OpenRaiser/Intern-Atlas huggingface dataset:
Trust-Enabled Privacy: Social Media Designs to Support Adolescent User Boundary Regulation
Venue: arXiv cs.HC 2025
Year: 2025

Adolescents heavily rely on social media to build and maintain close relationships, yet current platform designs often make self-disclosure feel risky or uncomfortable. Through a three-part study involving 19 teens aged 13-18, we identify key barriers to meaningful self-disclosure on social media. Our findings reveal that while these adolescents seek casual, frequent sharing to strengthen relationships, existing platform norms often discourage such interactions. Based on our co-design interview findings, we propose platform design ideas to foster a more dynamic and nuanced privacy experience for teen social media users. We then introduce \textbf{\textit{trust-enabled privacy}} as a framework that recognizes trust -- whether building or eroding -- as central to boundary regulation, and foregrounds the role of platform design in shaping the very norms and interaction patterns that influence how trust unfolds. When trust is supported, boundary regulation becomes more adaptive and empowering; when it erodes, users resort to self-censorship or disengagement. This work provides empirical insights and actionable guidelines for designing social media spaces where teens feel empowered to engage in meaningful relationship-building processes.
Software demodulation of weak radio signals using convolutional neural network
Venue: arXiv cs.LG 2025
Year: 2025

In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals.
Random Similarity Isolation Forests
Venue: arXiv cs.LG 2025
Year: 2025

With predictive models becoming prevalent, companies are expanding the types of data they gather. As a result, the collected datasets consist not only of simple numerical features but also more complex objects such as time series, images, or graphs. Such multi-modal data have the potential to improve performance in predictive tasks like outlier detection, where the goal is to identify objects deviating from the main data distribution. However, current outlier detection algorithms are dedicated to individual types of data. Consequently, working with mixed types of data requires either fusing multiple data-specific models or transforming all of the representations into a single format, both of which can hinder predictive performance. In this paper, we propose a multi-modal outlier detection algorithm called Random Similarity Isolation Forest. Our method combines the notions of isolation and similarity-based projection to handle datasets with mixtures of features of arbitrary data types. Experiments performed on 47 benchmark datasets demonstrate that Random Similarity Isolation Forest outperforms five state-of-the-art competitors. Our study shows that the use of multiple modalities can indeed improve the detection of anomalies and highlights the need for new outlier detection benchmarks tailored for multi-modal algorithms.
Voting or Consensus? Decision-Making in Multi-Agent Debate
Venue: arXiv cs.MA 2025
Year: 2025

Much of the success of multi-agent debates depends on carefully choosing the right parameters. The decision-making protocol stands out as it can highly impact final model answers, depending on how decisions are reached. Systematic comparison of decision protocols is difficult because many studies alter multiple discussion parameters beyond the protocol. So far, it has been largely unknown how decision-making influences different tasks. This work systematically evaluates the impact of seven decision protocols (e.g., majority voting, unanimity consensus). We change only one variable at a time - the decision protocol - to analyze how different methods affect the collaboration between agents and measure differences in knowledge and reasoning tasks. Our results show that voting protocols improve performance by 13.2% in reasoning tasks and consensus protocols by 2.8% in knowledge tasks compared to other decision protocols. Increasing the number of agents improves performance, while more discussion rounds before voting reduce it. To improve decision-making by increasing answer diversity, we propose two new methods, All-Agents Drafting (AAD) and Collective Improvement (CI). Our methods improve task performance by up to 3.3% with AAD and up to 7.4% with CI. This work demonstrates the importance of decision-making in multi-agent debates beyond scaling.
A Categorical Unification for Multi-Model Data: Part I Categorical Model and Normal Forms
Venue: arXiv cs.DB 2025
Year: 2025

Modern database systems face a significant challenge in effectively handling the Variety of data. The primary objective of this paper is to establish a unified data model and theoretical framework for multi-model data management. To achieve this, we present a categorical framework to unify three types of structured or semi-structured data: relation, XML, and graph-structured data. Utilizing the language of category theory, our framework offers a sound formal abstraction for representing these diverse data types. We extend the Entity-Relationship (ER) diagram with enriched semantic constraints, incorporating categorical ingredients such as pullback, pushout and limit. Furthermore, we develop a categorical normal form theory which is applied to category data to reduce redundancy and facilitate data maintenance. Those normal forms are applicable to relation, XML and graph data simultaneously, thereby eliminating the need for ad-hoc, model-specific definitions as found in separated normal form theories before. Finally, we discuss the connections between this new normal form framework and Boyce-Codd normal form, fourth normal form, and XML normal form.
DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition
Venue: arXiv cs.HC 2025
Year: 2025

Decomposition is a fundamental skill in algorithmic programming, requiring learners to break down complex problems into smaller, manageable parts. However, current self-study methods, such as browsing reference solutions or using LLM assistants, often provide excessive or generic assistance that misaligns with learners' decomposition strategies, hindering independent problem-solving and critical thinking. To address this, we introduce Decomposition Box (DBox), an interactive LLM-based system that scaffolds and adapts to learners' personalized construction of a step tree through a "learner-LLM co-decomposition" approach, providing tailored support at an appropriate level. A within-subjects study (N=24) found that compared to the baseline, DBox significantly improved learning gains, cognitive engagement, and critical thinking. Learners also reported a stronger sense of achievement and found the assistance appropriate and helpful for learning. Additionally, we examined DBox's impact on cognitive load, identified usage patterns, and analyzed learners' strategies for managing system errors. We conclude with design implications for future AI-powered tools to better support algorithmic programming education.
Language-Parametric Reference Synthesis (Extended)
Venue: arXiv cs.PL 2025
Year: 2025

Modern Integrated Development Environments (IDEs) offer automated refactorings to aid programmers in developing and maintaining software. However, implementing sound automated refactorings is challenging, as refactorings may inadvertently introduce name-binding errors or cause references to resolve to incorrect declarations. To address these issues, previous work by Schäfer et al. proposed replacing concrete references with locked references to separate binding preservation from transformation. Locked references vacuously resolve to a specific declaration, and after transformation must be replaced with concrete references that also resolve to that declaration. Synthesizing these references requires a faithful inverse of the name lookup functions of the underlying language. Manually implementing such inverse lookup functions is challenging due to the complex name-binding features in modern programming languages. Instead, we propose to automatically derive this function from type system specifications written in the Statix meta-DSL. To guide the synthesis of qualified references we use scope graphs, which represent the binding structure of a program, to infer their names and discover their syntactic structure. We evaluate our approach by synthesizing concrete references for locked references in 2528 Java, 196 ChocoPy, and 49 Featherweight Generic Java test programs. Our approach yields a principled language-parametric method for synthesizing references.
On the Byzantine Fault Tolerance of signSGD with Majority Vote
Venue: arXiv cs.LG 2025
Year: 2025

In distributed learning, sign-based compression algorithms such as signSGD with majority vote provide a lightweight alternative to SGD with an additional advantage: fault tolerance (almost) for free. However, for signSGD with majority vote, this fault tolerance has been shown to cover only the case of weaker adversaries, i.e., ones that are not omniscient or cannot collude to base their attack on common knowledge and strategy. In this work, we close this gap and provide new insights into how signSGD with majority vote can be resilient against omniscient and colluding adversaries, which craft an attack after communicating with other adversaries, thus having better information to perform the most damaging attack based on a common optimal strategy. Our core contribution is in providing a proof that begins by defining the omniscience framework and the strongest possible damage against signSGD with majority vote without imposing any restrictions on the attacker. Thanks to the filtering effect of the sign-based method, we upper-bound the space of attacks to the optimal strategy for maximizing damage by an attacker. Hence, we derive an explicit probabilistic bound in terms of incorrect aggregation without resorting to unknown constants, providing a convergence bound on signSGD with majority vote in the presence of Byzantine attackers, along with a precise convergence rate. Our findings are supported by experiments on the MNIST dataset in a distributed learning environment with adversaries of varying strength.
Universality of conformal prediction under the assumption of randomness
Venue: arXiv cs.LG 2025
Year: 2025

Conformal predictors provide set or functional predictions that are valid under the assumption of randomness, i.e., under the assumption of independent and identically distributed data. The question asked in this paper is whether there are predictors that are valid in the same sense under the assumption of randomness and that are more efficient than conformal predictors. The answer is that the class of conformal predictors is universal in that only limited gains in predictive efficiency are possible. The previous work in this area has relied on the algorithmic theory of randomness and so involved unspecified constants, whereas this paper's results are much more practical. They are also shown to be optimal in some respects.
Algorithms for Parallel Shared-Memory Sparse Matrix-Vector Multiplication on Unstructured Matrices
Venue: arXiv cs.DC 2025
Year: 2025

The sparse matrix-vector (SpMV) multiplication is an important computational kernel, but it is notoriously difficult to execute efficiently. This paper investigates algorithm performance for unstructured sparse matrices, which are more common than ever because of the trend towards large-scale data collection. The development of an SpMV multiplication algorithm for this type of data is hard due to two factors. First, parallel load balancing issues arise because of the unpredictable nonzero structure. Secondly, SpMV multiplication algorithms are inevitably memory-bound because the sparsity causes a low arithmetic intensity. Three state-of-the-art algorithms for parallel SpMV multiplication on shared-memory systems are discussed. Six new hybrid algorithms are developed which combine optimization techniques of the current algorithms. These techniques include parallelization strategies, storage formats, and nonzero orderings. A modern and high-performance implementation of all discussed algorithms is provided as open-source software. Using this implementation the algorithms are compared. Furthermore, SpMV multiplication algorithms require the matrix to be stored in a specific storage format. Therefore, the conversion time between these storage formats is also analyzed. Both tests are performed for multiple unstructured sparse matrices on different machines: two multi-CPU and two single-CPU architectures. We show that one of the newly developed algorithms outperforms the current state-of-the-art by 19% on one of the multi-CPU architectures. When taking conversion time into consideration, we show that 472 SpMV multiplications are needed to cover the cost of converting to a new storage format for one of the hybrid algorithms on a multi-CPU machine.
Equational Reasoning Modulo Commutativity in Languages with Binders (Extended Version)
Venue: arXiv cs.LO 2025
Year: 2025

Many formal languages include binders as well as operators that satisfy equational axioms, such as commutativity. Here we consider the nominal language, a general formal framework which provides support for the representation of binders, freshness conditions and $α$-renaming. Rather than relying on the usual freshness constraints, we introduce a nominal algebra which employs permutation fixed-point constraints in $α$-equivalence judgements, seamlessly integrating commutativity into the reasoning process. We establish its proof-theoretical properties and provide a sound and complete semantics in the setting of nominal sets. Additionally, we propose a novel algorithm for nominal unification modulo commutativity, which we prove terminating and correct. By leveraging fixed-point constraints, our approach ensures a finitary unification theory, unlike standard methods relying on freshness constraints. This framework offers a robust foundation for structural induction and recursion over syntax with binders and commutative operators, enabling reasoning in settings such as first-order logic and the $π$-calculus.
FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users
Venue: arXiv cs.LG 2025
Year: 2025

Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context learning capabilities of LLMs, we propose Few-Shot Preference Optimization (FSPO), which reframes reward modeling as a meta-learning problem. Under this framework, an LLM learns to quickly adapt to a user via a few labeled preferences from that user, constructing a personalized reward function for them. Additionally, since real-world preference data is scarce and challenging to collect at scale, we propose careful design choices to construct synthetic preference datasets for personalization, generating over 1M synthetic personalized preferences using publicly available LLMs. In particular, to successfully transfer from synthetic data to real users, we find it crucial for the data to exhibit both high diversity and coherent, self-consistent structure. We evaluate FSPO on personalized open-ended generation for up to 1,500 synthetic users across across three domains: movie reviews, pedagogical adaptation based on educational background, and general question answering, along with a controlled human study. Overall, FSPO achieves an 87% Alpaca Eval winrate on average in generating responses that are personalized to synthetic users and a 72% winrate with real human users in open-ended question answering.
The Simulation Semantics of Synthesisable Verilog
Venue: arXiv cs.PL 2025
Year: 2025

Despite numerous previous formalisation projects targeting Verilog, the semantics of Verilog defined by the Verilog standard -- Verilog's simulation semantics -- has thus far eluded definitive mathematical formalisation. Previous projects on formalising the semantics have made good progress but no previous project provides a formalisation that can be used to execute or formally reason about real-world hardware designs. In this paper, we show that the reason for this is that the Verilog standard is inconsistent both with Verilog practice and itself. We pinpoint a series of problems in the Verilog standard that we have identified in how the standard defines the semantics of the subset of Verilog used to describe hardware designs, that is, the synthesisable subset of Verilog. We show how the most complete Verilog formalisation to date inherits these problems and how, after we repair these problems in an executable implementation of the formalisation, the repaired implementation can be used to execute real-world hardware designs. The existing formalisation together with the repairs hence constitute the first formalisation of Verilog's simulation semantics compatible with real-world hardware designs. Additionally, to make the results of this paper accessible to a wider (nonmathematical) audience, we provide a visual formalisation of Verilog's simulation semantics.
CryptoPulse: Short-Term Cryptocurrency Forecasting with Dual-Prediction and Cross-Correlated Market Indicators
Venue: arXiv cs.LG 2025
Year: 2025

Cryptocurrencies fluctuate in markets with high price volatility, posing significant challenges for investors. To aid in informed decision-making, systems predicting cryptocurrency market movements have been developed, typically focusing on historical patterns. However, these methods often overlook three critical factors influencing market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations affecting collaborative investor behaviors; 2) overall market sentiment, heavily influenced by news impacting investor strategies; and 3) technical indicators, offering insights into overbought or oversold conditions, momentum, and market trends, which are crucial for short-term price movements. This paper proposes a dual prediction mechanism that forecasts the next day's closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Additionally, a novel refinement mechanism enhances predictions through market sentiment-based rescaling and fusion. Experiments demonstrate that the proposed model achieves state-of-the-art performance, consistently outperforming ten comparison methods.
dCMF: Learning interpretable evolving patterns from temporal multiway data
Venue: arXiv cs.LG 2025
Year: 2025

Multiway datasets are commonly analyzed using unsupervised matrix and tensor factorization methods to reveal underlying patterns. Frequently, such datasets include timestamps and could correspond to, for example, health-related measurements of subjects collected over time. The temporal dimension is inherently different from the other dimensions, requiring methods that account for its intrinsic properties. Linear Dynamical Systems (LDS) are specifically designed to capture sequential dependencies in the observed data. In this work, we bridge the gap between tensor factorizations and dynamical modeling by exploring the relationship between LDS, Coupled Matrix Factorizations (CMF) and the PARAFAC2 model. We propose a time-aware coupled factorization model called d(ynamical)CMF that constrains the temporal evolution of the latent factors to adhere to a specific LDS structure. Using synthetic datasets, we compare the performance of dCMF with PARAFAC2 and t(emporal)PARAFAC2 which incorporates temporal smoothness. Our results show that dCMF and PARAFAC2-based approaches perform similarly when capturing smoothly evolving patterns that adhere to the PARAFAC2 structure. However, dCMF outperforms alternatives when the patterns evolve smoothly but deviate from the PARAFAC2 structure. Furthermore, we demonstrate that the proposed dCMF method enables to capture more complex dynamics when additional prior information about the temporal evolution is incorporated.
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Combinatorial optimization (CO) problems arise across a broad spectrum of domains, including medicine, logistics, and manufacturing. While exact solutions are often computationally infeasible, many practical applications require high-quality solutions within a given time budget. To address this, we propose a learning-based approach that enhances existing non-learned approximation algorithms for CO. Specifically, we parameterize these approximation algorithms and train graph neural networks (GNNs) to predict parameter values that yield near-optimal solutions. Our method is trained end-to-end in a self-supervised fashion, using a novel gradient estimation scheme that treats the approximation algorithm as a black box. This approach combines the strengths of learning and traditional algorithms: the GNN learns from data to guide the algorithm toward better solutions, while the approximation algorithm ensures feasibility. We validate our method on two well-known combinatorial optimization problems: the travelling salesman problem (TSP) and the minimum k-cut problem. Our results demonstrate that the proposed approach is competitive with state-of-the-art learned CO solvers.
Learning Code-Edit Embedding to Model Student Debugging Behavior
Venue: arXiv cs.SE 2025
Year: 2025

Providing effective feedback for programming assignments in computer science education can be challenging: students solve problems by iteratively submitting code, executing it, and using limited feedback from the compiler or the auto-grader to debug. Analyzing student debugging behavior in this process may reveal important insights into their knowledge and inform better personalized support tools. In this work, we propose an encoder-decoder-based model that learns meaningful code-edit embeddings between consecutive student code submissions, to capture their debugging behavior. Our model leverages information on whether a student code submission passes each test case to fine-tune large language models (LLMs) to learn code editing representations. It enables personalized next-step code suggestions that maintain the student's coding style while improving test case correctness. Our model also enables us to analyze student code-editing patterns to uncover common student errors and debugging behaviors, using clustering techniques. Experimental results on a real-world student code submission dataset demonstrate that our model excels at code reconstruction and personalized code suggestion while revealing interesting patterns in student debugging behavior.