OpenRaiser/Intern-Atlas huggingface dataset:
Scalability Optimization in Cloud-Based AI Inference Services: Strategies for Real-Time Load Balancing and Automated Scaling
Venue: arXiv cs.DC 2025
Year: 2025

The rapid expansion of AI inference services in the cloud necessitates a robust scalability solution to manage dynamic workloads and maintain high performance. This study proposes a comprehensive scalability optimization framework for cloud AI inference services, focusing on real-time load balancing and autoscaling strategies. The proposed model is a hybrid approach that combines reinforcement learning for adaptive load distribution and deep neural networks for accurate demand forecasting. This multi-layered approach enables the system to anticipate workload fluctuations and proactively adjust resources, ensuring maximum resource utilisation and minimising latency. Furthermore, the incorporation of a decentralised decision-making process within the model serves to enhance fault tolerance and reduce response time in scaling operations. Experimental results demonstrate that the proposed model enhances load balancing efficiency by 35\ and reduces response delay by 28\, thereby exhibiting a substantial optimization effect in comparison with conventional scalability solutions.
M-TabNet: A Multi-Encoder Transformer Model for Predicting Neonatal Birth Weight from Multimodal Data
Venue: arXiv cs.LG 2025
Year: 2025

Birth weight (BW) is a key indicator of neonatal health, with low birth weight (LBW) linked to increased mortality and morbidity. Early prediction of BW enables timely interventions; however, current methods like ultrasonography have limitations, including reduced accuracy before 20 weeks and operator dependent variability. Existing models often neglect nutritional and genetic influences, focusing mainly on physiological and lifestyle factors. This study presents an attention-based transformer model with a multi-encoder architecture for early (less than 12 weeks of gestation) BW prediction. Our model effectively integrates diverse maternal data such as physiological, lifestyle, nutritional, and genetic, addressing limitations seen in prior attention-based models such as TabNet. The model achieves a Mean Absolute Error (MAE) of 122 grams and an R-squared value of 0.94, demonstrating high predictive accuracy and interoperability with our in-house private dataset. Independent validation confirms generalizability (MAE: 105 grams, R-squared: 0.95) with the IEEE children dataset. To enhance clinical utility, predicted BW is classified into low and normal categories, achieving a sensitivity of 97.55% and a specificity of 94.48%, facilitating early risk stratification. Model interpretability is reinforced through feature importance and SHAP analyses, highlighting significant influences of maternal age, tobacco exposure, and vitamin B12 status, with genetic factors playing a secondary role. Our results emphasize the potential of advanced deep-learning models to improve early BW prediction, offering clinicians a robust, interpretable, and personalized tool for identifying pregnancies at risk and optimizing neonatal outcomes.
PolicyEvol-Agent: Evolving Policy via Environment Perception and Self-Awareness with Theory of Mind
Venue: arXiv cs.AI 2025
Year: 2025

Multi-agents has exhibited significant intelligence in real-word simulations with Large language models (LLMs) due to the capabilities of social cognition and knowledge retrieval. However, existing research on agents equipped with effective cognition chains including reasoning, planning, decision-making and reflecting remains limited, especially in the dynamically interactive scenarios. In addition, unlike human, prompt-based responses face challenges in psychological state perception and empirical calibration during uncertain gaming process, which can inevitably lead to cognition bias. In light of above, we introduce PolicyEvol-Agent, a comprehensive LLM-empowered framework characterized by systematically acquiring intentions of others and adaptively optimizing irrational strategies for continual enhancement. Specifically, PolicyEvol-Agent first obtains reflective expertise patterns and then integrates a range of cognitive operations with Theory of Mind alongside internal and external perspectives. Simulation results, outperforming RL-based models and agent-based methods, demonstrate the superiority of PolicyEvol-Agent for final gaming victory. Moreover, the policy evolution mechanism reveals the effectiveness of dynamic guideline adjustments in both automatic and human evaluation.
HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning
Venue: arXiv cs.LG 2025
Year: 2025

While test-time fine-tuning is beneficial in few-shot learning, the need for multiple backpropagation steps can be prohibitively expensive in real-time or low-resource scenarios. To address this limitation, we propose an approach that emulates gradient descent without computing gradients, enabling efficient test-time adaptation. Specifically, we formulate gradient descent as an Euler discretization of an ordinary differential equation (ODE) and train an auxiliary network to predict the task-conditional drift using only the few-shot support set. The adaptation then reduces to a simple numerical integration (e.g., via the Euler method), which requires only a few forward passes of the auxiliary network -- no gradients or forward passes of the target model are needed. In experiments on cross-domain few-shot classification using the Meta-Dataset and CDFSL benchmarks, our method significantly improves out-of-domain performance over the non-fine-tuned baseline while incurring only 6\% of the memory cost and 0.02\% of the computation time of standard fine-tuning, thus establishing a practical middle ground between direct transfer and fully fine-tuned approaches.
Reliable Classification with Conformal Learning and Interval-Type 2 Fuzzy Sets
Venue: arXiv cs.AI 2025
Year: 2025

Classical machine learning classifiers tend to be overconfident can be unreliable outside of the laboratory benchmarks. Properly assessing the reliability of the output of the model per sample is instrumental for real-life scenarios where these systems are deployed. Because of this, different techniques have been employed to properly quantify the quality of prediction for a given model. These are most commonly Bayesian statistics and, more recently, conformal learning. Given a calibration set, conformal learning can produce outputs that are guaranteed to cover the target class with a desired significance level, and are more reliable than the standard confidence intervals used by Bayesian methods. In this work, we propose to use conformal learning with fuzzy rule-based systems in classification and show some metrics of their performance. Then, we discuss how the use of type 2 fuzzy sets can improve the quality of the output of the system compared to both fuzzy and crisp rules. Finally, we also discuss how the fine-tuning of the system can be adapted to improve the quality of the conformal prediction.
ICGM-FRAX: Iterative Cross Graph Matching for Hip Fracture Risk Assessment using Dual-energy X-ray Absorptiometry Images
Venue: arXiv cs.CV 2025
Year: 2025

Hip fractures represent a major health concern, particularly among the elderly, often leading decreased mobility and increased mortality. Early and accurate detection of at risk individuals is crucial for effective intervention. In this study, we propose Iterative Cross Graph Matching for Hip Fracture Risk Assessment (ICGM-FRAX), a novel approach for predicting hip fractures using Dual-energy X-ray Absorptiometry (DXA) images. ICGM-FRAX involves iteratively comparing a test (subject) graph with multiple template graphs representing the characteristics of hip fracture subjects to assess the similarity and accurately to predict hip fracture risk. These graphs are obtained as follows. The DXA images are separated into multiple regions of interest (RoIs), such as the femoral head, shaft, and lesser trochanter. Radiomic features are then calculated for each RoI, with the central coordinates used as nodes in a graph. The connectivity between nodes is established according to the Euclidean distance between these coordinates. This process transforms each DXA image into a graph, where each node represents a RoI, and edges derived by the centroids of RoIs capture the spatial relationships between them. If the test graph closely matches a set of template graphs representing subjects with incident hip fractures, it is classified as indicating high hip fracture risk. We evaluated our method using 547 subjects from the UK Biobank dataset, and experimental results show that ICGM-FRAX achieved a sensitivity of 0.9869, demonstrating high accuracy in predicting hip fractures.
Capacity on BMS Channels via Code Symmetry and Nesting
Venue: arXiv cs.IT 2025
Year: 2025

The past decade has seen notable advances in our understanding of structured error-correcting codes, particularly binary Reed--Muller (RM) codes. While initial breakthroughs were for erasure channels based on symmetry, extending these results to the binary symmetric channel (BSC) and other binary memoryless symmetric (BMS) channels required new tools and conditions. Recent work uses nesting to obtain multiple weakly correlated "looks" that imply capacity-achieving performance under bit-MAP and block-MAP decoding. This paper revisits and extends past approaches, aiming to simplify proofs, unify insights, and remove unnecessary conditions. By leveraging powerful results from the analysis of boolean functions, we derive recursive bounds using two or three looks at each stage. This gives bounds on the bit error probability that decay exponentially in the number of stages. For the BSC, we incorporate level-k inequalities and hypercontractive techniques to achieve the faster decay rate required for vanishing block error probability. The results are presented in a semitutorial style, providing both theoretical insights and practical implications for future research on structured codes.
Bare Minimum Mitigations for Autonomous AI Development
Venue: arXiv cs.CY 2025
Year: 2025

Artificial intelligence (AI) is advancing rapidly, with the potential for significantly automating AI research and development itself in the near future. In 2024, international scientists, including Turing Award recipients, warned of risks from autonomous AI research and development (R&D), suggesting a red line such that no AI system should be able to improve itself or other AI systems without explicit human approval and assistance. However, the criteria for meaningful human approval remain unclear, and there is limited analysis on the specific risks of autonomous AI R&D, how they arise, and how to mitigate them. In this brief paper, we outline how these risks may emerge and propose four minimum safeguard recommendations applicable when AI agents significantly automate or accelerate AI development.
Iris: A Next Generation Digital Pathology Rendering Engine
Venue: arXiv cs.GR 2025
Year: 2025

Digital pathology is a tool of rapidly evolving importance within the discipline of pathology. Whole slide imaging promises numerous advantages; however, adoption is limited by challenges in ease of use and speed of high-quality image rendering relative to the simplicity and visual quality of glass slides. We introduce Iris, a new high-performance digital pathology rendering system. Specifically, we outline and detail the performance metrics of Iris Core, the core rendering engine technology. Iris Core comprises machine code modules written from the ground up in C++ and using Vulkan, a low-level and low-overhead cross-platform graphical processing unit application program interface, and our novel rapid tile buffering algorithms. We provide a detailed explanation of Iris Core's system architecture, including the stateless isolation of core processes, interprocess communication paradigms, and explicit synchronization paradigms that provide powerful control over the graphical processing unit. Iris Core achieves slide rendering at the sustained maximum frame rate on all tested platforms and buffers an entire new slide field of, view without overlapping pixels, in 10 ms with enhanced detail in 30 ms. It is able to buffer and compute high-fidelity reduction-enhancements for viewing low-power cytology with increased visual quality at a rate of 100-160 us per slide tile, and with a cumulative median buffering rate of 1.36 GB of decompressed image data per second. This buffering rate allows for an entirely new field of view to be fully buffered and rendered in less than a single monitor refresh on a standard display, and high detail features within 2-3 monitor refresh frames. These metrics far exceed previously published specifications, beyond an order of magnitude in some contexts. The system shows no slowing with high use loads, but rather increases performance due to cache mechanisms.
Valkyrie: A Response Framework to Augment Runtime Detection of Time-Progressive Attacks
Venue: arXiv cs.CR 2025
Year: 2025

A popular approach to detect cyberattacks is to monitor systems in real-time to identify malicious activities as they occur. While these solutions aim to detect threats early, minimizing damage, they suffer from a significant challenge due to the presence of false positives. False positives have a detrimental impact on computer systems, which can lead to interruptions of legitimate operations and reduced productivity. Most contemporary works tend to use advanced Machine Learning and AI solutions to address this challenge. Unfortunately, false positives can, at best, be reduced but not eliminated. In this paper, we propose an alternate approach that focuses on reducing the impact of false positives rather than eliminating them. We introduce Valkyrie, a framework that can enhance any existing runtime detector with a post-detection response. Valkyrie is designed for time-progressive attacks, such as micro-architectural attacks, rowhammer, ransomware, and cryptominers, that achieve their objectives incrementally using system resources. As soon as an attack is detected, Valkyrie limits the allocated computing resources, throttling the attack, until the detector's confidence is sufficiently high to warrant a more decisive action. For a false positive, limiting the system resources only results in a small increase in execution time. On average, the slowdown incurred due to false positives is less than 1% for single-threaded programs and 6.7% for multi-threaded programs. On the other hand, attacks like rowhammer are prevented, while the potency of micro-architectural attacks, ransomware, and cryptominers is greatly reduced.
Learning Adaptive Parallel Reasoning with Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Scaling inference-time computation has substantially improved the reasoning capabilities of language models. However, existing methods have significant limitations: serialized chain-of-thought approaches generate overly long outputs, leading to increased latency and exhausted context windows, while parallel methods such as self-consistency suffer from insufficient coordination, resulting in redundant computations and limited performance gains. To address these shortcomings, we propose Adaptive Parallel Reasoning (APR), a novel reasoning framework that enables language models to orchestrate both serialized and parallel computations end-to-end. APR generalizes existing reasoning methods by enabling adaptive multi-threaded inference using spawn() and join() operations. A key innovation is our end-to-end reinforcement learning strategy, optimizing both parent and child inference threads to enhance task success rate without requiring predefined reasoning structures. Experiments on the Countdown reasoning task demonstrate significant benefits of APR: (1) higher performance within the same context window (83.4% vs. 60.0% at 4k context); (2) superior scalability with increased computation (80.1% vs. 66.6% at 20k total tokens); (3) improved accuracy at equivalent latency (75.2% vs. 57.3% at approximately 5,000ms). APR represents a step towards enabling language models to autonomously optimize their reasoning processes through adaptive allocation of computation.
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System
Venue: arXiv cs.IR 2025
Year: 2025

Conversational recommender systems (CRS) typically require extensive domain-specific conversational datasets, yet high costs, privacy concerns, and data-collection challenges severely limit their availability. Although Large Language Models (LLMs) demonstrate strong zero-shot recommendation capabilities, practical applications often favor smaller, internally managed recommender models due to scalability, interpretability, and data privacy constraints, especially in sensitive or rapidly evolving domains. However, training these smaller models effectively still demands substantial domain-specific conversational data, which remains challenging to obtain. To address these limitations, we propose an active data augmentation framework that synthesizes conversational training data by leveraging black-box LLMs guided by active learning techniques. Specifically, our method utilizes publicly available non-conversational domain data, including item metadata, user reviews, and collaborative signals, as seed inputs. By employing active learning strategies to select the most informative seed samples, our approach efficiently guides LLMs to generate synthetic, semantically coherent conversational interactions tailored explicitly to the target domain. Extensive experiments validate that conversational data generated by our proposed framework significantly improves the performance of LLM-based CRS models, effectively addressing the challenges of building CRS in no- or low-resource scenarios.
From Overload to Insight: Scaffolding Creative Ideation through Structuring Inspiration
Venue: arXiv cs.HC 2025
Year: 2025

Creative ideation relies on exploring diverse stimuli, but the overwhelming abundance of information often makes it difficult to identify valuable insights or reach the `aha' moment. Traditional methods for accessing design stimuli lack organization and fail to support users in discovering promising opportunities within large idea spaces. In this position paper, we explore how AI can be leveraged to structure, organize, and surface relevant stimuli, guiding users in both exploring idea spaces and mapping insights back to their design challenges.
Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions
Venue: arXiv cs.LG 2025
Year: 2025

This study proposes an algorithm for detecting suspicious behaviors in large payment flows based on deep generative models. By combining Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), the algorithm is designed to detect abnormal behaviors in financial transactions. First, the GAN is used to generate simulated data that approximates normal payment flows. The discriminator identifies anomalous patterns in transactions, enabling the detection of potential fraud and money laundering behaviors. Second, a VAE is introduced to model the latent distribution of payment flows, ensuring that the generated data more closely resembles real transaction features, thus improving the model's detection accuracy. The method optimizes the generative capabilities of both GAN and VAE, ensuring that the model can effectively capture suspicious behaviors even in sparse data conditions. Experimental results show that the proposed method significantly outperforms traditional machine learning algorithms and other deep learning models across various evaluation metrics, especially in detecting rare fraudulent behaviors. Furthermore, this study provides a detailed comparison of performance in recognizing different transaction patterns (such as normal, money laundering, and fraud) in large payment flows, validating the advantages of generative models in handling complex financial data.
Automatically Detecting Numerical Instability in Machine Learning Applications via Soft Assertions
Venue: arXiv cs.SE 2025
Year: 2025

Machine learning (ML) applications have become an integral part of our lives. ML applications extensively use floating-point computation and involve very large/small numbers; thus, maintaining the numerical stability of such complex computations remains an important challenge. Numerical bugs can lead to system crashes, incorrect output, and wasted computing resources. In this paper, we introduce a novel idea, namely soft assertions (SA), to encode safety/error conditions for the places where numerical instability can occur. A soft assertion is an ML model automatically trained using the dataset obtained during unit testing of unstable functions. Given the values at the unstable function in an ML application, a soft assertion reports how to change these values in order to trigger the instability. We then use the output of soft assertions as signals to effectively mutate inputs to trigger numerical instability in ML applications. In the evaluation, we used the GRIST benchmark, a total of 79 programs, as well as 15 real-world ML applications from GitHub. We compared our tool with 5 state-of-the-art (SOTA) fuzzers. We found all the GRIST bugs and outperformed the baselines. We found 13 numerical bugs in real-world code, one of which had already been confirmed by the GitHub developers. While the baselines mostly found the bugs that report NaN and INF, our tool \tool found numerical bugs with incorrect output. We showed one case where the Tumor Detection Model, trained on Brain MRI images, should have predicted "tumor", but instead, it incorrectly predicted "no tumor" due to the numerical bugs. Our replication package is located at https://figshare.com/s/6528d21ccd28bea94c32.
Few-Shot Vision-Language Action-Incremental Policy Learning
Venue: arXiv cs.RO 2025
Year: 2025

Recently, Transformer-based robotic manipulation methods utilize multi-view spatial representations and language instructions to learn robot motion trajectories by leveraging numerous robot demonstrations. However, the collection of robot data is extremely challenging, and existing methods lack the capability for continuous learning on new tasks with only a few demonstrations. In this paper, we formulate these challenges as the Few-Shot Action-Incremental Learning (FSAIL) task, and accordingly design a Task-prOmpt graPh evolutIon poliCy (TOPIC) to address these issues. Specifically, to address the data scarcity issue in robotic imitation learning, TOPIC learns Task-Specific Prompts (TSP) through the deep interaction of multi-modal information within few-shot demonstrations, thereby effectively extracting the task-specific discriminative information. On the other hand, to enhance the capability for continual learning on new tasks and mitigate the issue of catastrophic forgetting, TOPIC adopts a Continuous Evolution Strategy (CES). CES leverages the intrinsic relationships between tasks to construct a task relation graph, which effectively facilitates the adaptation of new tasks by reusing skills learned from previous tasks. TOPIC pioneers few-shot continual learning in the robotic manipulation task, and extensive experimental results demonstrate that TOPIC outperforms state-of-the-art baselines by over 26$\%$ in success rate, significantly enhancing the continual learning capabilities of existing Transformer-based policies.