OpenRaiser/Intern-Atlas huggingface dataset:
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction Networks
Venue: arXiv cs.LG 2025
Year: 2025

The challenge in computational biology and drug discovery lies in creating comprehensive representations of proteins and molecules that capture their intrinsic properties and interactions. Traditional methods often focus on unimodal data, such as protein sequences or molecular structures, limiting their ability to capture complex biochemical relationships. This work enhances these representations by integrating biochemical reactions encompassing interactions between molecules and proteins. By leveraging reaction data alongside pre-trained embeddings from state-of-the-art protein and molecule models, we develop ReactEmbed, a novel method that creates a unified embedding space through contrastive learning. We evaluate ReactEmbed across diverse tasks, including drug-target interaction, protein-protein interaction, protein property prediction, and molecular property prediction, consistently surpassing all current state-of-the-art models. Notably, we showcase ReactEmbed's practical utility through successful implementation in lipid nanoparticle-based drug delivery, enabling zero-shot prediction of blood-brain barrier permeability for protein-nanoparticle complexes. The code and comprehensive database of reaction pairs are available for open use at \href{https://github.com/amitaysicherman/ReactEmbed}{GitHub}.
A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification
Venue: arXiv cs.CV 2025
Year: 2025

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer stages. A cautious analysis is performed to overcome overfitting issues in model performance, taking into account minimum child weight and learning rate. A set of machine learning (ML) models including XGBoost (XGB), LGBM, Adaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), CatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted. Furthermore, the correlation between features and targets is examined using the deep neural network (DNN) model and thus their capability in detecting complex patternsis established. It is argued that several ML models can be capable of classifying lung cancer stages with great accuracy. In spite of the complexity of DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic Regression excel with superior performance. The models perform better than the others in lung cancer prediction on the complete set of comparative metrics like accuracy, precision, recall, and F-1 score
Knowledge in multi-robot systems: an interplay of dynamics, computation and communication
Venue: arXiv cs.LO 2025
Year: 2025

In this paper, we provide a framework integrating distributed multi-robot systems and temporal epistemic logic. We show that continuous-discrete hybrid systems are compatible with logical models of knowledge already used in distributed computing, and demonstrate its usefulness by deriving sufficient epistemic conditions for exploration and gathering robot tasks to be solvable. We provide a separation of the physical and computational aspects of a robotic system, allowing us to decouple the problems related to each and directly use methods from control theory and distributed computing, fields that are traditionally distant in the literature. Finally, we demonstrate a novel approach for reasoning about the knowledge in multi-robot systems through a principled method of converting a switched hybrid dynamical system into a temporal-epistemic logic model, passing through an abstract state machine representation. This creates space for methods and results to be exchanged across the fields of control theory, distributed computing and temporal-epistemic logic, while reasoning about multi-robot systems.
Leveraging LLM Agents for Automated Optimization Modeling for SASP Problems: A Graph-RAG based Approach
Venue: arXiv cs.AI 2025
Year: 2025

Automated optimization modeling (AOM) has evoked considerable interest with the rapid evolution of large language models (LLMs). Existing approaches predominantly rely on prompt engineering, utilizing meticulously designed expert response chains or structured guidance. However, prompt-based techniques have failed to perform well in the sensor array signal processing (SASP) area due the lack of specific domain knowledge. To address this issue, we propose an automated modeling approach based on retrieval-augmented generation (RAG) technique, which consists of two principal components: a multi-agent (MA) structure and a graph-based RAG (Graph-RAG) process. The MA structure is tailored for the architectural AOM process, with each agent being designed based on principles of human modeling procedure. The Graph-RAG process serves to match user query with specific SASP modeling knowledge, thereby enhancing the modeling result. Results on ten classical signal processing problems demonstrate that the proposed approach (termed as MAG-RAG) outperforms several AOM benchmarks.
Structural aspects of the Student Project Allocation problem
Venue: arXiv cs.GT 2025
Year: 2025

We study the Student Project Allocation problem with lecturer preferences over Students (SPA-S), which involves the assignment of students to projects based on student preferences over projects, lecturer preferences over students, and capacity constraints on both projects and lecturers. The goal is to find a stable matching that ensures no student and lecturer can mutually benefit by deviating from a given assignment to form an alternative arrangement involving some project. We explore the structural properties of SPA-S and characterise the set of stable matchings for an arbitrary SPA-S instance. We prove that, similar to the classical Stable Marriage problem (SM) and the Hospital Residents problem (HR), the set of all stable matchings in SPA-S forms a distributive lattice. In this lattice, the student-optimal and lecturer-optimal stable matchings represent the minimum and maximum elements, respectively. Finally, we introduce meta-rotations in the SPA-S setting using illustrations, demonstrating how they capture the relationships between stable matchings. These novel structural insights paves the way for efficient algorithms that address several open problems related to stable matchings in SPA-S.
FLASH-FHE: A Heterogeneous Architecture for Fully Homomorphic Encryption Acceleration
Venue: arXiv cs.AR 2025
Year: 2025

While many hardware accelerators have recently been proposed to address the inefficiency problem of fully homomorphic encryption (FHE) schemes, none of them is able to deliver optimal performance when facing real-world FHE workloads consisting of a mixture of shallow and deep computations, due primarily to their homogeneous design principle. This paper presents FLASH-FHE, the first FHE accelerator with a heterogeneous architecture for mixed workloads. At its heart, FLASH-FHE designs two types of computation clusters, ie, bootstrappable and swift, to optimize for deep and shallow workloads respectively in terms of cryptographic parameters and hardware pipelines. We organize one bootstrappable and two swift clusters into one cluster affiliation, and present a scheduling scheme that provides sufficient acceleration for deep FHE workloads by utilizing all the affiliations, while improving parallelism for shallow FHE workloads by assigning one shallow workload per affiliation and dynamically decomposing the bootstrappable cluster into multiple swift pipelines to accelerate the assigned workload. We further show that these two types of clusters can share valuable on-chip memory, improving performance without significant resource consumption. We implement FLASH-FHE with RTL and synthesize it using both 7nm and 14/12nm technology nodes, and our experiment results demonstrate that FLASH-FHE achieves an average performance improvement of $1.4\times$ and $11.2\times$ compared to state-of-the-art FHE accelerators CraterLake and F1 for deep workloads, while delivering up to $8.0\times$ speedup for shallow workloads due to its heterogeneous architecture.
Proofs for Folklore Theorems on the Radon-Nikodym Derivative
Venue: arXiv cs.IT 2025
Year: 2025

In this technical report, rigorous statements and formal proofs are presented for both foundational and advanced folklore theorems on the Radon-Nikodym derivative. The cases of conditional and marginal probability measures are carefully considered, which leads to an identity involving the sum of mutual and lautum information suggesting a new interpretation for such a sum.
Degree is Important: On Evolving Homogeneous Boolean Functions
Venue: arXiv cs.NE 2025
Year: 2025

Boolean functions with good cryptographic properties like high nonlinearity and algebraic degree play an important in the security of stream and block ciphers. Such functions may be designed, for instance, by algebraic constructions or metaheuristics. This paper investigates the use of Evolutionary Algorithms (EAs) to design homogeneous bent Boolean functions, i.e., functions that are maximally nonlinear and whose algebraic normal form contains only monomials of the same degree. In our work, we evaluate three genotype encodings and four fitness functions. Our results show that while EAs manage to find quadratic homogeneous bent functions (with the best method being a GA leveraging a restricted encoding), none of the approaches result in cubic homogeneous bent functions.
o3-mini vs DeepSeek-R1: Which One is Safer?
Venue: arXiv cs.SE 2025
Year: 2025

The irruption of DeepSeek-R1 constitutes a turning point for the AI industry in general and the LLMs in particular. Its capabilities have demonstrated outstanding performance in several tasks, including creative thinking, code generation, maths and automated program repair, at apparently lower execution cost. However, LLMs must adhere to an important qualitative property, i.e., their alignment with safety and human values. A clear competitor of DeepSeek-R1 is its American counterpart, OpenAI's o3-mini model, which is expected to set high standards in terms of performance, safety and cost. In this technical report, we systematically assess the safety level of both DeepSeek-R1 (70b version) and OpenAI's o3-mini (beta version). To this end, we make use of our recently released automated safety testing tool, named ASTRAL. By leveraging this tool, we automatically and systematically generated and executed 1,260 test inputs on both models. After conducting a semi-automated assessment of the outcomes provided by both LLMs, the results indicate that DeepSeek-R1 produces significantly more unsafe responses (12%) than OpenAI's o3-mini (1.2%).
Stable Marriage: Loyalty vs. Competition
Venue: arXiv cs.GT 2025
Year: 2025

We consider the stable matching problem (e.g. between doctors and hospitals) in a one-to-one matching setting, where preferences are drawn uniformly at random. It is known that when doctors propose and the number of doctors equals the number of hospitals, then the expected rank of doctors for their match is $Θ(\log n)$, while the expected rank of the hospitals for their match is $Θ(n/\log n)$, where $n$ is the size of each side of the market. However, when adding even a single doctor, [Ashlagi, Kanoria and Leshno, 2017] show that the tables have turned: doctors have expected rank of $Θ(n/\log n)$ while hospitals have expected rank of $Θ(\log n)$. That is, (slight) competition has a much more dramatically harmful effect than the benefit of being on the proposing side. Motivated by settings where agents inflate their value for an item if it is already allocated to them (termed endowment effect), we study the case where hospitals exhibit ``loyalty". We model loyalty as a parameter $k$, where a hospital currently matched to their $\ell$th most preferred doctor accepts proposals from their $\ell-k-1$th most preferred doctors. Hospital loyalty should help doctors mitigate the harmful effect of competition, as many more outcomes are now stable. However, we show that the effect of competition is so dramatic that, even in settings with extremely high loyalty, in unbalanced markets, the expected rank of doctors already becomes $\tildeΘ(\sqrt{n})$ for loyalty $k=n-\sqrt{n}\log n=n(1-o(1))$.
Conversation Games and a Strategic View of the Turing Test
Venue: arXiv cs.AI 2025
Year: 2025

Although many game-theoretic models replicate real interactions that often rely on natural language, explicit study of games where language is central to strategic interaction remains limited. This paper introduces the \emph{conversation game}, a multi-stage, extensive-form game based on linguistic strategic interaction. We focus on a subset of the games, called verdict games. In a verdict game, two players alternate to contribute to a conversation, which is evaluated at each stage by a non-strategic judge who may render a conclusive binary verdict, or a decision to continue the dialogue. The game ends once a limit is reached or a verdict is given. We show many familiar processes, such as interrogation or a court process fall under this category. We also, show that the Turing test is an instance of verdict game, and discuss the significance of a strategic view of the Turing test in the age of advanced AI deception. We show the practical relevance of the proposed concepts by simulation experiments, and show that a strategic agent outperforms a naive agent by a high margin.
Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches
Venue: arXiv cs.CV 2025
Year: 2025

The increasing complexity of autonomous systems has amplified the need for accurate and reliable labeling of runway and taxiway markings to ensure operational safety. Precise detection and labeling of these markings are critical for tasks such as navigation, landing assistance, and ground control automation. Existing labeling algorithms, like the Automated Line Identification and Notation Algorithm (ALINA), have demonstrated success in identifying taxiway markings but encounter significant challenges when applied to runway markings. This limitation arises due to notable differences in line characteristics, environmental context, and interference from elements such as shadows, tire marks, and varying surface conditions. To address these challenges, we modified ALINA by adjusting color thresholds and refining region of interest (ROI) selection to better suit runway-specific contexts. While these modifications yielded limited improvements, the algorithm still struggled with consistent runway identification, often mislabeling elements such as the horizon or non-relevant background features. This highlighted the need for a more robust solution capable of adapting to diverse visual interferences. In this paper, we propose integrating a classification step using a Convolutional Neural Network (CNN) named AssistNet. By incorporating this classification step, the detection pipeline becomes more resilient to environmental variations and misclassifications. This work not only identifies the challenges but also outlines solutions, paving the way for improved automated labeling techniques essential for autonomous aviation systems.
The Algebra of Parity Games
Venue: arXiv cs.LO 2025
Year: 2025

In recent work, Watanabe, Eberhart, Asada, and Hasuo have shown that parity games can be seen as string diagrams, that is, as the morphisms of a symmetric monoidal category, an algebraic structure with two different operations of composition. Furthermore, they have shown that the winning regions associated to a given game can be computed functorially, i.e. compositionally. Building on their results, this paper focuses on the equational properties of parity games, giving them a sound and complete axiomatisation. The payoff is that any parity game can be solved using equational reasoning directly at the level of the string diagram that represents it. Finally, we translate the diagrammatic language of parity games to an equally expressive symbolic calculus with fixpoints, and equip it with its own equational theory.
One-Bit Distributed Mean Estimation with Unknown Variance
Venue: arXiv cs.IT 2025
Year: 2025

In this work, we study the problem of distributed mean estimation with $1$-bit communication constraints when the variance is unknown. We focus on the specific case where each user has access to one i.i.d. sample drawn from a distribution that belongs to a scale-location family, and is limited to sending just a single bit of information to a central server whose goal is to estimate the mean. We propose simple non-adaptive and adaptive protocols that are shown to be asymptotically normal. We derive bounds on the asymptotic (in the number of users) Mean Squared Error (MSE) achieved by these protocols. For a class of symmetric log-concave distributions, we derive matching lower bounds for the MSE achieved by adaptive protocols, proving the optimality of our scheme. Furthermore, we develop a lower bound on the MSE for non-adaptive protocols that applies to any symmetric strictly log-concave distribution by means of a refined squared Hellinger distance analysis. Through this, we show that for many common distributions including a subclass of the generalized Gaussian family, the asymptotic minimax MSE achieved by the best non-adaptive protocol is higher than that achieved by our simple adaptive protocol. Our simulation results confirm a positive gap between the adaptive and non-adaptive settings, aligning with the theoretical bounds.
A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre
Venue: arXiv cs.LG 2025
Year: 2025

Patient length of stay (LoS) is a critical metric for evaluating the efficacy of hospital management. The primary objectives encompass to improve efficiency and reduce costs while enhancing patient outcomes and hospital capacity within the patient journey. By seamlessly merging data-driven techniques with simulation methodologies, the study proposes an all-encompassing framework for the optimization of patient flow. Using a comprehensive dataset of 2.3 million de-identified patient records, we analyzed demographics, diagnoses, treatments, services, costs, and charges with machine learning models (Decision Tree, Logistic Regression, Random Forest, Adaboost, LightGBM) and Python tools (Spark, AWS clusters, dimensionality reduction). Our model predicts patient length of stay (LoS) upon admission using supervised learning algorithms. This hybrid approach enables the identification of key factors influencing LoS, offering a robust framework for hospitals to streamline patient flow and resource utilization. The research focuses on patient flow, corroborating the efficacy of the approach, illustrating decreased patient length of stay within a real healthcare environment. The findings underscore the potential of hybrid data-driven models in transforming hospital management practices. This innovative methodology provides generally flexible decision-making, training, and patient flow enhancement; such a system could have huge implications for healthcare administration and overall satisfaction with healthcare.
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models
Venue: arXiv cs.CV 2025
Year: 2025

In real-world scenarios, achieving domain adaptation and generalization poses significant challenges, as models must adapt to or generalize across unknown target distributions. Extending these capabilities to unseen multimodal distributions, i.e., multimodal domain adaptation and generalization, is even more challenging due to the distinct characteristics of different modalities. Significant progress has been made over the years, with applications ranging from action recognition to semantic segmentation. Besides, the recent advent of large-scale pre-trained multimodal foundation models, such as CLIP, has inspired works leveraging these models to enhance adaptation and generalization performances or adapting them to downstream tasks. This survey provides the first comprehensive review of recent advances from traditional approaches to foundation models, covering: (1) Multimodal domain adaptation; (2) Multimodal test-time adaptation; (3) Multimodal domain generalization; (4) Domain adaptation and generalization with the help of multimodal foundation models; and (5) Adaptation of multimodal foundation models. For each topic, we formally define the problem and thoroughly review existing methods. Additionally, we analyze relevant datasets and applications, highlighting open challenges and potential future research directions. We maintain an active repository that contains up-to-date literature at https://github.com/donghao51/Awesome-Multimodal-Adaptation.
Stable Andrews-Curtis trivialization of AK(3) revisited. A case study using automated deduction
Venue: arXiv cs.LO 2025
Year: 2025

Recent work by Shehper et al. (2024) demonstrated that the well-known Akbulut-Kirby AK(3) balanced presentation of the trivial group is stably AC-equivalent to the trivial presentation. This result eliminates AK(3) as a potential counterexample to the stable Andrews-Curtis conjecture. In this paper, we present an alternative proof of this result using an automated deduction approach. We provide several transformation sequences, derived from two different proofs generated by the automated theorem prover Prover9, that certify the stable AC-equivalence of AK(3) and the trivial presentation. We conclude by proposing a challenge to develop computational methods for searching stable AC-transformations.
DarkMind: Latent Chain-of-Thought Backdoor in Customized LLMs
Venue: arXiv cs.CR 2025
Year: 2025

With the rapid rise of personalized AI, customized large language models (LLMs) equipped with Chain of Thought (COT) reasoning now power millions of AI agents. However, their complex reasoning processes introduce new and largely unexplored security vulnerabilities. We present DarkMind, a novel latent reasoning level backdoor attack that targets customized LLMs by manipulating internal COT steps without altering user queries. Unlike prior prompt based attacks, DarkMind activates covertly within the reasoning chain via latent triggers, enabling adversarial behaviors without modifying input prompts or requiring access to model parameters. To achieve stealth and reliability, we propose dual trigger types instant and retrospective and integrate them within a unified embedding template that governs trigger dependent activation, employ a stealth optimization algorithm to minimize semantic drift, and introduce an automated conversation starter for covert activation across domains. Comprehensive experiments on eight reasoning datasets spanning arithmetic, commonsense, and symbolic domains, using five LLMs, demonstrate that DarkMind consistently achieves high attack success rates. We further investigate defense strategies to mitigate these risks and reveal that reasoning level backdoors represent a significant yet underexplored threat, underscoring the need for robust, reasoning aware security mechanisms.