OpenRaiser/Intern-Atlas huggingface dataset:
Multiport Support for Vortex OpenGPU Memory Hierarchy
Venue: arXiv cs.AR 2025
Year: 2025

Modern day applications have grown in size and require more computational power. The rise of machine learning and AI increased the need for parallel computation, which has increased the need for GPGPUs. With the increasing demand for computational power, GPGPUs' SIMT architecture has solved this with an increase in the number of threads and the number of cores in a GPU, increasing the throughput of these processors to match the demand of the applications. However, this created a larger demand for the memory, making the memory bandwidth a bottleneck. The introduction of High-Bandwidth Memory (HBM) with its increased number of memory ports offers a potential solution for the GPU to exploit its memory parallelism to increase the memory bandwidth. However, effectively leveraging HBM's memory parallelism to maximize bandwidth presents a unique and complex challenge for GPU architectures on how to distribute those ports among the streaming multiprocessors in the GPGPU. In this work, we extend the Vortex OpenGPU microarchitecture to incorporate a multiport memory hierarchy, spanning from the L1 cache to the last-level cache (LLC). In addition, we propose various arbitration strategies to optimize memory transfers across the cache hierarchy. The results have shown that an increase in memory ports increases IPC, achieving an average speedup of 2.34x with 8 memory ports in the tested configuration while showing relatively small area overhead.
AI-Based Screening for Depression and Social Anxiety Through Eye Tracking: An Exploratory Study
Venue: arXiv cs.CV 2025
Year: 2025

Well-being is a dynamic construct that evolves over time and fluctuates within individuals, presenting challenges for accurate quantification. Reduced well-being is often linked to depression or anxiety disorders, which are characterised by biases in visual attention towards specific stimuli, such as human faces. This paper introduces a novel approach to AI-assisted screening of affective disorders by analysing visual attention scan paths using convolutional neural networks (CNNs). Data were collected from two studies examining (1) attentional tendencies in individuals diagnosed with major depression and (2) social anxiety. These data were processed using residual CNNs through images generated from eye-gaze patterns. Experimental results, obtained with ResNet architectures, demonstrated an average accuracy of 48% for a three-class system and 62% for a two-class system. Based on these exploratory findings, we propose that this method could be employed in rapid, ecological, and effective mental health screening systems to assess well-being through eye-tracking.
InstructVEdit: A Holistic Approach for Instructional Video Editing
Venue: arXiv cs.CV 2025
Year: 2025

Video editing according to instructions is a highly challenging task due to the difficulty in collecting large-scale, high-quality edited video pair data. This scarcity not only limits the availability of training data but also hinders the systematic exploration of model architectures and training strategies. While prior work has improved specific aspects of video editing (e.g., synthesizing a video dataset using image editing techniques or decomposed video editing training), a holistic framework addressing the above challenges remains underexplored. In this study, we introduce InstructVEdit, a full-cycle instructional video editing approach that: (1) establishes a reliable dataset curation workflow to initialize training, (2) incorporates two model architectural improvements to enhance edit quality while preserving temporal consistency, and (3) proposes an iterative refinement strategy leveraging real-world data to enhance generalization and minimize train-test discrepancies. Extensive experiments show that InstructVEdit achieves state-of-the-art performance in instruction-based video editing, demonstrating robust adaptability to diverse real-world scenarios. Project page: https://o937-blip.github.io/InstructVEdit.
OMR-Diffusion:Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Intent Understanding
Venue: arXiv cs.CV 2025
Year: 2025

Generative AI has significantly advanced text-driven image generation, but it still faces challenges in producing outputs that consistently align with evolving user preferences and intents, particularly in multi-turn dialogue scenarios. In this research, We present a Visual Co-Adaptation (VCA) framework that incorporates human-in-the-loop feedback, utilizing a well-trained reward model specifically designed to closely align with human preferences. Using a diverse multi-turn dialogue dataset, the framework applies multiple reward functions (such as diversity, consistency, and preference feedback) to refine the diffusion model through LoRA, effectively optimizing image generation based on user input. We also constructed multi-round dialogue datasets with prompts and image pairs that well-fit user intent. Experiments show the model achieves 508 wins in human evaluation, outperforming DALL-E 3 (463 wins) and others. It also achieves 3.4 rounds in dialogue efficiency (vs. 13.7 for DALL-E 3) and excels in metrics like LPIPS (0.15) and BLIP (0.59). Various experiments demonstrate the effectiveness of the proposed method over state-of-the-art baselines, with significant improvements in image consistency and alignment with user intent.
ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation
Venue: arXiv cs.MA 2025
Year: 2025

ComfyUI is a popular workflow-based interface that allows users to customize image generation tasks through an intuitive node-based system. However, the complexity of managing node connections and diverse modules can be challenging for users. In this paper, we introduce ComfyGPT, a self-optimizing multi-agent system designed to generate ComfyUI workflows based on task descriptions automatically. The key innovations of ComfyGPT include: (1) consisting of four specialized agents to build a multi-agent workflow generation system: ReformatAgent, FlowAgent, RefineAgent, and ExecuteAgent; (2) focusing on generating precise node connections instead of entire workflows, improving generation accuracy; and (3) enhancing workflow generation through reinforcement learning. Moreover, we introduce FlowDataset, a large-scale dataset containing 13,571 workflow-description pairs, and FlowBench, a comprehensive benchmark for evaluating workflow generation systems. Additionally, we propose four novel evaluation metrics: Format Validation (FV), Pass Accuracy (PA), Pass Instruct Alignment (PIA), and Pass Node Diversity (PND). Experimental results demonstrate that ComfyGPT significantly outperforms existing LLM-based methods in workflow generation, making it a significant step forward in this field. Code is avaliable at https://github.com/comfygpt/comfygpt.
Intelligence Sequencing and the Path-Dependence of Intelligence Evolution: AGI-First vs. DCI-First as Irreversible Attractors
Venue: arXiv cs.AI 2025
Year: 2025

The trajectory of intelligence evolution is often framed around the emergence of artificial general intelligence (AGI) and its alignment with human values. This paper challenges that framing by introducing the concept of intelligence sequencing: the idea that the order in which AGI and decentralized collective intelligence (DCI) emerge determines the long-term attractor basin of intelligence. Using insights from dynamical systems, evolutionary game theory, and network models, it argues that intelligence follows a path-dependent, irreversible trajectory. Once development enters a centralized (AGI-first) or decentralized (DCI-first) regime, transitions become structurally infeasible due to feedback loops and resource lock-in. Intelligence attractors are modeled in functional state space as the co-navigation of conceptual and adaptive fitness spaces. Early-phase structuring constrains later dynamics, much like renormalization in physics. This has major implications for AI safety: traditional alignment assumes AGI will emerge and must be controlled after the fact, but this paper argues that intelligence sequencing is more foundational. If AGI-first architectures dominate before DCI reaches critical mass, hierarchical monopolization and existential risk become locked in. If DCI-first emerges, intelligence stabilizes around decentralized cooperative equilibrium. The paper further explores whether intelligence structurally biases itself toward an attractor based on its self-modeling method -- externally imposed axioms (favoring AGI) vs. recursive internal visualization (favoring DCI). Finally, it proposes methods to test this theory via simulations, historical lock-in case studies, and intelligence network analysis. The findings suggest that intelligence sequencing is a civilizational tipping point: determining whether the future is shaped by unbounded competition or unbounded cooperation.
Aportes para el cumplimiento del Reglamento (UE) 2024/1689 en robótica y sistemas autónomos
Venue: arXiv cs.RO 2025
Year: 2025

Cybersecurity in robotics stands out as a key aspect within Regulation (EU) 2024/1689, also known as the Artificial Intelligence Act, which establishes specific guidelines for intelligent and automated systems. A fundamental distinction in this regulatory framework is the difference between robots with Artificial Intelligence (AI) and those that operate through automation systems without AI, since the former are subject to stricter security requirements due to their learning and autonomy capabilities. This work analyzes cybersecurity tools applicable to advanced robotic systems, with special emphasis on the protection of knowledge bases in cognitive architectures. Furthermore, a list of basic tools is proposed to guarantee the security, integrity, and resilience of these systems, and a practical case is presented, focused on the analysis of robot knowledge management, where ten evaluation criteria are defined to ensure compliance with the regulation and reduce risks in human-robot interaction (HRI) environments.
Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information
Venue: arXiv cs.CL 2025
Year: 2025

Language agents powered by large language models (LLMs) face significant deployment challenges in resource-constrained environments, particularly for specialized domains and less-common languages. This paper presents Tox-chat, a Korean chemical toxicity information agent devised within these limitations. We propose two key innovations: a context-efficient architecture that reduces token consumption through hierarchical section search, and a scenario-based dialogue generation methodology that effectively distills tool-using capabilities from larger models. Experimental evaluations demonstrate that our fine-tuned 8B parameter model substantially outperforms both untuned models and baseline approaches, in terms of DB faithfulness and preference. Our work offers valuable insights for researchers developing domain-specific language agents under practical constraints.
Why do Opinions and Actions Diverge? A Dynamic Framework to Explore the Impact of Subjective Norms
Venue: arXiv cs.SI 2025
Year: 2025

Socio-psychological studies have identified a common phenomenon where an individual's public actions do not necessarily coincide with their private opinions, yet most existing models fail to capture the dynamic interplay between these two aspects. To bridge this gap, we propose a novel agent-based modeling framework that integrates opinion dynamics with a decision-making mechanism. More precisely, our framework generalizes the classical Hegselmann-Krause model by combining it with a utility maximization problem. Preliminary results from our model demonstrate that the degree of opinion-action divergence within a population can be effectively controlled by adjusting two key parameters that reflect agents' personality traits, while the presence of social network amplifies the divergence. In addition, we study the social diffusion process by introducing a small number of committed agents into the model, and identify three key outcomes: adoption of innovation, rejection of innovation, and the enforcement of unpopular norms, consistent with findings in socio-psychological literature. The strong relevance of the results to real-world phenomena highlights our framework's potential for future applications in understanding and predicting complex social behaviors.
Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in code large language models (LLMs) have demonstrated remarkable capabilities in code generation and understanding. It is still challenging to build a code LLM with comprehensive performance yet ultimate efficiency. Many attempts have been released in the open source community to break the trade-off between performance and efficiency, such as the Qwen Coder series and the DeepSeek Coder series. This paper introduces yet another attempt in this area, namely Ling-Coder-Lite. We leverage the efficient Mixture-of-Experts (MoE) architecture along with a set of high-quality data curation methods (especially those based on program analytics) to build an efficient yet powerful code LLM. Ling-Coder-Lite exhibits on-par performance on 12 representative coding benchmarks compared to state-of-the-art models of similar size, such as Qwen2.5-Coder-7B and DeepSeek-Coder-V2-Lite, while offering competitive latency and throughput. In practice, we achieve a 50\% reduction in deployment resources compared to the similar-sized dense model without performance loss. To facilitate further research and development in this area, we open-source our models as well as a substantial portion of high-quality data for the annealing and post-training stages. The models and data can be accessed at~\url{https://huggingface.co/inclusionAI/Ling-Coder-lite}.
Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative Models
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-image generative models often struggle with long prompts detailing complex scenes, diverse objects with distinct visual characteristics and spatial relationships. In this work, we propose SCoPE (Scheduled interpolation of Coarse-to-fine Prompt Embeddings), a training-free method to improve text-to-image alignment by progressively refining the input prompt in a coarse-to-fine-grained manner. Given a detailed input prompt, we first decompose it into multiple sub-prompts which evolve from describing broad scene layout to highly intricate details. During inference, we interpolate between these sub-prompts and thus progressively introduce finer-grained details into the generated image. Our training-free plug-and-play approach significantly enhances prompt alignment, achieves an average improvement of more than +8 in Visual Question Answering (VQA) scores over the Stable Diffusion baselines on 83% of the prompts from the GenAI-Bench dataset.
Connectedness: a dimension of security bug severity assessment for measuring uncertainty
Venue: arXiv cs.CR 2025
Year: 2025

Current frameworks for evaluating security bug severity, such as the Common Vulnerability Scoring System (CVSS), prioritize the ratio of exploitability to impact. This paper suggests that the above approach measures the "known knowns" but inadequately addresses the "known unknowns" especially when there exist multiple possible exploit paths and side effects, which introduce significant uncertainty. This paper introduces the concept of connectedness, which measures how strongly a security bug is connected with different entities, thereby reflecting the uncertainty of impact and the exploit potential. This work highlights the critical but underappreciated role connectedness plays in severity assessments.
RefCut: Interactive Segmentation with Reference Guidance
Venue: arXiv cs.CV 2025
Year: 2025

Interactive segmentation aims to segment the specified target on the image with positive and negative clicks from users. Interactive ambiguity is a crucial issue in this field, which refers to the possibility of multiple compliant outcomes with the same clicks, such as selecting a part of an object versus the entire object, a single object versus a combination of multiple objects, and so on. The existing methods cannot provide intuitive guidance to the model, which leads to unstable output results and makes it difficult to meet the large-scale and efficient annotation requirements for specific targets in some scenarios. To bridge this gap, we introduce RefCut, a reference-based interactive segmentation framework designed to address part ambiguity and object ambiguity in segmenting specific targets. Users only need to provide a reference image and corresponding reference masks, and the model will be optimized based on them, which greatly reduces the interactive burden on users when annotating a large number of such targets. In addition, to enrich these two kinds of ambiguous data, we propose a new Target Disassembly Dataset which contains two subsets of part disassembly and object disassembly for evaluation. In the combination evaluation of multiple datasets, our RefCut achieved state-of-the-art performance. Extensive experiments and visualized results demonstrate that RefCut advances the field of intuitive and controllable interactive segmentation. Our code will be publicly available and the demo video is in https://www.lin-zheng.com/refcut.
Privacy-Preserving Hamming Distance Computation with Property-Preserving Hashing
Venue: arXiv cs.CC 2025
Year: 2025

We study the problem of approximating Hamming distance in sublinear time under property-preserving hashing (PPH), where only hashed representations of inputs are available. Building on the threshold evaluation framework of Fleischhacker, Larsen, and Simkin (EUROCRYPT 2022), we present a sequence of constructions with progressively improved complexity: a baseline binary search algorithm, a refined variant with constant repetition per query, and a novel hash design that enables constant-time approximation without oracle access. Our results demonstrate that approximate distance recovery is possible under strong cryptographic guarantees, bridging efficiency and security in similarity estimation.
CP-AgentNet: Autonomous and Explainable Communication Protocol Design Using Generative Agents
Venue: arXiv cs.NI 2025
Year: 2025

Although DRL (deep reinforcement learning) has emerged as a powerful tool for making better decisions than existing hand-crafted communication protocols, it faces significant limitations: 1) Selecting the appropriate neural network architecture and setting hyperparameters are crucial for achieving desired performance levels, requiring domain expertise. 2) The decision-making process in DRL models is often opaque, commonly described as a 'black box.' 3) DRL models are data hungry. In response, we propose CP-AgentNet, the first framework designed to use generative agents for developing communication network protocols. This approach addresses these challenges by creating an autonomous system for protocol design, significantly reducing human effort. We developed LLMA (LLM-agents-based multiple access) and CPTCP (CP-Agent-based TCP) for heterogeneous environments. Our comprehensive simulations have demonstrated the efficient coexistence of LLMA and CPTCP with nodes using different types of protocols, as well as enhanced explainability.
GSound-SIR: A Spatial Impulse Response Ray-Tracing and High-order Ambisonic Auralization Python Toolkit
Venue: arXiv cs.SD 2025
Year: 2025

Accurate and efficient simulation of room impulse responses is crucial for spatial audio applications. However, existing acoustic ray-tracing tools often operate as black boxes and only output impulse responses (IRs), providing limited access to intermediate data or spatial fidelity. To address those problems, this paper presents GSound-SIR, a novel Python-based toolkit for room acoustics simulation that addresses these limitations. The contribution of this paper includes the follows. First, GSound-SIR provides direct access to up to millions of raw ray data points from simulations, enabling in-depth analysis of sound propagation paths that was not possible with previous solutions. Second, we introduce a tool to convert acoustic rays into high-order Ambisonic impulse response synthesis, capturing spatial audio cues with greater fidelity than standard techniques. Third, to enhance efficiency, the toolkit implements an energy-based filtering algorithm and can export only the top-X or top-X-% rays. Fourth, we propose to store the simulation results into Parquet formats, facilitating fast data I/O and seamless integration with data analysis workflows. Together, these features make GSound-SIR an advanced, efficient, and modern foundation for room acoustics research, providing researchers and developers with a powerful new tool for spatial audio exploration. We release the library under Apache 2.0 License at https://github.com/yongyizang/GSound-SIR.
Finding Stable Subnetworks at Initialization with Dataset Distillation
Venue: arXiv cs.LG 2025
Year: 2025

Recent works have shown that Dataset Distillation, the process for summarizing the training data, can be leveraged to accelerate the training of deep learning models. However, its impact on training dynamics, particularly in neural network pruning, remains largely unexplored. In our work, we use distilled data in the inner loop of iterative magnitude pruning to produce sparse, trainable subnetworks at initialization -- more commonly known as lottery tickets. While using 150x less training points, our algorithm matches the performance of traditional lottery ticket rewinding on ResNet-18 & CIFAR-10. Previous work highlights that lottery tickets can be found when the dense initialization is stable to SGD noise (i.e. training across different ordering of the data converges to the same minima). We extend this discovery, demonstrating that stable subnetworks can exist even within an unstable dense initialization. In our linear mode connectivity studies, we find that pruning with distilled data discards parameters that contribute to the sharpness of the loss landscape. Lastly, we show that by first generating a stable sparsity mask at initialization, we can find lottery tickets at significantly higher sparsities than traditional iterative magnitude pruning.
Financial Wind Tunnel: A Retrieval-Augmented Market Simulator
Venue: arXiv cs.CE 2025
Year: 2025

Market simulator tries to create high-quality synthetic financial data that mimics real-world market dynamics, which is crucial for model development and robust assessment. Despite continuous advancements in simulation methodologies, market fluctuations vary in terms of scale and sources, but existing frameworks often excel in only specific tasks. To address this challenge, we propose Financial Wind Tunnel (FWT), a retrieval-augmented market simulator designed to generate controllable, reasonable, and adaptable market dynamics for model testing. FWT offers a more comprehensive and systematic generative capability across different data frequencies. By leveraging a retrieval method to discover cross-sectional information as the augmented condition, our diffusion-based simulator seamlessly integrates both macro- and micro-level market patterns. Furthermore, our framework allows the simulation to be controlled with wide applicability, including causal generation through "what-if" prompts or unprecedented cross-market trend synthesis. Additionally, we develop an automated optimizer for downstream quantitative models, using stress testing of simulated scenarios via FWT to enhance returns while controlling risks. Experimental results demonstrate that our approach enables the generalizable and reliable market simulation, significantly improve the performance and adaptability of downstream models, particularly in highly complex and volatile market conditions. Our code and data sample is available at https://anonymous.4open.science/r/fwt_-E852