OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) suffers from severe sample inefficiency, especially during early training, requiring extensive environmental interactions to perform competently. Existing methods tend to solve this by incorporating prior knowledge, but introduce significant architectural and implementation complexity. We propose Dynamic Action Interpolation (DAI), a universal yet straightforward framework that interpolates expert and RL actions via a time-varying weight $α(t)$, integrating into any Actor-Critic algorithm with just a few lines of code and without auxiliary networks or additional losses. Our theoretical analysis shows that DAI reshapes state visitation distributions to accelerate value function learning while preserving convergence guarantees. Empirical evaluations across MuJoCo continuous control tasks demonstrate that DAI improves early-stage performance by over 160\% on average and final performance by more than 50\%, with the Humanoid task showing a 4$\times$ improvement early on and a 2$\times$ gain at convergence. These results challenge the assumption that complex architectural modifications are necessary for sample-efficient reinforcement learning.
STDArm: Transferring Visuomotor Policies From Static Data Training to Dynamic Robot Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Recent advances in mobile robotic platforms like quadruped robots and drones have spurred a demand for deploying visuomotor policies in increasingly dynamic environments. However, the collection of high-quality training data, the impact of platform motion and processing delays, and limited onboard computing resources pose significant barriers to existing solutions. In this work, we present STDArm, a system that directly transfers policies trained under static conditions to dynamic platforms without extensive modifications. The core of STDArm is a real-time action correction framework consisting of: (1) an action manager to boost control frequency and maintain temporal consistency, (2) a stabilizer with a lightweight prediction network to compensate for motion disturbances, and (3) an online latency estimation module for calibrating system parameters. In this way, STDArm achieves centimeter-level precision in mobile manipulation tasks. We conduct comprehensive evaluations of the proposed STDArm on two types of robotic arms, four types of mobile platforms, and three tasks. Experimental results indicate that the STDArm enables real-time compensation for platform motion disturbances while preserving the original policy's manipulation capabilities, achieving centimeter-level operational precision during robot motion.
Building Scalable AI-Powered Applications with Cloud Databases: Architectures, Best Practices and Performance Considerations
Venue: arXiv cs.DB 2025
Year: 2025

The rapid adoption of AI-powered applications demands high-performance, scalable, and efficient cloud database solutions, as traditional architectures often struggle with AI-driven workloads requiring real-time data access, vector search, and low-latency queries. This paper explores how cloud-native databases enable AI-driven applications by leveraging purpose-built technologies such as vector databases (pgvector), graph databases (AWS Neptune), NoSQL stores (Amazon DocumentDB, DynamoDB), and relational cloud databases (Aurora MySQL and PostgreSQL). It presents architectural patterns for integrating AI workloads with cloud databases, including Retrieval-Augmented Generation (RAG) [1] with LLMs, real-time data pipelines, AI-driven query optimization, and embeddings-based search. Performance benchmarks, scalability considerations, and cost-efficient strategies are evaluated to guide the design of AI-enabled applications. Real-world case studies from industries such as healthcare, finance, and customer experience illustrate how enterprises utilize cloud databases to enhance AI capabilities while ensuring security, governance, and compliance with enterprise and regulatory standards. By providing a comprehensive analysis of AI and cloud database integration, this paper serves as a practical guide for researchers, architects, and enterprises to build next-generation AI applications that optimize performance, scalability, and cost efficiency in cloud environments.
Hierarchical Reinforcement Learning in Multi-Goal Spatial Navigation with Autonomous Mobile Robots
Venue: arXiv cs.AI 2025
Year: 2025

Hierarchical reinforcement learning (HRL) is hypothesized to be able to leverage the inherent hierarchy in learning tasks where traditional reinforcement learning (RL) often fails. In this research, HRL is evaluated and contrasted with traditional RL in complex robotic navigation tasks. We evaluate unique characteristics of HRL, including its ability to create sub-goals and the termination functions. We constructed a number of experiments to test: 1) the differences between RL proximal policy optimization (PPO) and HRL, 2) different ways of creating sub-goals in HRL, 3) manual vs automatic sub-goal creation in HRL, and 4) the effects of the frequency of termination on performance in HRL. These experiments highlight the advantages of HRL over RL and how it achieves these advantages.
A Survey on Multimodal Music Emotion Recognition
Venue: arXiv cs.MM 2025
Year: 2025

Multimodal music emotion recognition (MMER) is an emerging discipline in music information retrieval that has experienced a surge in interest in recent years. This survey provides a comprehensive overview of the current state-of-the-art in MMER. Discussing the different approaches and techniques used in this field, the paper introduces a four-stage MMER framework, including multimodal data selection, feature extraction, feature processing, and final emotion prediction. The survey further reveals significant advancements in deep learning methods and the increasing importance of feature fusion techniques. Despite these advancements, challenges such as the need for large annotated datasets, datasets with more modalities, and real-time processing capabilities remain. This paper also contributes to the field by identifying critical gaps in current research and suggesting potential directions for future research. The gaps underscore the importance of developing robust, scalable, a interpretable models for MMER, with implications for applications in music recommendation systems, therapeutic tools, and entertainment.
Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation
Venue: arXiv cs.CL 2025
Year: 2025

Generating engaging, accurate short-form videos from scientific papers is challenging due to content complexity and the gap between expert authors and readers. Existing end-to-end methods often suffer from factual inaccuracies and visual artifacts, limiting their utility for scientific dissemination. To address these issues, we propose SciTalk, a novel multi-LLM agentic framework, grounding videos in various sources, such as text, figures, visual styles, and avatars. Inspired by content creators' workflows, SciTalk uses specialized agents for content summarization, visual scene planning, and text and layout editing, and incorporates an iterative feedback mechanism where video agents simulate user roles to give feedback on generated videos from previous iterations and refine generation prompts. Experimental evaluations show that SciTalk outperforms simple prompting methods in generating scientifically accurate and engaging content over the refined loop of video generation. Although preliminary results are still not yet matching human creators' quality, our framework provides valuable insights into the challenges and benefits of feedback-driven video generation. Our code, data, and generated videos will be publicly available.
BugsRepo: A Comprehensive Curated Dataset of Bug Reports, Comments and Contributors Information from Bugzilla
Venue: arXiv cs.SE 2025
Year: 2025

Bug reports help software development teams enhance software quality, yet their utility is often compromised by unclear or incomplete information. This issue not only hinders developers' ability to quickly understand and resolve bugs but also poses significant challenges for various software maintenance prediction systems, such as bug triaging, severity prediction, and bug report summarization. To address this issue, we introduce \textnormal{{\fontfamily{ppl}\selectfont BugsRepo}}, a multifaceted dataset derived from Mozilla projects that offers three key components to support a wide range of software maintenance tasks. First, it includes a Bug report meta-data & Comments dataset with detailed records for 119,585 fixed or closed and resolved bug reports, capturing fields like severity, creation time, status, and resolution to provide rich contextual insights. Second, {\fontfamily{ppl}\selectfont BugsRepo} features a contributor information dataset comprising 19,351 Mozilla community members, enriched with metadata on user roles, activity history, and contribution metrics such as the number of bugs filed, comments made, and patches reviewed, thus offering valuable information for tasks like developer recommendation. Lastly, the dataset provides a structured bug report subset of 10,351 well-structured bug reports, complete with steps to reproduce, actual behavior, and expected behavior. After this initial filter, a secondary filtering layer is applied using the CTQRS scale. By integrating static metadata, contributor statistics, and detailed comment threads, {\fontfamily{ppl}\selectfont BugsRepo} presents a holistic view of each bug's history, supporting advancements in automated bug report analysis, which can enhance the efficiency and effectiveness of software maintenance processes.
Apollo: Automated Routing-Informed Placement for Large-Scale Photonic Integrated Circuits
Venue: arXiv cs.ET 2025
Year: 2025

As technology advances, photonic integrated circuits (PICs) are rapidly scaling in size and complexity, with modern designs integrating thousands of components. However, the analog custom layout nature of photonics, the curvy waveguide structures, and single-layer routing resources impose stringent physical constraints, such as minimum bend radii and waveguide crossing penalties, which make manual layout the de facto standard. This manual process takes weeks to complete and is error-prone, which is fundamentally unscalable for large-scale PIC systems. Existing automation solutions have adopted force-directed placement on small benchmarks with tens of components, with limited routability and scalability. To fill this fundamental gap in the electronic-photonic design automation (EPDA) toolchain, we present the first GPU-accelerated, routing-informed placement framework. It features an asymmetric bending-aware wirelength function with explicit modeling of waveguide routing congestion and crossings for routability maximization. Meanwhile, conditional projection is employed to gradually enforce a variety of user-defined layout constraints, including alignment, spacing, etc. This constrained optimization is accelerated and stabilized by a custom blockwise adaptive Nesterov-accelerated optimizer, ensuring stable and high-quality convergence. Compared to existing methods, our method can generate high-quality layouts for large-scale PICs with an average routing success rate of 94.79% across all benchmarks within minutes. By tightly coupling placement with physical-aware routing, our method establishes a new paradigm for automated PIC design, bringing intelligent, scalable layout synthesis to the forefront of next-generation EPDA. Our code is open-sourced at https://github.com/ScopeX-ASU/Apollo.
Understanding Decentralized Social Feed Curation on Mastodon
Venue: arXiv cs.HC 2025
Year: 2025

As centralized social media platforms face growing concerns, more users are seeking greater control over their social feeds and turning to decentralized alternatives such as Mastodon. The decentralized nature of Mastodon creates unique opportunities for customizing feeds, yet user perceptions and curation strategies on these platforms remain unknown. This paper presents findings from a two-part interview study with 21 Mastodon users, exploring how they perceive, interact with, and manage their current feeds, and how we can better empower users to personalize their feeds on Mastodon. We use the qualitative findings of the first part of the study to guide the creation of Braids, a web-based prototype for feed curation. Results from the second part of our study, using Braids, highlighted opportunities and challenges for future research, particularly in using seamful design to enhance people's acceptance of algorithmic curation and nuanced trade-offs between machine learning-based and rule-based curation algorithms. To optimize user experience, we also discuss the tension between creating new apps and building add-ons in the decentralized social media realm.
Sentiment and Social Signals in the Climate Crisis: A Survey on Analyzing Social Media Responses to Extreme Weather Events
Venue: arXiv cs.SI 2025
Year: 2025

Extreme weather events driven by climate change, such as wildfires, floods, and heatwaves, prompt significant public reactions on social media platforms. Analyzing the sentiment expressed in these online discussions can offer valuable insights into public perception, inform policy decisions, and enhance emergency responses. Although sentiment analysis has been widely studied in various fields, its specific application to climate-induced events, particularly in real-time, high-impact situations like the 2025 Los Angeles forest fires, remains underexplored. In this survey, we thoroughly examine the methods, datasets, challenges, and ethical considerations related to sentiment analysis of social media content concerning weather and climate change events. We present a detailed taxonomy of approaches, ranging from lexicon-based and machine learning models to the latest strategies driven by large language models (LLMs). Additionally, we discuss data collection and annotation techniques, including weak supervision and real-time event tracking. Finally, we highlight several open problems, such as misinformation detection, multimodal sentiment extraction, and model alignment with human values. Our goal is to guide researchers and practitioners in effectively understanding sentiment during the climate crisis era.
A Microgravity Simulation Experimental Platform For Small Space Robots In Orbit
Venue: arXiv cs.RO 2025
Year: 2025

This study describes the development and validation of a novel microgravity experimental platform that is mainly applied to small robots such as modular self-reconfigurable robots. This platform mainly consists of an air supply system, a microporous platform and glass. By supplying air to the microporous platform to form an air film, the influence of the weight of the air foot and the ventilation hose of traditional air-float platforms on microgravity experiments is solved. The contribution of this work is to provide a platform with less external interference for microgravity simulation experiments on small robots.
Theoretical Framework for Tempered Fractional Gradient Descent: Application to Breast Cancer Classification
Venue: arXiv cs.LG 2025
Year: 2025

This paper introduces Tempered Fractional Gradient Descent (TFGD), a novel optimization framework that synergizes fractional calculus with exponential tempering to enhance gradient-based learning. Traditional gradient descent methods often suffer from oscillatory updates and slow convergence in high-dimensional, noisy landscapes. TFGD addresses these limitations by incorporating a tempered memory mechanism, where historical gradients are weighted by fractional coefficients $|w_j| = \binomα{j}$ and exponentially decayed via a tempering parameter $λ$. Theoretical analysis establishes TFGD's convergence guarantees: in convex settings, it achieves an $\mathcal{O}(1/K)$ rate with alignment coefficient $d_{α,λ} = (1 - e^{-λ})^{-α}$, while stochastic variants attain $\mathcal{O}(1/k^α)$ error decay. The algorithm maintains $\mathcal{O}(n)$ time complexity equivalent to SGD, with memory overhead scaling as $\mathcal{O}(d/λ)$ for parameter dimension $d$. Empirical validation on the Breast Cancer Wisconsin dataset demonstrates TFGD's superiority, achieving 98.25\% test accuracy (vs. 92.11\% for SGD) and 2$\times$ faster convergence. The tempered memory mechanism proves particularly effective in medical classification tasks, where feature correlations benefit from stable gradient averaging. These results position TFGD as a robust alternative to conventional optimizers in both theoretical and applied machine learning.
Latent Adversarial Training Improves the Representation of Refusal
Venue: arXiv cs.CL 2025
Year: 2025

Recent work has shown that language models' refusal behavior is primarily encoded in a single direction in their latent space, making it vulnerable to targeted attacks. Although Latent Adversarial Training (LAT) attempts to improve robustness by introducing noise during training, a key question remains: How does this noise-based training affect the underlying representation of refusal behavior? Understanding this encoding is crucial for evaluating LAT's effectiveness and limitations, just as the discovery of linear refusal directions revealed vulnerabilities in traditional supervised safety fine-tuning (SSFT). Through the analysis of Llama 2 7B, we examine how LAT reorganizes the refusal behavior in the model's latent space compared to SSFT and embedding space adversarial training (AT). By computing activation differences between harmful and harmless instruction pairs and applying Singular Value Decomposition (SVD), we find that LAT significantly alters the refusal representation, concentrating it in the first two SVD components which explain approximately 75 percent of the activation differences variance - significantly higher than in reference models. This concentrated representation leads to more effective and transferable refusal vectors for ablation attacks: LAT models show improved robustness when attacked with vectors from reference models but become more vulnerable to self-generated vectors compared to SSFT and AT. Our findings suggest that LAT's training perturbations enable a more comprehensive representation of refusal behavior, highlighting both its potential strengths and vulnerabilities for improving model safety.
Effect of perceived preprint effectiveness and research intensity on posting behaviour
Venue: arXiv cs.DL 2025
Year: 2025

Open science is increasingly recognised worldwide, with preprint posting emerging as a key strategy. This study explores the factors influencing researchers' adoption of preprint publication, particularly the perceived effectiveness of this practice and research intensity indicators such as publication and review frequency. Using open data from a comprehensive survey with 5,873 valid responses, we conducted regression analyses to control for demographic variables. Researchers' productivity, particularly the number of journal articles and books published, greatly influences the frequency of preprint deposits. The perception of the effectiveness of preprints follows this. Preprints are viewed positively in terms of early access to new research, but negatively in terms of early feedback. Demographic variables, such as gender and the type of organisation conducting the research, do not have a significant impact on the production of preprints when other factors are controlled for. However, the researcher's discipline, years of experience and geographical region generally have a moderate effect on the production of preprints. These findings highlight the motivations and barriers associated with preprint publication and provide insights into how researchers perceive the benefits and challenges of this practice within the broader context of open science.
Factor Analysis with Correlated Topic Model for Multi-Modal Data
Venue: arXiv cs.LG 2025
Year: 2025

Integrating various data modalities brings valuable insights into underlying phenomena. Multimodal factor analysis (FA) uncovers shared axes of variation underlying different simple data modalities, where each sample is represented by a vector of features. However, FA is not suited for structured data modalities, such as text or single cell sequencing data, where multiple data points are measured per each sample and exhibit a clustering structure. To overcome this challenge, we introduce FACTM, a novel, multi-view and multi-structure Bayesian model that combines FA with correlated topic modeling and is optimized using variational inference. Additionally, we introduce a method for rotating latent factors to enhance interpretability with respect to binary features. On text and video benchmarks as well as real-world music and COVID-19 datasets, we demonstrate that FACTM outperforms other methods in identifying clusters in structured data, and integrating them with simple modalities via the inference of shared, interpretable factors.
RAIR: Retrieval-Augmented Iterative Refinement for Chinese Spelling Correction
Venue: arXiv cs.CL 2025
Year: 2025

Chinese Spelling Correction (CSC) aims to detect and correct erroneous tokens in sentences. Traditional CSC focuses on equal length correction and uses pretrained language models (PLMs). While Large Language Models (LLMs) have shown remarkable success in identifying and rectifying potential errors, they often struggle with adapting to domain-specific corrections, especially when encountering terminologies in specialized domains. To address domain adaptation, we propose a \textbf{R}etrieval-\textbf{A}ugmented \textbf{I}terative \textbf{R}efinement (RAIR) framework. Our approach constructs a retrieval corpus adaptively from domain-specific training data and dictionaries, employing a fine-tuned retriever to ensure that the retriever catches the error correction pattern. We also extend equal-length into variable-length correction scenarios. Extensive experiments demonstrate that our framework outperforms current approaches in domain spelling correction and significantly improves the performance of LLMs in variable-length scenarios.
GPU accelerated program synthesis: Enumerate semantics, not syntax!
Venue: arXiv cs.PL 2025
Year: 2025

Program synthesis is an umbrella term for generating programs and logical formulae from specifications. With the remarkable performance improvements that GPUs enable for deep learning, a natural question arose: can we also implement a search-based program synthesiser on GPUs to achieve similar performance improvements? In this article we discuss our insights on this question, based on recent works~. The goal is to build a synthesiser running on GPUs which takes as input positive and negative example traces and returns a logical formula accepting the positive and rejecting the negative traces. With GPU-friendly programming techniques -- using the semantics of formulae to minimise data movement and reduce data-dependent branching -- our synthesiser scales to significantly larger synthesis problems, and operates much faster than the previous CPU-based state-of-the-art. We believe the insights that make our approach GPU-friendly have wide potential for enhancing the performance of other formal methods (FM) workloads.