OpenRaiser/Intern-Atlas huggingface dataset:
Scheduling Coflows for Minimizing the Maximum Completion Time in Heterogeneous Parallel Networks
Venue: arXiv cs.DS 2025
Year: 2025

Coflow represents a network abstraction that models communication patterns within data centers. Scheduling coflows is a significant issue in large data center environments and is classified as an $\mathcal{NP}$-hard problem. This paper focuses on the scheduling of coflows in heterogeneous parallel networks, which are characterized by architectures that feature multiple network cores operating simultaneously. We introduce two pseudo-polynomial-time algorithms and two polynomial-time approximation algorithms aimed at minimizing the maximum completion time, known as makespan, in these heterogeneous parallel networks. Our approach includes a randomized algorithm with an expected approximation ratio of 1.5. Building on this, we present a deterministic algorithm that employs derandomization techniques, offering a performance guarantee of $1.5 + \frac{1}{2 \cdot LB}$, where $LB$ is the lower bound of the makespan for each instance. To tackle concerns regarding time complexity, we implement an exponential partitioning of time intervals and propose a randomized algorithm with an expected approximation ratio of $1.5 + ε$ in polynomial time, where $ε>0$. Furthermore, we develop a deterministic algorithm with a performance guarantee of $1.5+\frac{1}{2\cdot LB}+ε$, also within polynomial time. When the flow size is sufficiently large, this algorithm can achieve an approximation ratio of $1.5+ε$. These advancements significantly improve the best-known approximation ratio, previously $2+ε$.
A Study of In-Context-Learning-Based Text-to-SQL Errors
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have been adopted to perform text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into structured query language (SQL). However, such a technique faces correctness problems and requires efficient repairing solutions. In this paper, we conduct the first comprehensive study of text-to-SQL errors. Our study covers four representative ICL-based techniques, five basic repairing methods, two benchmarks, and two LLM settings. We find that text-to-SQL errors are widespread and summarize 29 error types of 7 categories. We also find that existing repairing attempts have limited correctness improvement at the cost of high computational overhead with many mis-repairs. Based on the findings, we propose MapleRepair, a novel text-to-SQL error detection and repairing framework. The evaluation demonstrates that MapleRepair outperforms existing solutions by repairing 13.8% more queries with neglectable mis-repairs and 67.4% less overhead.
Modeling Language for Scenario Development of Autonomous Driving Systems
Venue: arXiv cs.SE 2025
Year: 2025

Autonomous driving systems are typically verified based on scenarios. To represent the positions and movements of cars in these scenarios, diagrams that utilize icons are typically employed. However, the interpretation of such diagrams is typically ambiguous, which can lead to misunderstandings among users, making them unsuitable for the development of high-reliability systems. To address this issue, this study introduces a notation called the car position diagram (CPD). The CPD allows for the concise representation of numerous scenarios and is particularly suitable for scenario analysis and design. In addition, we propose a method for converting CPD-based models into propositional logic formulas and enumerating all scenarios using a SAT solver. A tool for scenario enumeration is implemented, and experiments are conducted on both typical car behaviors and international standards. The results demonstrate that the CPD enables the concise description of numerous scenarios, thereby confirming the effectiveness of our scenario analysis method.
On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression
Venue: arXiv cs.LG 2025
Year: 2025

In real-world sequential decision making tasks like autonomous driving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitation, classification, and clustering. For example, self-driving cars must replicate human driving behaviors, while robots and healthcare systems benefit from modeling decision sequences, whether or not they come from expert data. Existing trajectory encoding methods often focus on specific tasks or rely on reward signals, limiting their ability to generalize across domains and tasks. Inspired by the success of embedding models like CLIP and BERT in static domains, we propose a novel method for embedding state-action trajectories into a latent space that captures the skills and competencies in the dynamic underlying decision-making processes. This method operates without the need for reward labels, enabling better generalization across diverse domains and tasks. Our contributions are threefold: (1) We introduce a trajectory embedding approach that captures multiple abilities from state-action data. (2) The learned embeddings exhibit strong representational power across downstream tasks, including imitation, classification, clustering, and regression. (3) The embeddings demonstrate unique properties, such as controlling agent behaviors in IQ-Learn and an additive structure in the latent space. Experimental results confirm that our method outperforms traditional approaches, offering more flexible and powerful trajectory representations for various applications. Our code is available at https://github.com/Erasmo1015/vte.
Identifying Information from Observations with Uncertainty and Novelty
Venue: arXiv cs.LG 2025
Year: 2025

A machine learning tasks from observations must encounter and process uncertainty and novelty, especially when it is to maintain performance when observing new information and to choose the hypothesis that best fits the current observations. In this context, some key questions arise: what and how much information did the observations provide, how much information is required to identify the data-generating process, how many observations remain to get that information, and how does a predictor determine that it has observed novel information? This paper strengthens existing answers to these questions by formalizing the notion of identifiable information that arises from the language used to express the relationship between distinct states. Model identifiability and sample complexity are defined via computation of an indicator function over a set of hypotheses, bridging algorithmic and probabilistic information. Their properties and asymptotic statistics are described for data-generating processes ranging from deterministic processes to ergodic stationary stochastic processes. This connects the notion of identifying information in finite steps with asymptotic statistics and PAC-learning. The indicator function's computation naturally formalizes novel information and its identification from observations with respect to a hypothesis set. We also proved that computable PAC-Bayes learners' sample complexity distribution is determined by its moments in terms of the prior probability distribution over a fixed finite hypothesis set.
Robust UAV Path Planning with Obstacle Avoidance for Emergency Rescue
Venue: arXiv cs.RO 2025
Year: 2025

The unmanned aerial vehicles (UAVs) are efficient tools for diverse tasks such as electronic reconnaissance, agricultural operations and disaster relief. In the complex three-dimensional (3D) environments, the path planning with obstacle avoidance for UAVs is a significant issue for security assurance. In this paper, we construct a comprehensive 3D scenario with obstacles and no-fly zones for dynamic UAV trajectory. Moreover, a novel artificial potential field algorithm coupled with simulated annealing (APF-SA) is proposed to tackle the robust path planning problem. APF-SA modifies the attractive and repulsive potential functions and leverages simulated annealing to escape local minimum and converge to globally optimal solutions. Simulation results demonstrate that the effectiveness of APF-SA, enabling efficient autonomous path planning for UAVs with obstacle avoidance.
Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals
Venue: arXiv cs.CV 2025
Year: 2025

This paper studies the brave new idea for Multimedia community, and proposes a novel framework to convert dreams into coherent video narratives using fMRI data. Essentially, dreams have intrigued humanity for centuries, offering glimpses into our subconscious minds. Recent advancements in brain imaging, particularly functional magnetic resonance imaging (fMRI), have provided new ways to explore the neural basis of dreaming. By combining subjective dream experiences with objective neurophysiological data, we aim to understand the visual aspects of dreams and create complete video narratives. Our process involves three main steps: reconstructing visual perception, decoding dream imagery, and integrating dream stories. Using innovative techniques in fMRI analysis and language modeling, we seek to push the boundaries of dream research and gain deeper insights into visual experiences during sleep. This technical report introduces a novel approach to visually decoding dreams using fMRI signals and weaving dream visuals into narratives using language models. We gather a dataset of dreams along with descriptions to assess the effectiveness of our framework.
A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining
Venue: arXiv cs.IR 2025
Year: 2025

This paper introduces an intelligent baggage item recommendation system to optimize packing for air travelers by providing tailored suggestions based on specific travel needs and destinations. Using FastText word embeddings and Association Rule Mining (ARM), the system ensures efficient luggage space utilization, compliance with weight limits, and an enhanced travel experience. The methodology comprises four phases: (1) data collection and preprocessing with pre-trained FastText embeddings for text representation and similarity scoring (2) a content-based recommendation system enriched by user search history (3) application of ARM to user interactions to uncover meaningful item associations and (4) integration of FastText and ARM for accurate, personalized recommendations. Performance is evaluated using metrics such as coverage, support, confidence, lift, leverage, and conviction. Results demonstrate the system's effectiveness in providing relevant suggestions, improving customer satisfaction, and simplifying the packing process. These insights advance personalized recommendations, targeted marketing, and product optimization in air travel and beyond.
SVIA: A Street View Image Anonymization Framework for Self-Driving Applications
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, there has been an increasing interest in image anonymization, particularly focusing on the de-identification of faces and individuals. However, for self-driving applications, merely de-identifying faces and individuals might not provide sufficient privacy protection since street views like vehicles and buildings can still disclose locations, trajectories, and other sensitive information. Therefore, it remains crucial to extend anonymization techniques to street view images to fully preserve the privacy of users, pedestrians, and vehicles. In this paper, we propose a Street View Image Anonymization (SVIA) framework for self-driving applications. The SVIA framework consists of three integral components: a semantic segmenter to segment an input image into functional regions, an inpainter to generate alternatives to privacy-sensitive regions, and a harmonizer to seamlessly stitch modified regions to guarantee visual coherence. Compared to existing methods, SVIA achieves a much better trade-off between image generation quality and privacy protection, as evidenced by experimental results for five common metrics on two widely used public datasets.
AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling
Venue: arXiv cs.CL 2025
Year: 2025

Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential solution for those hesitant to seek help due to feelings of shame. Cognitive Behavioral Therapy (CBT) is an essential and widely used approach in psychological counseling. The advent of large language models (LLMs) and agent technology enables automatic CBT diagnosis and treatment. However, current LLM-based CBT systems use agents with a fixed structure, limiting their self-optimization capabilities, or providing hollow, unhelpful suggestions due to redundant response patterns. In this work, we utilize Quora-like and YiXinLi single-round consultation models to build a general agent framework that generates high-quality responses for single-turn psychological consultation scenarios. We use a bilingual dataset to evaluate the quality of single-response consultations generated by each framework. Then, we incorporate dynamic routing and supervisory mechanisms inspired by real psychological counseling to construct a CBT-oriented autonomous multi-agent framework, demonstrating its general applicability. Experimental results indicate that AutoCBT can provide higher-quality automated psychological counseling services.
A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy
Venue: arXiv cs.AI 2025
Year: 2025

While large language models (LLMs) present significant potential for supporting numerous real-world applications and delivering positive social impacts, they still face significant challenges in terms of the inherent risk of privacy leakage, hallucinated outputs, and value misalignment, and can be maliciously used for generating toxic content and unethical purposes after been jailbroken. Therefore, in this survey, we present a comprehensive review of recent advancements aimed at mitigating these issues, organized across the four phases of LLM development and usage: data collecting and pre-training, fine-tuning and alignment, prompting and reasoning, and post-processing and auditing. We elaborate on the recent advances for enhancing the performance of LLMs in terms of privacy protection, hallucination reduction, value alignment, toxicity elimination, and jailbreak defenses. In contrast to previous surveys that focus on a single dimension of responsible LLMs, this survey presents a unified framework that encompasses these diverse dimensions, providing a comprehensive view of enhancing LLMs to better serve real-world applications.
CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
Venue: arXiv cs.CV 2025
Year: 2025

The synthesis of high-quality 3D assets from textual or visual inputs has become a central objective in modern generative modeling. Despite the proliferation of 3D generation algorithms, they frequently grapple with challenges such as multi-view inconsistency, slow generation times, low fidelity, and surface reconstruction problems. While some studies have addressed some of these issues, a comprehensive solution remains elusive. In this paper, we introduce \textbf{CaPa}, a carve-and-paint framework that generates high-fidelity 3D assets efficiently. CaPa employs a two-stage process, decoupling geometry generation from texture synthesis. Initially, a 3D latent diffusion model generates geometry guided by multi-view inputs, ensuring structural consistency across perspectives. Subsequently, leveraging a novel, model-agnostic Spatially Decoupled Attention, the framework synthesizes high-resolution textures (up to 4K) for a given geometry. Furthermore, we propose a 3D-aware occlusion inpainting algorithm that fills untextured regions, resulting in cohesive results across the entire model. This pipeline generates high-quality 3D assets in less than 30 seconds, providing ready-to-use outputs for commercial applications. Experimental results demonstrate that CaPa excels in both texture fidelity and geometric stability, establishing a new standard for practical, scalable 3D asset generation.
Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment
Venue: arXiv cs.RO 2025
Year: 2025

This article presents a comparative analysis of a mobile robot trajectories computed by various ROS-based SLAM systems. For this reason we developed a prototype of a mobile robot with common sensors: 2D lidar, a monocular and ZED stereo cameras. Then we conducted experiments in a typical office environment and collected data from all sensors, running all tested SLAM systems based on the acquired dataset. We studied the following SLAM systems: (a) 2D lidar-based: GMapping, Hector SLAM, Cartographer; (b) monocular camera-based: Large Scale Direct monocular SLAM (LSD SLAM), ORB SLAM, Direct Sparse Odometry (DSO); and (c) stereo camera-based: ZEDfu, Real-Time Appearance-Based Mapping (RTAB map), ORB SLAM, Stereo Parallel Tracking and Mapping (S-PTAM). Since all SLAM methods were tested on the same dataset we compared results for different SLAM systems with appropriate metrics, demonstrating encouraging results for lidar-based Cartographer SLAM, Monocular ORB SLAM and Stereo RTAB Map methods.
Omni-Emotion: Extending Video MLLM with Detailed Face and Audio Modeling for Multimodal Emotion Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Understanding emotions accurately is essential for fields like human-computer interaction. Due to the complexity of emotions and their multi-modal nature (e.g., emotions are influenced by facial expressions and audio), researchers have turned to using multi-modal models to understand human emotions rather than single-modality. However, current video multi-modal large language models (MLLMs) encounter difficulties in effectively integrating audio and identifying subtle facial micro-expressions. Furthermore, the lack of detailed emotion analysis datasets also limits the development of multimodal emotion analysis. To address these issues, we introduce a self-reviewed dataset and a human-reviewed dataset, comprising 24,137 coarse-grained samples and 3,500 manually annotated samples with detailed emotion annotations, respectively. These datasets allow models to learn from diverse scenarios and better generalize to real-world applications. Moreover, in addition to the audio modeling, we propose to explicitly integrate facial encoding models into the existing advanced Video MLLM, enabling the MLLM to effectively unify audio and the subtle facial cues for emotion understanding. By aligning these features within a unified space and employing instruction tuning in our proposed datasets, our Omni-Emotion achieves state-of-the-art performance in both emotion recognition and reasoning tasks.
Hardness of clique approximation for monotone circuits
Venue: arXiv cs.CC 2025
Year: 2025

We consider a problem of approximating the size of the largest clique in a graph, with a monotone circuit. Concretely, we focus on distinguishing a random Erdős-Renyi graph $\mathcal{G}_{n,p}$, with $p=n^{-\frac{2}{α-1}}$ chosen st. with high probability it does not even have an $α$-clique, from a random clique on $β$ vertices (where $α\leq β$). Using the approximation method of Razborov, Alon and Boppana showed in 1987 that as long as $\sqrtα β< n^{1-δ}/\log n$, this problem requires a monotone circuit of size $n^{Ω(δ\sqrtα)}$, implying a lower bound of $2^{\tildeΩ(n^{1/3})}$ for the exact version of the problem when $k\approx n^{2/3}$. Recently Cavalar, Kumar, and Rossman improved their result by showing the tight lower bound $n^{Ω(k)}$, in a limited range $k \leq n^{1/3}$, implying a comparable $2^{\tildeΩ(n^{1/3})}$ lower bound. We combine the ideas of Cavalar, Kumar and Rossman with the recent breakthrough results on the sunflower conjecture by Alweiss, Lovett, Wu and Zhang to show that as long as $αβ< n^{1-δ}/\log n$, any monotone circuit rejecting $\mathcal{G}_{n,p}$ while accepting a $β$-clique needs to have size at least $n^{Ω(δ^2 α)}$; this implies a stronger $2^{\tildeΩ(\sqrt{n})}$ lower bound for the unrestricted version of the problem. We complement this result with a construction of an explicit monotone circuit of size $O(n^{δ^2 α/2})$ which rejects $\mathcal{G}_{n,p}$, and accepts any graph containing $β$-clique whenever $β> n^{1-δ}$. Those two theorems explain the largest $β$-clique that can be distinguished from $\mathcal{G}_{n, 1/2}$: when $β> n / 2^{C \sqrt{\log n}}$, polynomial size circuit co do it, while for $β< n / 2^{ω(\sqrt{\log n})}$ every circuit needs size $n^{ω(1)}$.
Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation
Venue: arXiv cs.LG 2025
Year: 2025

The prediction of solar irradiance enhances reliability in photovoltaic (PV) solar plant generation and grid integration. In Colombia, PV plants face penalties if energy production deviates beyond governmental thresholds from intraday market offers. This research employs Long Short-Term Memory (LSTM) and Bidirectional-LSTM (Bi-LSTM) models, utilizing meteorological data from a PV plant in El Paso, Cesar, Colombia, to predict solar irradiance with a 6-hour horizon and 10-minute resolution. While Bi-LSTM showed superior performance, the LSTM model achieved comparable results with significantly reduced training time (6 hours versus 18 hours), making it computationally advantageous. The LSTM predictions were averaged to create an hourly resolution model, evaluated using Mean Absolute Error, Root-Mean-Square Error, Normalized Root-Mean-Square Error, and Mean Absolute Percentage Error metrics. Comparison with the Global Forecast System (GFS) revealed similar performance, with both models effectively capturing daily solar irradiance patterns. The forecast model integrates with an Object-Oriented power production model, enabling accurate energy offers in the intraday market while minimizing penalty costs.
On a Variant of the Minimum Path Cover Problem in Acyclic Digraphs: Computational Complexity Results and Exact Method
Venue: arXiv cs.DM 2025
Year: 2025

The Minimum Path Cover (MPC) problem consists of finding a minimum-cardinality set of node-disjoint paths that cover all nodes in a given graph. We explore a variant of the MPC problem on acyclic digraphs (DAGs) where, given a subset of arcs, each path within the MPC should contain at least one arc from this subset. We prove that the feasibility problem is strongly NP-hard on arbitrary DAGs, but the problem can be solved in polynomial time when the DAG is the transitive closure of a path. Given that the problem may not always be feasible, our solution focuses on covering a maximum number of nodes with a minimum number of node-disjoint paths, such that each path includes at least one arc from the predefined subset of arcs. This paper introduces and investigates two integer programming formulations for this problem. We propose several valid inequalities to enhance the linear programming relaxations, employing them as cutting planes in a branch-and-cut approach. The procedure is implemented and tested on a wide range of instances, including real-world instances derived from an airline crew scheduling problem, demonstrating the effectiveness of the proposed approach.