OpenRaiser/Intern-Atlas huggingface dataset:
A Unit-Cell Shape Optimization Approach for Maximizing Heat Transfer in Periodic Fin Arrays at Constant Solid Temperature
Venue: arXiv cs.CE 2025
Year: 2025

Periodic fin structures are often employed to enhance heat transfer in compact cooling solutions and heat exchangers. Adjoint-based optimization methods are able to further increase the heat transfer by optimizing the fin geometry. However, obtaining optimal geometries remains challenging in general because of the high computational cost of full array simulations. In this paper, a unit cell optimization approach is presented that starts from recently developed macro-scale models for isothermal solid structures. The models exploit the periodicity of the problem to reduce the computational cost of evaluating the array heat transfer to that of a single periodic unit cell. By combining these models with a geometrically-constrained free-shape optimization approach, optimal fin geometries are obtained for the periodic fin array that maintain a minimal fin distance. Moreover, using an augmented Lagrangian approach, also the average pressure gradient and barycenter of the fin can be fixed. On a fictitious use-case, heat transfer increases up to 104 \% are obtained. When also flow rate is constrained in addition to maintain a high effectiveness, only up to 8 \% heat transfer increase is observed. Finally, the errors of the unit-cell optimization approach are investigated, indicating that with a good choice of cost functional formulation, errors of the approach as low as 1-2 \% can be obtained for the periodically developed part of the array. Finally, the entrance effect to the heat transfer is found to be non-negligible with a contribution of 10-15 \% for the considered fin array. This advocates for further research to extend the unit-cell models towards improved modeling of entrance effects.
Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in convolutional neural networks (CNNs) and transformer-based methods have improved anomaly detection and localization, but challenges persist in precisely localizing small anomalies. While CNNs face limitations in capturing long-range dependencies, transformer architectures often suffer from substantial computational overheads. We introduce a state space model (SSM)-based Pyramidal Scanning Strategy (PSS) for multi-class anomaly detection and localization--a novel approach designed to address the challenge of small anomaly localization. Our method captures fine-grained details at multiple scales by integrating the PSS with a pre-trained encoder for multi-scale feature extraction and a feature-level synthetic anomaly generator. An improvement of $+1\%$ AP for multi-class anomaly localization and a +$1\%$ increase in AU-PRO on MVTec benchmark demonstrate our method's superiority in precise anomaly localization across diverse industrial scenarios. The code is available at https://github.com/iqbalmlpuniud/Pyramid Mamba.
LLMSched: Uncertainty-Aware Workload Scheduling for Compound LLM Applications
Venue: arXiv cs.DC 2025
Year: 2025

Developing compound Large Language Model (LLM) applications is becoming an increasingly prevalent approach to solving real-world problems. In these applications, an LLM collaborates with various external modules, including APIs and even other LLMs, to realize complex intelligent services. However, we reveal that the intrinsic duration and structural uncertainty in compound LLM applications pose great challenges for LLM service providers in serving and scheduling them efficiently. In this paper, we propose LLMSched, an uncertainty-aware scheduling framework for emerging compound LLM applications. In LLMSched, we first design a novel DAG-based model to describe the uncertain compound LLM applications. Then, we adopt the Bayesian network to comprehensively profile compound LLM applications and identify uncertainty-reducing stages, along with an entropy-based mechanism to quantify their uncertainty reduction. Combining an uncertainty reduction strategy and a job completion time (JCT)-efficient scheme, we further propose an efficient scheduler to reduce the average JCT. Evaluation of both simulation and testbed experiments on various representative compound LLM applications shows that compared to existing state-of-the-art scheduling schemes, LLMSched can reduce the average JCT by 14~79%.
NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System
Venue: arXiv cs.AR 2025
Year: 2025

Linear-response time-dependent Density Functional Theory (LR-TDDFT) is a widely used method for accurately predicting the excited-state properties of physical systems. Previous works have attempted to accelerate LR-TDDFT using heterogeneous systems such as GPUs, FPGAs, and the Sunway architecture. However, a major drawback of these approaches is the constant data movement between host memory and the memory of the heterogeneous systems, which results in substantial \textit{data movement overhead}. Moreover, these works focus primarily on optimizing the compute-intensive portions of LR-TDDFT, despite the fact that the calculation steps are fundamentally \textit{memory-bound}. To address these challenges, we propose NDFT, a \underline{N}ear-\underline{D}ata Density \underline{F}unctional \underline{T}heory framework. Specifically, we design a novel task partitioning and scheduling mechanism to offload each part of LR-TDDFT to the most suitable computing units within a CPU-NDP system. Additionally, we implement a hardware/software co-optimization of a critical kernel in LR-TDDFT to further enhance performance on the CPU-NDP system. Our results show that NDFT achieves performance improvements of 5.2x and 2.5x over CPU and GPU baselines, respectively, on a large physical system.
Managing Information Overload in Large-Scale Distributed Mixed-Reality Meetings
Venue: arXiv cs.HC 2025
Year: 2025

Large-scale distributed mixed-reality meetings involve many people and their audiovisual representations. These collaborative environments can introduce challenges such as sensory overload, cognitive strain, and social fatigue. In this paper, we discuss how the unique adaptability of Mixed Reality can be leveraged to weaken these stressors by managing information overload.
Dynamic Importance in Diffusion U-Net for Enhanced Image Synthesis
Venue: arXiv cs.CV 2025
Year: 2025

Traditional diffusion models typically employ a U-Net architecture. Previous studies have unveiled the roles of attention blocks in the U-Net. However, they overlook the dynamic evolution of their importance during the inference process, which hinders their further exploitation to improve image applications. In this study, we first theoretically proved that, re-weighting the outputs of the Transformer blocks within the U-Net is a "free lunch" for improving the signal-to-noise ratio during the sampling process. Next, we proposed Importance Probe to uncover and quantify the dynamic shifts in importance of the Transformer blocks throughout the denoising process. Finally, we design an adaptive importance-based re-weighting schedule tailored to specific image generation and editing tasks. Experimental results demonstrate that, our approach significantly improves the efficiency of the inference process, and enhances the aesthetic quality of the samples with identity consistency. Our method can be seamlessly integrated into any U-Net-based architecture. Code: https://github.com/Hytidel/UNetReweighting
Neutralizing the Narrative: AI-Powered Debiasing of Online News Articles
Venue: arXiv cs.CL 2025
Year: 2025

Bias in news reporting significantly impacts public perception, particularly regarding crime, politics, and societal issues. Traditional bias detection methods, predominantly reliant on human moderation, suffer from subjective interpretations and scalability constraints. Here, we introduce an AI-driven framework leveraging advanced large language models (LLMs), specifically GPT-4o, GPT-4o Mini, Gemini Pro, Gemini Flash, Llama 8B, and Llama 3B, to systematically identify and mitigate biases in news articles. To this end, we collect an extensive dataset consisting of over 30,000 crime-related articles from five politically diverse news sources spanning a decade (2013-2023). Our approach employs a two-stage methodology: (1) bias detection, where each LLM scores and justifies biased content at the paragraph level, validated through human evaluation for ground truth establishment, and (2) iterative debiasing using GPT-4o Mini, verified by both automated reassessment and human reviewers. Empirical results indicate GPT-4o Mini's superior accuracy in bias detection and effectiveness in debiasing. Furthermore, our analysis reveals temporal and geographical variations in media bias correlating with socio-political dynamics and real-world events. This study contributes to scalable computational methodologies for bias mitigation, promoting fairness and accountability in news reporting.
PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector
Venue: arXiv cs.CV 2025
Year: 2025

3D object detection is crucial for autonomous driving, leveraging both LiDAR point clouds for precise depth information and camera images for rich semantic information. Therefore, the multi-modal methods that combine both modalities offer more robust detection results. However, efficiently fusing LiDAR points and images remains challenging due to the domain gaps. In addition, the performance of many models is limited by the amount of high quality labeled data, which is expensive to create. The recent advances in foundation models, which use large-scale pre-training on different modalities, enable better multi-modal fusion. Combining the prompt engineering techniques for efficient training, we propose the Prompted Foundational 3D Detector (PF3Det), which integrates foundation model encoders and soft prompts to enhance LiDAR-camera feature fusion. PF3Det achieves the state-of-the-art results under limited training data, improving NDS by 1.19% and mAP by 2.42% on the nuScenes dataset, demonstrating its efficiency in 3D detection.
An overview of the efficiency and censorship-resistance guarantees of widely-used consensus protocols
Venue: arXiv cs.DC 2025
Year: 2025

Censorship resistance with short-term inclusion guarantees is an important feature of decentralized systems, missing from many state-of-the-art and even deployed consensus protocols. In leader-based protocols the leader arbitrarily selects the transactions to be included in the new block, and so does a block builder in protocols such as Bitcoin and Ethereum. In a different line of work, since the redundancy of consensus for implementing distributed payments was formally proven, consensusless protocols have been described in theory and deployed in the real world. This has resulted in blockchains and payment systems that are more efficient, and at the same time avoid the centralized role of a leader or block builder. In this report we review existing consensus and consensusless protocols with regard to their censorship-resistance, efficiency, and other properties. Moreover, we present an approach for new constructions with these properties in mind, building on existing leader-based protocols.
Robust Human Registration with Body Part Segmentation on Noisy Point Clouds
Venue: arXiv cs.CV 2025
Year: 2025

Registering human meshes to 3D point clouds is essential for applications such as augmented reality and human-robot interaction but often yields imprecise results due to noise and background clutter in real-world data. We introduce a hybrid approach that incorporates body-part segmentation into the mesh fitting process, enhancing both human pose estimation and segmentation accuracy. Our method first assigns body part labels to individual points, which then guide a two-step SMPL-X fitting: initial pose and orientation estimation using body part centroids, followed by global refinement of the point cloud alignment. Additionally, we demonstrate that the fitted human mesh can refine body part labels, leading to improved segmentation. Evaluations on the cluttered and noisy real-world datasets InterCap, EgoBody, and BEHAVE show that our approach significantly outperforms prior methods in both pose estimation and segmentation accuracy. Code and results are available on our project website: https://segfit.github.io
Towards deployment-centric multimodal AI beyond vision and language
Venue: arXiv cs.AI 2025
Year: 2025

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact.
Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design
Venue: arXiv cs.GT 2025
Year: 2025

We study the problem of position allocation in job marketplaces, where the platform determines the ranking of the jobs for each seeker. The design of ranking mechanisms is critical to marketplace efficiency, as it influences both short-term revenue from promoted job placements and long-term health through sustained seeker engagement. Our analysis focuses on the tradeoff between revenue and relevance, as well as the innovations in job auction design. We demonstrated two ways to improve relevance with minimal impact on revenue: incorporating the seekers preferences and applying position-aware auctions.
Bonsai: Interpretable Tree-Adaptive Grounded Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

To develop general-purpose collaborative agents, humans need reliable AI systems that can (1) adapt to new domains and (2) transparently reason with uncertainty to allow for verification and correction. Black-box models demonstrate powerful data processing abilities but do not satisfy these criteria due to their opaqueness, domain specificity, and lack of uncertainty awareness. We introduce Bonsai, a compositional and probabilistic reasoning system that generates adaptable inference trees by retrieving relevant grounding evidence and using it to compute likelihoods of sub-claims derived from broader natural language inferences. Bonsai's reasoning power is tunable at test-time via evidence scaling and it demonstrates reliable handling of varied domains including transcripts, photographs, videos, audio, and databases. Question-answering and human alignment experiments demonstrate that Bonsai matches the performance of domain-specific black-box methods while generating interpretable, grounded, and uncertainty-aware reasoning traces.
Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving
Venue: arXiv cs.DC 2025
Year: 2025

Large language models have been widely deployed in various applications, encompassing both interactive online tasks and batched offline tasks. Given the burstiness and latency sensitivity of online tasks, over-provisioning resources is common practice. This allows for the integration of latency-insensitive offline tasks during periods of low online load, enhancing resource utilization. However, strategically serving online and offline tasks through a preemption mechanism fails to fully leverage the flexibility of offline tasks and suffers from KV cache recomputation and irregular workloads. In this paper, we introduce Echo, a collaborative online-offline task serving system, including a scheduler, a KV cache manager, and estimation toolkits. The scheduler and KV cache manager work tightly to maximize the throughput of offline tasks, while the estimator further predicts execution time to ensure online task SLOs. The scheduler leverages the batch information of last iteration to reduce the search space for finding the optimal schedule. The KV cache manager sets the priority of the KV cache based on the type of tasks and the opportunity of prefix sharing to reduce the recomputation. Finally, the estimation toolkits predict the execution time, future memory consumption, and the throughput of offline tasks to guide the scheduler, KV cache manager, and the system deployer. Evaluation based on real-world workloads demonstrates that Echo can increase offline task throughput by up to $3.3\times$, while satisfying online task SLOs.
LLM & HPC:Benchmarking DeepSeek's Performance in High-Performance Computing Tasks
Venue: arXiv cs.DC 2025
Year: 2025

Large Language Models (LLMs), such as GPT-4 and DeepSeek, have been applied to a wide range of domains in software engineering. However, their potential in the context of High-Performance Computing (HPC) much remains to be explored. This paper evaluates how well DeepSeek, a recent LLM, performs in generating a set of HPC benchmark codes: a conjugate gradient solver, the parallel heat equation, parallel matrix multiplication, DGEMM, and the STREAM triad operation. We analyze DeepSeek's code generation capabilities for traditional HPC languages like Cpp, Fortran, Julia and Python. The evaluation includes testing for code correctness, performance, and scaling across different configurations and matrix sizes. We also provide a detailed comparison between DeepSeek and another widely used tool: GPT-4. Our results demonstrate that while DeepSeek generates functional code for HPC tasks, it lags behind GPT-4, in terms of scalability and execution efficiency of the generated code.
Self-Learning-Based Optimization for Free-form Pipe Routing in Aeroengine with Dynamic Design Environment
Venue: arXiv cs.LG 2025
Year: 2025

Pipe routing is a highly complex, time-consuming, and no-deterministic polynomial-time hard (NP-hard) problem in aeroengine design. Despite extensive research efforts in optimizing constant-curvature pipe routing, the growing demand for free-form pipes poses new challenges. Dynamic design environments and fuzzy layout rules further impact the optimization performance and efficiency. To tackle these challenges, this study proposes a self-learning-based method (SLPR) for optimizing free-form pipe routing in aeroengines. The SLPR is based on the proximal policy optimization (PPO) algorithm and integrates a unified rule modeling framework for efficient obstacle detection and fuzzy rule modeling in continuous space. Additionally, a potential energy table is constructed to enable rapid queries of layout tendencies and interference. The agent within SLPR iteratively refines pipe routing and accumulates the design knowledge through interaction with the environment. Once the design environment shifts, the agent can swiftly adapt by fine-tuning network parameters. Comparative tests reveal that SLPR ensures smooth pipe routing through cubic non-uniform B-spline (NURBS) curves, avoiding redundant pipe segments found in constant-curvature pipe routing. Results in both static and dynamic design environments demonstrate that SLPR outperforms three representative baselines in terms of the pipe length reduction, the adherence to layout rules, the path complexity, and the computational efficiency. Furthermore, tests in dynamic environments indicate that SLPR eliminates labor-intensive searches from scratch and even yields superior solutions compared to the retrained model. These results highlight the practical value of SLPR for real-world pipe routing, meeting lightweight, precision, and sustainability requirements of the modern aeroengine design.
CLCR: Contrastive Learning-based Constraint Reordering for Efficient MILP Solving
Venue: arXiv cs.LG 2025
Year: 2025

Constraint ordering plays a critical role in the efficiency of Mixed-Integer Linear Programming (MILP) solvers, particularly for large-scale problems where poorly ordered constraints trigger increased LP iterations and suboptimal search trajectories. This paper introduces CLCR (Contrastive Learning-based Constraint Reordering), a novel framework that systematically optimizes constraint ordering to accelerate MILP solving. CLCR first clusters constraints based on their structural patterns and then employs contrastive learning with a pointer network to optimize their sequence, preserving problem equivalence while improving solver efficiency. Experiments on benchmarks show CLCR reduces solving time by 30% and LP iterations by 25% on average, without sacrificing solution accuracy. This work demonstrates the potential of data-driven constraint ordering to enhance optimization models, offering a new paradigm for bridging mathematical programming with machine learning.
RLDBF: Enhancing LLMs Via Reinforcement Learning With DataBase FeedBack
Venue: arXiv cs.LG 2025
Year: 2025

While current large language models (LLMs) demonstrate remarkable linguistic capabilities through training on massive unstructured text corpora, they remain inadequate in leveraging structured scientific data (e.g., chemical molecular properties in databases) that encapsulate centuries of accumulated scientific expertise. These structured datasets hold strategic significance for advancing AI for Science yet current approaches merely treat them as auxiliary supplements to unstructured text. This study pioneers a systematic investigation into enhancing LLMs with structured scientific data, using chemical molecular science as a testbed. We investigate the impact of incorporating molecular property data on LLM across distinct training phases, including continual pre-training, supervised fine-tuning, and reinforcement learning. Notably, to address the inherent limitation of numerical insensitivity in large models, we propose an innovative methodology termed "Reinforcement Learning with Database Feedback" (RLDBF). Experimental evaluations demonstrate the efficacy of the proposed approach, with the model exhibiting remarkable generalization capabilities on previously unseen data and other chemical tasks. The results substantiate the potential of our method in advancing the field of structured scientific data processing within LLMs.