OpenRaiser/Intern-Atlas huggingface dataset:
SketchAssist: A Practical Assistant for Semantic Edits and Precise Local Redrawing
Venue: arXiv cs.CV 2025
Year: 2025

Sketch editing is central to digital illustration, yet existing image editing systems struggle to preserve the sparse, style-sensitive structure of line art while supporting both high-level semantic changes and precise local redrawing. We present SketchAssist, an interactive sketch drawing assistant that accelerates creation by unifying instruction-guided global edits with line-guided region redrawing, while keeping unrelated regions and overall composition intact. To enable this assistant at scale, we introduce a controllable data generation pipeline that (i) constructs attribute-addition sequences from attribute-free base sketches, (ii) forms multi-step edit chains via cross-sequence sampling, and (iii) expands stylistic coverage with a style-preserving attribute-removal model applied to diverse sketches. Building on this data, SketchAssist employs a unified sketch editing framework with minimal changes to DiT-based editors. We repurpose the RGB channels to encode the inputs, enabling seamless switching between instruction-guided edits and line-guided redrawing within a single input interface. To further specialize behavior across modes, we integrate a task-guided mixture-of-experts into LoRA layers, routing by text and visual cues to improve semantic controllability, structural fidelity, and style preservation. Extensive experiments show state-of-the-art results on both tasks, with superior instruction adherence and style/structure preservation compared to recent baselines. Together, our dataset and SketchAssist provide a practical, controllable assistant for sketch creation and revision.
Astraea: A State-Aware Scheduling Engine for LLM-Powered Agents
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are increasingly being deployed as intelligent agents. Their multi-stage workflows, which alternate between local computation and calls to external network services like Web APIs, introduce a mismatch in their execution pattern and the scheduling granularity of existing inference systems such as vLLM. Existing systems typically focus on per-segment optimization which prevents them from minimizing the end-to-end latency of the complete agentic workflow, i.e., the global Job Completion Time (JCT) over the entire request lifecycle. To address this limitation, we propose Astraea, a service engine designed to shift the optimization from local segments to the global request lifecycle. Astraea employs a state-aware, hierarchical scheduling algorithm that integrates a request's historical state with future predictions. It dynamically classifies requests by their I/O and compute intensive nature and uses an enhanced HRRN policy to balance efficiency and fairness. Astraea also implements an adaptive KV cache manager that intelligently handles the agent state during I/O waits based on the system memory pressure. Extensive experiments show that Astraea reduces average JCT by up to 25.5\% compared to baseline methods. Moreover, our approach demonstrates strong robustness and stability under high load across various model scales.
Adaptive Cache Pollution Control for Large Language Model Inference Workloads Using Temporal CNN-Based Prediction and Priority-Aware Replacement
Venue: arXiv cs.AR 2025
Year: 2025

Large Language Models (LLMs), such as GPT and LLaMA, introduce unique memory access characteristics during inference due to frequent token sequence lookups and embedding vector retrievals. These workloads generate highly irregular and bursty access patterns, causing traditional prefetching and replacement policies to mispredict and trigger severe cache pollution, thereby degrading system performance. To address this challenge, this paper proposes an Adaptive Cache Pollution Control (ACPC) mechanism tailored for LLM inference workloads, integrating Temporal Convolutional Network (TCN)-based access prediction with a priority-aware replacement strategy. The TCN module learns temporal dependencies in token access sequences to identify potential high-reuse cache lines, while the replacement policy dynamically adjusts eviction priorities based on predicted reuse likelihood and cache occupancy. The proposed framework is implemented and evaluated on representative transformer-based inference traces, including GPT-style autoregressive decoding and embedding retrieval workloads. Experimental results demonstrate that ACPC reduces cache pollution by 41.7 percent, improves cache hit rate by 8.9 percent, and achieves a 60.0 percent reduction in L2 miss penalty, compared with state-of-the-art machine-learning-based replacement baselines. Additionally, the proposed Temporal CNN-based ACPC framework increases token generation throughput by 15.9 percent and achieves the lowest final loss of 0.21, confirming its superior efficiency and stability under complex LLM inference workloads. These results highlight ACPC's effectiveness in recognizing useful cache lines and mitigating redundant prefetches under dynamic LLM access behaviors. The proposed approach provides a scalable, learning-driven solution for optimizing memory efficiency and latency in large-scale LLM serving and inference systems.
Transfer Learning-Based Surrogate Modeling for Nonlinear Time-History Response Analysis of High-Fidelity Structural Models
Venue: arXiv cs.CE 2025
Year: 2025

In a performance based earthquake engineering (PBEE) framework, nonlinear time-history response analysis (NLTHA) for numerous ground motions are required to assess the seismic risk of buildings or civil engineering structures. However, such numerical simulations are computationally expensive, limiting the real-world practical application of the framework. To address this issue, previous studies have used machine learning to predict the structural responses to ground motions with low computational costs. These studies typically conduct NLTHAs for a few hundreds ground motions and use the results to train and validate surrogate models. However, most of the previous studies focused on computationally-inexpensive response analysis models such as single degree of freedom. Surrogate models of high-fidelity response analysis are required to enrich the quantity and diversity of information used for damage assessment in PBEE. Notably, the computational cost of creating training and validation datasets increases if the fidelity of response analysis model becomes higher. Therefore, methods that enable surrogate modeling of high-fidelity response analysis without a large number of training samples are needed. This study proposes a framework that uses transfer learning to construct the surrogate model of a high-fidelity response analysis model. This framework uses a surrogate model of low-fidelity response analysis as the pretrained model and transfers its knowledge to construct surrogate models for high-fidelity response analysis with substantially reduced computational cost. As a case study, surrogate models that predict responses of a 20-story steel moment frame were constructed with only 20 samples as the training dataset. The responses to the ground motions predicted by constructed surrogate model were consistent with a site-specific time-based hazard.
FastDDHPose: Towards Unified, Efficient, and Disentangled 3D Human Pose Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Recent approaches for monocular 3D human pose estimation (3D HPE) have achieved leading performance by directly regressing 3D poses from 2D keypoint sequences. Despite the rapid progress in 3D HPE, existing methods are typically trained and evaluated under disparate frameworks, lacking a unified framework for fair comparison. To address these limitations, we propose Fast3DHPE, a modular framework that facilitates rapid reproduction and flexible development of new methods. By standardizing training and evaluation protocols, Fast3DHPE enables fair comparison across 3D human pose estimation methods while significantly improving training efficiency. Within this framework, we introduce FastDDHPose, a Disentangled Diffusion-based 3D Human Pose Estimation method which leverages the strong latent distribution modeling capability of diffusion models to explicitly model the distributions of bone length and bone direction while avoiding further amplification of hierarchical error accumulation. Moreover, we design an efficient Kinematic-Hierarchical Spatial and Temporal Denoiser that encourages the model to focus on kinematic joint hierarchies while avoiding unnecessary modeling of overly complex joint topologies. Extensive experiments on Human3.6M and MPI-INF-3DHP show that the Fast3DHPE framework enables fair comparison of all methods while significantly improving training efficiency. Within this unified framework, FastDDHPose achieves state-of-the-art performance with strong generalization and robustness in in-the-wild scenarios. The framework and models will be released at: https://github.com/Andyen512/Fast3DHPE
ReadyPower: A Reliable, Interpretable, and Handy Architectural Power Model Based on Analytical Framework
Venue: arXiv cs.AR 2025
Year: 2025

Power is a primary objective in modern processor design, requiring accurate yet efficient power modeling techniques. Architecture-level power models are necessary for early power optimization and design space exploration. However, classical analytical architecture-level power models (e.g., McPAT) suffer from significant inaccuracies. Emerging machine learning (ML)-based power models, despite their superior accuracy in research papers, are not widely adopted in the industry. In this work, we point out three inherent limitations of ML-based power models: unreliability, limited interpretability, and difficulty in usage. This work proposes a new analytical power modeling framework named ReadyPower, which is ready-for-use by being reliable, interpretable, and handy. We observe that the root cause of the low accuracy of classical analytical power models is the discrepancies between the real processor implementation and the processor's analytical model. To bridge the discrepancies, we introduce architecture-level, implementation-level, and technology-level parameters into the widely adopted McPAT analytical model to build ReadyPower. The parameters at three different levels are decided in different ways. In our experiment, averaged across different training scenarios, ReadyPower achieves >20% lower mean absolute percentage error (MAPE) and >0.2 higher correlation coefficient R compared with the ML-based baselines, on both BOOM and XiangShan CPU architectures.baselines, on both BOOM and XiangShan CPU architectures.
Polygon Containment and Translational Min-Hausdorff-Distance between Segment Sets are 3SUM-Hard
Venue: arXiv cs.CG 2025
Year: 2025

The 3SUM problem represents a class of problems conjectured to require $Ω(n^2)$ time to solve, where $n$ is the size of the input. Given two polygons $P$ and $Q$ in the plane, we show that some variants of the decision problem, whether there exists a transformation of $P$ that makes it contained in $Q$, are 3SUM-Hard. In the first variant $P$ and $Q$ are any simple polygons and the allowed transformations are translations only; in the second and third variants both polygons are convex and we allow either rotations only or any rigid motion. We also show that finding the translation in the plane that minimizes the Hausdorff distance between two segment sets is 3SUM-Hard.
Trajectory Tracking for Multi-Manipulator Systems in Constrained Environments
Venue: arXiv cs.RO 2025
Year: 2025

We consider the problem of cooperative manipulation by a mobile multi-manipulator system operating in obstacle-cluttered and highly constrained environments under spatio-temporal task specifications. The task requires transporting a grasped object while respecting both continuous robot dynamics and discrete geometric constraints arising from obstacles and narrow passages. To address this hybrid structure, we propose a multi-rate planning and control framework that combines offline generation of an STL-satisfying object trajectory and collision-free base footprints with online constrained inverse kinematics and continuous-time feedback control. The resulting closed-loop system enables coordinated reconfiguration of multiple manipulators while tracking the desired object motion. The approach is evaluated in high-fidelity physics simulations using three Franka Emika Panda mobile manipulators rigidly grasping an object.
Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets
Venue: arXiv cs.CL 2025
Year: 2025

Fine-tuning large language models (LLMs) is often limited by the memory available on commodity GPUs. Parameter-efficient fine-tuning (PEFT) methods such as QLoRA reduce the number of trainable parameters, yet still incur high memory usage induced by the backward pass in the full model. We revisit Ladder Side Tuning (LST), a rarely explored PEFT technique that adds a lightweight side network, and show that it matches QLoRA's compute scaling slope while cutting peak memory by 50\%. Across different downstream benchmarks spanning natural language understanding, mathematical and LLM-critic tasks, LST has competitive performance with QLoRA's accuracy on average while being much more memory-efficient. This efficiency enables fine-tuning of 7B-parameter models on a single 12 GB consumer GPU with 2k-token contexts, requiring no gradient checkpointing\textemdash conditions under which QLoRA exhausts memory. Beyond memory efficiency, we also establish scaling laws showing that LST scales similarly to QLoRA. We exploit Ladder's architectural flexibility by introducing xLadder, a depth-extended variant that increases effective depth via cross-connections and shortens chain-of-thought (CoT) at fixed parameter count. Ladder is strong when memory is the bottleneck; xLadder builds on this by enabling deeper reasoning without additional memory overhead.
LegionITS: A Federated Intrusion-Tolerant System Architecture
Venue: arXiv cs.CR 2025
Year: 2025

The growing sophistication, frequency, and diversity of cyberattacks increasingly exceed the capacity of individual entities to fully understand and counter them. While existing solutions, such as Security Information and Event Management (SIEM) systems, Security Orchestration, Automation, and Response (SOAR) platforms, and Security Operation Center (SOC), play a vital role in mitigating known threats, they often struggle to effectively address emerging and unforeseen attacks. To increase the effectiveness of cyber defense, it is essential to foster greater information sharing between entities; however, this requires addressing the challenge of exchanging sensitive data without compromising confidentiality or operational security. To address the challenges of secure and confidential Cyber Threat Intelligence (CTI) sharing, we propose a novel architecture that federates Intrusion Tolerant Systems (ITSs) and leverages concepts from Malware Information Sharing Platform (MISP) to empower SOCs. This framework enables controlled collaboration and data privacy while enhancing collective defenses. As a proof of concept, we evaluate one module by applying Differential Privacy (DP) to Federated Learning (FL), observing a manageable accuracy drop from 98.42% to 85.98% (average loss 12.44%) while maintaining reliable detection of compromised messages. These results highlight the viability of secure data sharing and establishes a foundation for the future full-scale implementation of LegionITS.
CaFe-TeleVision: A Coarse-to-Fine Teleoperation System with Immersive Situated Visualization for Enhanced Ergonomics
Venue: arXiv cs.RO 2025
Year: 2025

Teleoperation presents a promising paradigm for remote control and robot proprioceptive data collection. Despite recent progress, current teleoperation systems still suffer from limitations in efficiency and ergonomics, particularly in challenging scenarios. In this paper, we propose CaFe-TeleVision, a coarse-to-fine teleoperation system with immersive situated visualization for enhanced ergonomics. At its core, a coarse-to-fine control mechanism is proposed in the retargeting module to bridge workspace disparities, jointly optimizing efficiency and physical ergonomics. To stream immersive feedback with adequate visual cues for human vision systems, an on-demand situated visualization technique is integrated in the perception module, which reduces the cognitive load for multi-view processing. The system is built on a humanoid collaborative robot and validated with six challenging bimanual manipulation tasks. User study among 24 participants confirms that CaFe-TeleVision enhances ergonomics with statistical significance, indicating a lower task load and a higher user acceptance during teleoperation. Quantitative results also validate the superior performance of our system across six tasks, surpassing comparative methods by up to 28.89% in success rate and accelerating by 26.81% in completion time. Project webpage: https://clover-cuhk.github.io/cafe_television/
A Hybrid Reactive-Proactive Auto-scaling Algorithm for SLA-Constrained Edge Computing
Venue: arXiv cs.DC 2025
Year: 2025

Edge computing decentralizes computing resources, allowing for novel applications in domains such as the Internet of Things (IoT) in healthcare and agriculture by reducing latency and improving performance. This decentralization is achieved through the implementation of microservice architectures, which require low latencies to meet stringent service level agreements (SLA) such as performance, reliability, and availability metrics. While cloud computing offers the large data storage and computation resources necessary to handle peak demands, a hybrid cloud and edge environment is required to ensure SLA compliance. This is achieved by sophisticated orchestration strategies such as Kubernetes, which help facilitate resource management. The orchestration strategies alone do not guarantee SLA adherence due to the inherent delay of scaling resources. Existing auto-scaling algorithms have been proposed to address these challenges, but they suffer from performance issues and configuration complexity. In this paper, a novel auto-scaling algorithm is proposed for SLA-constrained edge computing applications. This approach combines a Machine Learning (ML) based proactive auto-scaling algorithm, capable of predicting incoming resource requests to forecast demand, with a reactive autoscaler which considers current resource utilization and SLA constraints for immediate adjustments. The algorithm is integrated into Kubernetes as an extension, and its performance is evaluated through extensive experiments in an edge environment with real applications. The results demonstrate that existing solutions have an SLA violation rate of up to 23%, whereas the proposed hybrid solution outperforms the baselines with an SLA violation rate of only 6%, ensuring stable SLA compliance across various applications.
Multi-Agent Medical Decision Consensus Matrix System: An Intelligent Collaborative Framework for Oncology MDT Consultations
Venue: arXiv cs.MA 2025
Year: 2025

Multidisciplinary team (MDT) consultations are the gold standard for cancer care decision-making, yet current practice lacks structured mechanisms for quantifying consensus and ensuring decision traceability. We introduce a Multi-Agent Medical Decision Consensus Matrix System that deploys seven specialized large language model agents, including an oncologist, a radiologist, a nurse, a psychologist, a patient advocate, a nutritionist and a rehabilitation therapist, to simulate realistic MDT workflows. The framework incorporates a mathematically grounded consensus matrix that uses Kendall's coefficient of concordance to objectively assess agreement. To further enhance treatment recommendation quality and consensus efficiency, the system integrates reinforcement learning methods, including Q-Learning, PPO and DQN. Evaluation across five medical benchmarks (MedQA, PubMedQA, DDXPlus, MedBullets and SymCat) shows substantial gains over existing approaches, achieving an average accuracy of 87.5% compared with 83.8% for the strongest baseline, a consensus achievement rate of 89.3% and a mean Kendall's W of 0.823. Expert reviewers rated the clinical appropriateness of system outputs at 8.9/10. The system guarantees full evidence traceability through mandatory citations of clinical guidelines and peer-reviewed literature, following GRADE principles. This work advances medical AI by providing structured consensus measurement, role-specialized multi-agent collaboration and evidence-based explainability to improve the quality and efficiency of clinical decision-making.
Criminal Liability in AI-Enabled Autonomous Vehicles: A Comparative Study
Venue: arXiv cs.CY 2025
Year: 2025

AI revolutionizes transportation through autonomous vehicles (AVs) but introduces complex criminal liability issues regarding infractions. This study employs a comparative legal analysis of primary statutes, real-world liability claims, and academic literature across the US, Germany, UK, China, and India; jurisdictions selected for their technological advancement and contrasting regulatory approaches. The research examines the attribution of human error, AI moral agency, and the identification of primary offenders in AV incidents. Findings reveal fragmented regulatory landscapes: India and the US rely on loose networks of state laws, whereas the UK enacted the pioneering Automated and Electric Vehicles Act 2018. Germany enforces strict safety standards, distinguishing liability based on the vehicle's operating mode, while China similarly aims for a stringent liability regime. The study concludes that globally harmonized legal standards are essential to foster technological innovation while ensuring minimum risk and clear liability attribution.
Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring
Venue: arXiv cs.CL 2025
Year: 2025

The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and more accurately. However, a growing body of studies show that LRMs are still inefficient, over-generating verification and reflection steps. To address this challenge, we introduce the Step-Tagging framework, a lightweight sentence-classifier enabling real-time annotation of the type of reasoning steps that an LRM is generating. To monitor reasoning behaviors, we introduced ReasonType: a novel taxonomy of reasoning steps. Building on this framework, we demonstrated that online monitoring of the count of specific steps can produce effective interpretable early stopping criteria of LRM inferences. We evaluate the Step-tagging framework on three open-source reasoning models across standard benchmark datasets: MATH500, GSM8K, AIME and non-mathematical tasks (GPQA and MMLU-Pro). We achieve 20 to 50\% token reduction while maintaining comparable accuracy to standard generation, with largest gains observed on more computation-heavy tasks. This work offers a novel way to increase control over the generation of LRMs, and a new tool to study behaviors of LRMs.