveal the internal mechanism of oscillation: in ambiguous regions near the balance point, models vacillate between modalities across layers, explaining externally observed indecision. Together, these findings establish relative uncertainty and inherent preference as the two governing principles of modality following, offering both a quantitative framework and mechanistic insight into how MLLMs resolve conflicting information.
Monocular absolute depth estimation from endoscopy via domain-invariant feature learning and latent consistency
Venue: arXiv cs.CV 2025
Year: 2025

Monocular depth estimation (MDE) is a critical task to guide autonomous medical robots. However, obtaining absolute (metric) depth from an endoscopy camera in surgical scenes is difficult, which limits supervised learning of depth on real endoscopic images. Current image-level unsupervised domain adaptation methods translate synthetic images with known depth maps into the style of real endoscopic frames and train depth networks using these translated images with their corresponding depth maps. However a domain gap often remains between real and translated synthetic images. In this paper, we present a latent feature alignment method to improve absolute depth estimation by reducing this domain gap in the context of endoscopic videos of the central airway. Our methods are agnostic to the image translation process and focus on the depth estimation itself. Specifically, the depth network takes translated synthetic and real endoscopic frames as input and learns latent domain-invariant features via adversarial learning and directional feature consistency. The evaluation is conducted on endoscopic videos of central airway phantoms with manually aligned absolute depth maps. Compared to state-of-the-art MDE methods, our approach achieves superior performance on both absolute and relative depth metrics, and consistently improves results across various backbones and pretrained weights. Our code is available at https://github.com/MedICL-VU/MDE.
From Models to Operators: Rethinking Autoscaling Granularity for Large Generative Models
Venue: arXiv cs.DC 2025
Year: 2025

Serving large generative models such as LLMs and multi- modal transformers requires balancing user-facing SLOs (e.g., time-to-first-token, time-between-tokens) with provider goals of efficiency and cost reduction. Existing solutions rely on static provisioning or model-level autoscaling, both of which treat the model as a monolith. This coarse-grained resource management leads to degraded performance or significant resource underutilization due to poor adaptability to dynamic inference traffic that is common online. The root cause of this inefficiency lies in the internal structure of generative models: they are executed as graphs of interconnected operators. Through detailed characterization and systematic analysis, we find that operators are heterogeneous in their compute and memory footprints and exhibit diverse sensitivity to workload and resource factors such as batch size, sequence length, and traffic rate. This heterogeneity suggests that the operator, rather than the entire model, is the right granularity for scaling decisions. We propose an operator-level autoscaling framework, which allocates resources at finer (operator)-granularity, optimizing the scaling, batching, and placement based on individual operator profiles. Evaluated on production-scale traces, our approach preserves SLOs with up to 40% fewer GPUs and 35% less energy, or under fixed resources achieves 1.6x higher throughput with 5% less energy. These results show that the operator, rather than the model, is fundamentally a more effective unit for scaling large generative workloads.
How large is the error effect when summing or averaging nonlinear field normalization citation counts at the paper level?
Venue: arXiv cs.DL 2025
Year: 2025

Summing or averaging nonlinearly field-normalized citation counts is a common but methodologically problematic practice, as it violates mathematical principles. The issue originates from the nonlinear transformation, which disrupts the equal-interval property of the data. Such unequal data do not satisfy the necessary conditions for summation. In our study, we normalized citation counts of papers from all sample universities using six linear and nonlinear methods, and then computed the total and average scores for each university under each method. By benchmarking against raw citations and linear normalized scores, we explore how large the error effect is from summing or averaging the nonlinear field normalized citation counts. Our empirical results indicate that the error exists but is relatively small. We further found that the magnitude of the error is significantly influenced by whether the sample publications are homogeneous or heterogeneous. This study has significant implications for whether the results obtained through nonlinear methods on a single level can be directly summed or averaged when calculating the overall impact of a research unit.
Downlink Channel Estimation for mmWave Systems with Impulsive Interference
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we investigate a channel estimation problem in a downlink millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, which suffers from impulsive interference caused by hardware non-idealities or external disruptions. Specifically, impulsive interference presents a significant challenge to channel estimation due to its sporadic, unpredictable, and high-power nature. To tackle this issue, we develop a Bayesian channel estimation technique based on variational inference (VI) that leverages the sparsity of the mmWave channel in the angular domain and the intermittent nature of impulsive interference to minimize channel estimation errors. The proposed technique employs mean-field approximation to approximate posterior inference and integrates VI into the sparse Bayesian learning (SBL) framework. Simulation results demonstrate that the proposed technique outperforms baselines in terms of channel estimation accuracy.
Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition
Venue: arXiv cs.LG 2025
Year: 2025

We introduce a lightweight, real-time motion recognition system that enables synergic human-machine performance through wearable IMU sensor data, MiniRocket time-series classification, and responsive multimedia control. By mapping dancer-specific movement to sound through somatic memory and association, we propose an alternative approach to human-machine collaboration, one that preserves the expressive depth of the performing body while leveraging machine learning for attentive observation and responsiveness. We demonstrate that this human-centered design reliably supports high accuracy classification (<50 ms latency), offering a replicable framework to integrate dance-literate machines into creative, educational, and live performance contexts.
AI Credibility Signals Outrank Institutions and Engagement in Shaping News Perception on Social Media
Venue: arXiv cs.HC 2025
Year: 2025

AI-generated content is rapidly becoming a salient component of online information ecosystems, yet its influence on public trust and epistemic judgments remains poorly understood. We present a large-scale mixed-design experiment (N = 1,000) investigating how AI-generated credibility scores affect user perception of political news. Our results reveal that AI feedback significantly moderates partisan bias and institutional distrust, surpassing traditional engagement signals such as likes and shares. These findings demonstrate the persuasive power of generative AI and suggest a need for design strategies that balance epistemic influence with user autonomy.
Fuzzy Soft Set Theory based Expert System for the Risk Assessment in Breast Cancer Patients
Venue: arXiv cs.AI 2025
Year: 2025

Breast cancer remains one of the leading causes of mortality among women worldwide, with early diagnosis being critical for effective treatment and improved survival rates. However, timely detection continues to be a challenge due to the complex nature of the disease and variability in patient risk factors. This study presents a fuzzy soft set theory-based expert system designed to assess the risk of breast cancer in patients using measurable clinical and physiological parameters. The proposed system integrates Body Mass Index, Insulin Level, Leptin Level, Adiponectin Level, and age as input variables to estimate breast cancer risk through a set of fuzzy inference rules and soft set computations. These parameters can be obtained from routine blood analyses, enabling a non-invasive and accessible method for preliminary assessment. The dataset used for model development and validation was obtained from the UCI Machine Learning Repository. The proposed expert system aims to support healthcare professionals in identifying high-risk patients and determining the necessity of further diagnostic procedures such as biopsies.
Who's Who? LLM-assisted Software Traceability with Architecture Entity Recognition
Venue: arXiv cs.SE 2025
Year: 2025

Identifying architecturally relevant entities in textual artifacts is crucial for Traceability Link Recovery (TLR) between Software Architecture Documentation (SAD) and source code. While Software Architecture Models (SAMs) can bridge the semantic gap between these artifacts, their manual creation is time-consuming. Large Language Models (LLMs) offer new capabilities for extracting architectural entities from SAD and source code to construct SAMs automatically or establish direct trace links. This paper presents two LLM-based approaches: ExArch extracts component names as simple SAMs from SAD and source code to eliminate the need for manual SAM creation, while ArTEMiS identifies architectural entities in documentation and matches them with (manually or automatically generated) SAM entities. Our evaluation compares against state-of-the-art approaches SWATTR, TransArC and ArDoCode. TransArC achieves strong performance (F1: 0.87) but requires manually created SAMs; ExArch achieves comparable results (F1: 0.86) using only SAD and code. ArTEMiS is on par with the traditional heuristic-based SWATTR (F1: 0.81) and can successfully replace it when integrated with TransArC. The combination of ArTEMiS and ExArch outperforms ArDoCode, the best baseline without manual SAMs. Our results demonstrate that LLMs can effectively identify architectural entities in textual artifacts, enabling automated SAM generation and TLR, making architecture-code traceability more practical and accessible.
OpenCourier: an Open Protocol for Building a Decentralized Ecosystem of Community-owned Delivery Platforms
Venue: arXiv cs.HC 2025
Year: 2025

Although the platform gig economy has reshaped the landscape of work, its centralized operation by select actors has brought about challenges that impedes workers' well-being. We present the architecture and design of OpenCourier, an open protocol that defines communication patterns within a decentralized ecosystem of delivery platforms. Through this protocol, we aim to address three key challenges in the current economy: power imbalances between the platform and workers, information asymmetries caused by black-boxed algorithms and value misalignments in the infrastructure design process. With the OpenCourier protocol, we outline a blueprint for community-owned ecosystem of delivery platforms that centers worker agency, transparency, and bottom-up design.
KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image
Venue: arXiv cs.CV 2025
Year: 2025

Satellite image inpainting is a crucial task in remote sensing, where accurately restoring missing or occluded regions is essential for robust image analysis. In this paper, we propose KAO, a novel framework that utilizes Kernel-Adaptive Optimization within diffusion models for satellite image inpainting. KAO is specifically designed to address the challenges posed by very high-resolution (VHR) satellite datasets, such as DeepGlobe and the Massachusetts Roads Dataset. Unlike existing methods that rely on preconditioned models requiring extensive retraining or postconditioned models with significant computational overhead, KAO introduces a Latent Space Conditioning approach, optimizing a compact latent space to achieve efficient and accurate inpainting. Furthermore, we incorporate Explicit Propagation into the diffusion process, facilitating forward-backward fusion, which improves the stability and precision of the method. Experimental results demonstrate that KAO sets a new benchmark for VHR satellite image restoration, providing a scalable, high-performance solution that balances the efficiency of preconditioned models with the flexibility of postconditioned models.
MVAFormer: RGB-based Multi-View Spatio-Temporal Action Recognition with Transformer
Venue: arXiv cs.CV 2025
Year: 2025

Multi-view action recognition aims to recognize human actions using multiple camera views and deals with occlusion caused by obstacles or crowds. In this task, cooperation among views, which generates a joint representation by combining multiple views, is vital. Previous studies have explored promising cooperation methods for improving performance. However, since their methods focus only on the task setting of recognizing a single action from an entire video, they are not applicable to the recently popular spatio-temporal action recognition~(STAR) setting, in which each person's action is recognized sequentially. To address this problem, this paper proposes a multi-view action recognition method for the STAR setting, called MVAFormer. In MVAFormer, we introduce a novel transformer-based cooperation module among views. In contrast to previous studies, which utilize embedding vectors with lost spatial information, our module utilizes the feature map for effective cooperation in the STAR setting, which preserves the spatial information. Furthermore, in our module, we divide the self-attention for the same and different views to model the relationship between multiple views effectively. The results of experiments using a newly collected dataset demonstrate that MVAFormer outperforms the comparison baselines by approximately $4.4$ points on the F-measure.
Wireless Video Semantic Communication with Decoupled Diffusion Multi-frame Compensation
Venue: arXiv cs.MM 2025
Year: 2025

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework with decoupled diffusion multi-frame compensation (DDMFC), abbreviated as WVSC-D, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC-D first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, DDMFC is proposed to generate compensated current semantic frame by a two-stage conditional diffusion process. With both the reference frame transmission and DDMFC frame compensation, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC-D over other DL-based methods e.g. DVSC about 1.8 dB in terms of PSNR.
BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring
Venue: arXiv cs.LG 2025
Year: 2025

As the global burden of Alzheimer's disease (AD) continues to grow, early and accurate detection has become increasingly critical, especially in regions with limited access to advanced diagnostic tools. We propose BRAINS (Biomedical Retrieval-Augmented Intelligence for Neurodegeneration Screening) to address this challenge. This novel system harnesses the powerful reasoning capabilities of Large Language Models (LLMs) for Alzheimer's detection and monitoring. BRAINS features a dual-module architecture: a cognitive diagnostic module and a case-retrieval module. The Diagnostic Module utilizes LLMs fine-tuned on cognitive and neuroimaging datasets -- including MMSE, CDR scores, and brain volume metrics -- to perform structured assessments of Alzheimer's risk. Meanwhile, the Case Retrieval Module encodes patient profiles into latent representations and retrieves similar cases from a curated knowledge base. These auxiliary cases are fused with the input profile via a Case Fusion Layer to enhance contextual understanding. The combined representation is then processed with clinical prompts for inference. Evaluations on real-world datasets demonstrate BRAINS effectiveness in classifying disease severity and identifying early signs of cognitive decline. This system not only shows strong potential as an assistive tool for scalable, explainable, and early-stage Alzheimer's disease detection, but also offers hope for future applications in the field.
Lightweight Latency Prediction Scheme for Edge Applications: A Rational Modelling Approach
Venue: arXiv cs.NI 2025
Year: 2025

Accurately predicting end-to-end network latency is essential for enabling reliable task offloading in real-time edge computing applications. This paper introduces a lightweight latency prediction scheme based on rational modelling that uses features such as frame size, arrival rate, and link utilization, eliminating the need for intrusive active probing. The model achieves state-of-the-art prediction accuracy through extensive experiments and 5-fold cross-validation (MAE = 0.0115, R$^2$ = 0.9847) with competitive inference time, offering a substantial trade-off between precision and efficiency compared to traditional regressors and neural networks.
Rawlsian many-to-one matching with non-linear utility
Venue: arXiv cs.LG 2025
Year: 2025

We study a many-to-one matching problem, such as the college admission problem, where each college can admit multiple students. Unlike classical models, colleges evaluate sets of students through non-linear utility functions that capture diversity between them. In this setting, we show that classical stable matchings may fail to exist. To address this, we propose alternative solution concepts based on Rawlsian fairness, aiming to maximize the minimum utility across colleges. We design both deterministic and stochastic algorithms that iteratively improve the outcome of the worst-off college, offering a practical approach to fair allocation when stability cannot be guaranteed.