OpenRaiser/Intern-Atlas huggingface dataset:
STL-GO: Spatio-Temporal Logic with Graph Operators for Distributed Systems with Multiple Network Topologies
Venue: arXiv cs.LO 2025
Year: 2025

Multi-agent systems (MASs) consisting of a number of autonomous agents that communicate, coordinate, and jointly sense the environment to achieve complex missions can be found in a variety of applications such as robotics, smart cities, and internet-of-things applications. Modeling and monitoring MAS requirements to guarantee overall mission objectives, safety, and reliability is an important problem. Such requirements implicitly require reasoning about diverse sensing and communication modalities between agents, analysis of the dependencies between agent tasks, and the spatial or virtual distance between agents. To capture such rich MAS requirements, we model agent interactions via multiple directed graphs, and introduce a new logic -- Spatio-Temporal Logic with Graph Operators (STL-GO). The key innovation in STL-GO are graph operators that enable us to reason about the number of agents along either the incoming or outgoing edges of the underlying interaction graph that satisfy a given property of interest; for example, the requirement that an agent should sense at least two neighboring agents whose task graphs indicate the ability to collaborate. We then propose novel distributed monitoring conditions for individual agents that use only local information to determine whether or not an STL-GO specification is satisfied. We compare the expressivity of STL-GO against existing spatio-temporal logic formalisms, and demonstrate the utility of STL-GO and our distributed monitors in a bike-sharing and a multi-drone case study.
What Level of Automation is "Good Enough"? A Benchmark of Large Language Models for Meta-Analysis Data Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Automating data extraction from full-text randomised controlled trials (RCTs) for meta-analysis remains a significant challenge. This study evaluates the practical performance of three LLMs (Gemini-2.0-flash, Grok-3, GPT-4o-mini) across tasks involving statistical results, risk-of-bias assessments, and study-level characteristics in three medical domains: hypertension, diabetes, and orthopaedics. We tested four distinct prompting strategies (basic prompting, self-reflective prompting, model ensemble, and customised prompts) to determine how to improve extraction quality. All models demonstrate high precision but consistently suffer from poor recall by omitting key information. We found that customised prompts were the most effective, boosting recall by up to 15\%. Based on this analysis, we propose a three-tiered set of guidelines for using LLMs in data extraction, matching data types to appropriate levels of automation based on task complexity and risk. Our study offers practical advice for automating data extraction in real-world meta-analyses, balancing LLM efficiency with expert oversight through targeted, task-specific automation.
Dynatune: Dynamic Tuning of Raft Election Parameters Using Network Measurement
Venue: arXiv cs.DC 2025
Year: 2025

Raft is a leader-based consensus algorithm that implements State Machine Replication (SMR), which replicates the service state across multiple servers to enhance fault tolerance. In Raft, the servers play one of three roles: leader, follower, or candidate. The leader receives client requests, determines the processing order, and replicates them to the followers. When the leader fails, the service must elect a new leader to continue processing requests, during which the service experiences an out-of-service (OTS) time. The OTS time is directly influenced by election parameters, such as heartbeat interval and election timeout. However, traditional approaches, such as Raft, often struggle to effectively tune these parameters, particularly under fluctuating network conditions, leading to increased OTS time and reduced service responsiveness. To address this, we propose Dynatune, a mechanism that dynamically adjusts Raft's election parameters based on network metrics such as round-trip time and packet loss rates measured via heartbeats. By adapting to changing network environments, Dynatune significantly reduces the leader failure detection and OTS time without altering Raft's core mechanisms or introducing additional communication overheads. Experimental results demonstrate that Dynatune reduces the leader failure detection and OTS times by 80% and 45%, respectively, compared with Raft, while maintaining high availability even under dynamic network conditions. These findings confirm that Dynatune effectively enhances the performance and reliability of SMR services in various network scenarios.
Designing User-Centric Metrics for Evaluation of Counterfactual Explanations
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning-based decision models are increasingly being used to make decisions that significantly impact people's lives, but their opaque nature leaves end users without a clear understanding of why a decision was made. Counterfactual Explanations (CFEs) have grown in popularity as a means of offering actionable guidance by identifying the minimum changes in feature values required to flip a model's prediction to something more desirable. Unfortunately, most prior research in CFEs relies on artificial evaluation metrics, such as proximity, which may overlook end-user preferences and constraints, e.g., the user's perception of effort needed to make certain feature changes may differ from that of the model designer. To address this research gap, this paper makes three novel contributions. First, we conduct a pilot study with 20 crowd-workers on Amazon MTurk to experimentally validate the alignment of existing CF evaluation metrics with real-world user preferences. Results show that user-preferred CFEs matched those based on proximity in only 63.81% of cases, highlighting the limited applicability of these metrics in real-world settings. Second, inspired by the need to design a user-informed evaluation metric for CFEs, we conduct a more detailed two-day user study with 41 participants facing realistic credit application scenarios to find experimental support for or against three intuitive hypotheses that may explain how end users evaluate CFEs. Third, based on the findings of this second study, we propose the AWP model, a novel user-centric, two-stage model that describes one possible mechanism by which users evaluate and select CFEs. Our results show that AWP predicts user-preferred CFEs with 84.37% accuracy. Our study provides the first human-centered validation for personalized cost models in CFE generation and highlights the need for adaptive, user-centered evaluation metrics.
Better Models and Algorithms for Learning Ising Models from Dynamics
Venue: arXiv cs.LG 2025
Year: 2025

We study the problem of learning the structure and parameters of the Ising model, a fundamental model of high-dimensional data, when observing the evolution of an associated Markov chain. A recent line of work has studied the natural problem of learning when observing an evolution of the well-known Glauber dynamics [Bresler, Gamarnik, Shah, IEEE Trans. Inf. Theory 2018, Gaitonde, Mossel STOC 2024], which provides an arguably more realistic generative model than the classical i.i.d. setting. However, this prior work crucially assumes that all site update attempts are observed, \emph{even when this attempt does not change the configuration}: this strong observation model is seemingly essential for these approaches. While perhaps possible in restrictive contexts, this precludes applicability to most realistic settings where we can observe \emph{only} the stochastic evolution itself, a minimal and natural assumption for any process we might hope to learn from. However, designing algorithms that succeed in this more realistic setting has remained an open problem [Bresler, Gamarnik, Shah, IEEE Trans. Inf. Theory 2018, Gaitonde, Moitra, Mossel, STOC 2025]. In this work, we give the first algorithms that efficiently learn the Ising model in this much more natural observation model that only observes when the configuration changes. For Ising models with maximum degree $d$, our algorithm recovers the underlying dependency graph in time $\mathsf{poly}(d)\cdot n^2\log n$ and then the actual parameters in additional $\widetilde{O}(2^d n)$ time, which qualitatively matches the state-of-the-art even in the i.i.d. setting in a much weaker observation model. Our analysis holds more generally for a broader class of reversible, single-site Markov chains that also includes the popular Metropolis chain by leveraging more robust properties of reversible Markov chains.
Deep Learning Framework Testing via Heuristic Guidance Based on Multiple Model Measurements
Venue: arXiv cs.SE 2025
Year: 2025

Deep learning frameworks serve as the foundation for developing and deploying deep learning applications. To enhance the quality of deep learning frameworks, researchers have proposed numerous testing methods using deep learning models as test inputs. However, existing methods predominantly measure model bug detection effectiveness as heuristic indicators, presenting three critical limitations. Firstly, existing methods fail to quantitatively measure model's operator combination variety, potentially missing critical operator combinations that could trigger framework bugs. Secondly, existing methods neglect measuring and heuristically guiding the model execution time, resulting in the omission of numerous models potential for detecting more framework bugs within limited testing time. Thirdly, existing methods overlook correlation between different model measurements, relying simply on single-indicator heuristic guidance without considering their trade-offs. To overcome these limitations, we propose DLMMM, the first deep learning framework testing method to include multiple model measurements into heuristic guidance and fuse these measurements to achieve their trade-offs. DLMMM firstly quantitatively measures model's bug detection performance, operator combination variety, and model execution time. After that, DLMMM fuses these measurements based on their correlation to achieve their trade-offs. To further enhance testing effectiveness, DLMMM designs multi-level heuristic guidance for test input model generation. We apply DLMMM to test three widely used deep learning frameworks (including TensorFlow, PyTorch, and MindSpore). The experimental results show that DLMMM outperforms state-of-the-art methods in effectiveness and efficiency.
The Labeled Coupon Collector Problem
Venue: arXiv cs.DM 2025
Year: 2025

We generalize the well-known Coupon Collector Problem (CCP) in combinatorics. Our problem is to find the minimum and expected number of draws, with replacement, required to recover $n$ distinctly labeled coupons, with each draw consisting of a random subset of $k$ different coupons and a random ordering of their associated labels. We specify two variations of the problem, Type-I in which the set of labels is known at the start, and Type-II in which the set of labels is unknown at the start. We show that our problem can be viewed as an extension of the separating system problem introduced by Rényi and Katona, provide a full characterization of the minimum, and provide a numerical approach to finding the expectation using a Markov chain model, with special attention given to the case where two coupons are drawn at a time.
How Does Empirical Research Facilitate Creation Tool Design? A Data Video Perspective
Venue: arXiv cs.HC 2025
Year: 2025

Empirical research in creative design deepens our theoretical understanding of design principles and perceptual effects, offering valuable guidance for innovating creation tools. However, how these empirical insights currently influence the development of creation tools, and how their integration can be enhanced in the future, remains insufficiently understood. In this paper, we aim to unveil the gap through a case study on data videos, a prominent and wide-spread medium for effective data storytelling. To achieve the goal, we conducted a comprehensive analysis of 46 empirical research papers and 48 creation tool papers on data video, complemented by interviews with 11 experts. Building upon a systematic collection and structured characterization of empirical research by their methodologies (e.g., corpus analysis, comparative evaluations) and component focus (e.g., visuals, motions, narratives, audio), we conducted a context-aware citation analysis and revealed a taxonomy of recurring patterns in how empirical findings inform tool design across citation functions (e.g., problem framing, technical reference). Expert interviews further uncovered researchers' practice patterns in applying empirical findings (e.g., adaptation, synthesis, iteration, etc.) and identified key factors influencing applicability, such as contextual relevance, granularity matching, clarity, credibility, and feasibility. Finally, we derive suggestions and discuss future opportunities to foster closer mutual engagement between empirical and tool research, aiming to reinforce the theoretical grounding of creation tools and enhance the practical impact of empirical research.
A Novel Self-Evolution Framework for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

The capabilities of Large Language Models (LLMs) are limited to some extent by pre-training, so some researchers optimize LLMs through post-training. Existing post-training strategies, such as memory-based retrieval or preference optimization, improve user alignment yet fail to enhance the model's domain cognition. To bridge this gap, we propose a novel Dual-Phase Self-Evolution (DPSE) framework that jointly optimizes user preference adaptation and domain-specific competence. DPSE introduces a Censor module to extract multi-dimensional interaction signals and estimate satisfaction scores, which guide structured data expansion via topic-aware and preference-driven strategies. These expanded datasets support a two-stage fine-tuning pipeline: supervised domain grounding followed by frequency-aware preference optimization. Experiments across general NLP benchmarks and long-term dialogue tasks demonstrate that DPSE consistently outperforms Supervised Fine-Tuning, Preference Optimization, and Memory-Augmented baselines. Ablation studies validate the contribution of each module. In this way, our framework provides an autonomous path toward continual self-evolution of LLMs.
Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems
Venue: arXiv cs.SE 2025
Year: 2025

The emergence of the tool agent paradigm has broadened the capability boundaries of the Large Language Model (LLM), enabling it to complete more complex tasks. However, the effectiveness of this paradigm is limited due to the issue of parameter failure during its execution. To explore this phenomenon and propose corresponding suggestions, we first construct a parameter failure taxonomy in this paper. We derive five failure categories from the invocation chain of a mainstream tool agent. Then, we explore the correlation between three different input sources and failure categories by applying 15 input perturbation methods to the input. Experimental results show that parameter name hallucination failure primarily stems from inherent LLM limitations, while issues with input sources mainly cause other failure patterns. To improve the reliability and effectiveness of tool-agent interactions, we propose corresponding improvement suggestions, including standardizing tool return formats, improving error feedback mechanisms, and ensuring parameter consistency.
Minutiae-Anchored Local Dense Representation for Fingerprint Matching
Venue: arXiv cs.CV 2025
Year: 2025

Fingerprint matching under diverse capture conditions remains a fundamental challenge in biometric recognition. To achieve robust and accurate performance in such scenarios, we propose DMD, a minutiae-anchored local dense representation which captures both fine-grained ridge textures and discriminative minutiae features in a spatially structured manner. Specifically, descriptors are extracted from local patches centered and oriented on each detected minutia, forming a three-dimensional tensor, where two dimensions represent spatial locations on the fingerprint plane and the third encodes semantic features. This representation explicitly captures abstract features of local image patches, enabling a multi-level, fine-grained description that aggregates information from multiple minutiae and their surrounding ridge structures. Furthermore, thanks to its strong spatial correspondence with the patch image, DMD allows for the use of foreground segmentation masks to identify valid descriptor regions. During matching, comparisons are then restricted to overlapping foreground areas, improving efficiency and robustness. Extensive experiments on rolled, plain, parital, contactless, and latent fingerprint datasets demonstrate the effectiveness and generalizability of the proposed method. It achieves state-of-the-art accuracy across multiple benchmarks while maintaining high computational efficiency, showing strong potential for large-scale fingerprint recognition. Corresponding code is available at https://github.com/Yu-Yy/DMD.
GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing
Venue: arXiv cs.AR 2025
Year: 2025

3D Gaussian Splatting (3DGS) has emerged as a leading neural rendering technique for high-fidelity view synthesis, prompting the development of dedicated 3DGS accelerators for resource-constrained platforms. The conventional decoupled preprocessing-rendering dataflow in existing accelerators has two major limitations: 1) a significant portion of preprocessed Gaussians are not used in rendering, and 2) the same Gaussian gets repeatedly loaded across different tile renderings, resulting in substantial computational and data movement overhead. To address these issues, we propose GCC, a novel accelerator designed for fast and energy-efficient 3DGS inference. GCC introduces a novel dataflow featuring: 1) \textit{cross-stage conditional processing}, which interleaves preprocessing and rendering to dynamically skip unnecessary Gaussian preprocessing; and 2) \textit{Gaussian-wise rendering}, ensuring that all rendering operations for a given Gaussian are completed before moving to the next, thereby eliminating duplicated Gaussian loading. We also propose an alpha-based boundary identification method to derive compact and accurate Gaussian regions, thereby reducing rendering costs. We implement our GCC accelerator in 28nm technology. Extensive experiments demonstrate that GCC significantly outperforms the state-of-the-art 3DGS inference accelerator, GSCore, in both performance and energy efficiency.
A Novel Two-Dimensional Smoothing Algorithm
Venue: arXiv cs.IT 2025
Year: 2025

Smoothing and filtering two-dimensional sequences are fundamental tasks in fields such as computer vision. Conventional filtering algorithms often rely on the selection of the filtering window, limiting their applicability in certain scenarios. To this end, we propose a novel Two-Dimensional Smoothing (TDS) algorithm for the smoothing and filtering problem of two-dimensional sequences. Typically, the TDS algorithm does not require assumptions about the type of noise distribution. It is simple and easy to implement compared to conventional filtering methods, such as 2D adaptive Wiener filtering and Gaussian filtering. The TDS algorithm can effectively extract the trend contained in the two-dimensional sequence and reduce the influence of noise on the data by adjusting only a single parameter. In this work, unlike existing algorithms that depend on the filtering window, we introduce a loss function, where the trend sequence is identified as the solution when this loss function takes a minimum value. Therefore, within the framework of the TDS algorithm, a general two-dimensional sequence can be innovatively decomposed into a trend sequence and a fluctuation sequence, in which the trend sequence contains the main features of the sequence and the fluctuation sequence contains the detailed features or noise interference of the sequence. To ensure the reliability of the TDS algorithm, a crucial lemma is first established, indicating that the trend sequence and fluctuation sequence obtained by the TDS algorithm are existent and unique when the global smoothing parameter is determined. Three modified algorithms are then proposed based on the TDS algorithm, with corresponding lemmas and corollaries demonstrating their reliability. Finally, the accuracy and effectiveness of the TDS algorithm are further verified through numerical simulations and image processing cases.
Input-Driven Pushdown Automata with Translucent Input Letters
Venue: arXiv cs.FL 2025
Year: 2025

Input-driven pushdown automata with translucent input letters are investigated. Here, the use of translucent input letters means that the input is processed in several sweeps and that, depending on the current state of the automaton, some input symbols are visible and can be processed, whereas some other symbols are invisible, and may be processed in another sweep. Additionally, the returning mode as well as the non-returning mode are considered, where in the former mode a new sweep must start after processing a visible input symbol. Input-driven pushdown automata differ from traditional pushdown automata by the fact that the actions on the pushdown store (push, pop, nothing) are dictated by the input symbols. We obtain the result that the input-driven nondeterministic model is computationally stronger than the deterministic model both in the returning mode and in the non-returning mode, whereas it is known that the deterministic and the nondeterministic model are equivalent for input-driven pushdown automata without translucency. It also turns out that the non-returning model is computationally stronger than the returning model both in the deterministic and nondeterministic case. Furthermore, we investigate the closure properties of the language families introduced under the Boolean operations. We obtain a complete picture in the deterministic case, whereas in the nondeterministic case the language families are shown to be not closed under complementation. Finally, we look at decidability questions and obtain the non-semidecidability of the questions of universality, inclusion, equivalence, and regularity in the nondeterministic case.
On a Generalization of the Christoffel Tree: Epichristoffel Trees
Venue: arXiv cs.FL 2025
Year: 2025

Sturmian words form a family of one-sided infinite words over a binary alphabet that are obtained as a discretization of a line with an irrational slope starting from the origin. A finite version of this class of words called Christoffel words has been extensively studied for their interesting properties. It is a class of words that has a geometric and an algebraic definition, making it an intriguing topic of study for many mathematicians. Recently, a generalization of Christoffel words for an alphabet with 3 letters or more, called epichristoffel words, using episturmian morphisms has been studied, and many of the properties of Christoffel words have been shown to carry over to epichristoffel words; however, many properties are not shared by them as well. In this paper, we introduce the notion of an epichristoffel tree, which proves to be a useful tool in determining a subclass of epichristoffel words that share an important property of Christoffel words, which is the ability to factorize an epichristoffel word as a product of smaller epichristoffel words. We also use the epichristoffel tree to present some interesting results that help to better understand epichristoffel words.