OpenRaiser/Intern-Atlas huggingface dataset:
TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning for Enhancing LLMs' Social Intelligence
Venue: arXiv cs.CL 2025
Year: 2025

Recently, Large Language Models (LLMs) have made significant progress in IQ-related domains that require careful thinking, such as mathematics and coding. However, enhancing LLMs' cognitive development in social domains, particularly from a post-training perspective, remains underexplored. Recognizing that the social world follows a distinct timeline and requires a richer blend of cognitive modes (from intuitive reactions (System 1) and surface-level thinking to deliberate thinking (System 2)) than mathematics, which primarily relies on System 2 cognition (careful, step-by-step reasoning), we introduce Temporal-aware Hierarchical Cognitive Reinforcement Learning (TimeHC-RL) for enhancing LLMs' social intelligence. In our experiments, we systematically explore improving LLMs' social intelligence and validate the effectiveness of the TimeHC-RL method, through five other post-training paradigms and two test-time intervention paradigms on eight datasets with diverse data patterns. Experimental results reveal the superiority of our proposed TimeHC-RL method compared to the widely adopted System 2 RL method. It gives the 7B backbone model wings, enabling it to rival the performance of advanced models like DeepSeek-R1 and OpenAI-O3. Additionally, the systematic exploration from post-training and test-time interventions perspectives to improve LLMs' social intelligence has uncovered several valuable insights.
Bi-SamplerZ: A Hardware-Efficient Gaussian Sampler Architecture for Quantum-Resistant Falcon Signatures
Venue: arXiv cs.AR 2025
Year: 2025

FALCON is a standardized quantum-resistant digital signature scheme that offers advantages over other schemes, but features more complex signature generation process. This paper presents Bi-Samplerz, a fully hardware-implemented, high-efficiency dual-path discrete Gaussian sampler designed to accelerate Falcon signature generation. Observing that the SamplerZ subroutine is consistently invoked in pairs during each signature generation, we propose a dual-datapath architecture capable of generating two sampling results simultaneously. To make the best use of coefficient correlation and the inherent properties of rejection sampling, we introduce an assistance mechanism that enables effective collaboration between the two datapaths, rather than simply duplicating the sampling process. Additionally, we incorporate several architectural optimizations over existing designs to further enhance speed, area efficiency, and resource utilization. Experimental results demonstrate that Bi-SamplerZ achieves the lowest sampling latency to date among existing designs, benefiting from fine-grained pipeline optimization and efficient control coordination. Compared with the state-of-the-art full hardware implementations, Bi-SamplerZ reduces the sampling cycle count by 54.1\% while incurring only a moderate increase in hardware resource consumption, thereby achieving the best-known area-time product (ATP) for fully hardware-based sampler designs. In addition, to facilitate comparison with existing works, we provide both ASIC and FPGA implementations. Together, these results highlight the suitability of Bi-SamplerZ as a high-performance sampling engine in standardized post-quantum cryptographic systems such as Falcon.
Transformers Are Universally Consistent
Venue: arXiv cs.LG 2025
Year: 2025

Despite their central role in the success of foundational models and large-scale language modeling, the theoretical foundations governing the operation of Transformers remain only partially understood. Contemporary research has largely focused on their representational capacity for language comprehension and their prowess in in-context learning, frequently under idealized assumptions such as linearized attention mechanisms. Initially conceived to model sequence-to-sequence transformations, a fundamental and unresolved question is whether Transformers can robustly perform functional regression over sequences of input tokens. This question assumes heightened importance given the inherently non-Euclidean geometry underlying real-world data distributions. In this work, we establish that Transformers equipped with softmax-based nonlinear attention are uniformly consistent when tasked with executing Ordinary Least Squares (OLS) regression, provided both the inputs and outputs are embedded in hyperbolic space. We derive deterministic upper bounds on the empirical error which, in the asymptotic regime, decay at a provable rate of $\mathcal{O}(t^{-1/2d})$, where $t$ denotes the number of input tokens and $d$ the embedding dimensionality. Notably, our analysis subsumes the Euclidean setting as a special case, recovering analogous convergence guarantees parameterized by the intrinsic dimensionality of the data manifold. These theoretical insights are corroborated through empirical evaluations on real-world datasets involving both continuous and categorical response variables.
Bench4KE: Benchmarking Automated Competency Question Generation
Venue: arXiv cs.CL 2025
Year: 2025

The availability of Large Language Models (LLMs) presents a unique opportunity to reinvigorate research on Knowledge Engineering (KE) automation. This trend is already evident in recent efforts developing LLM-based methods and tools for the automatic generation of Competency Questions (CQs), natural language questions used by ontology engineers to define the functional requirements of an ontology. However, the evaluation of these tools lacks standardization. This undermines the methodological rigor and hinders the replication and comparison of results. To address this gap, we introduce Bench4KE, an extensible API-based benchmarking system for KE automation. The presented release focuses on evaluating tools that generate CQs automatically. Bench4KE provides a curated gold standard consisting of CQ datasets from 17 real-world ontology engineering projects and uses a suite of similarity metrics to assess the quality of the CQs generated. We present a comparative analysis of 6 recent CQ generation systems, which are based on LLMs, establishing a baseline for future research. Bench4KE is also designed to accommodate additional KE automation tasks, such as SPARQL query generation, ontology testing and drafting. Code and datasets are publicly available under the Apache 2.0 license.
GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training
Venue: arXiv cs.CL 2025
Year: 2025

Semantic textual similarity (STS) is a critical task in natural language processing (NLP), enabling applications in retrieval, clustering, and understanding semantic relationships between texts. However, research in this area for the Arabic language remains limited due to the lack of high-quality datasets and pre-trained models. This scarcity of resources has restricted the accurate evaluation and advance of semantic similarity in Arabic text. This paper introduces General Arabic Text Embedding (GATE) models that achieve state-of-the-art performance on the Semantic Textual Similarity task within the MTEB benchmark. GATE leverages Matryoshka Representation Learning and a hybrid loss training approach with Arabic triplet datasets for Natural Language Inference, which are essential for enhancing model performance in tasks that demand fine-grained semantic understanding. GATE outperforms larger models, including OpenAI, with a 20-25% performance improvement on STS benchmarks, effectively capturing the unique semantic nuances of Arabic.
Taxonomic Networks: A Representation for Neuro-Symbolic Pairing
Venue: arXiv cs.AI 2025
Year: 2025

We introduce the concept of a \textbf{neuro-symbolic pair} -- neural and symbolic approaches that are linked through a common knowledge representation. Next, we present \textbf{taxonomic networks}, a type of discrimination network in which nodes represent hierarchically organized taxonomic concepts. Using this representation, we construct a novel neuro-symbolic pair and evaluate its performance. We show that our symbolic method learns taxonomic nets more efficiently with less data and compute, while the neural method finds higher-accuracy taxonomic nets when provided with greater resources. As a neuro-symbolic pair, these approaches can be used interchangeably based on situational needs, with seamless translation between them when necessary. This work lays the foundation for future systems that more fundamentally integrate neural and symbolic computation.
Multi-criteria Rank-based Aggregation for Explainable AI
Venue: arXiv cs.LG 2025
Year: 2025

Explainability is crucial for improving the transparency of black-box machine learning models. With the advancement of explanation methods such as LIME and SHAP, various XAI performance metrics have been developed to evaluate the quality of explanations. However, different explainers can provide contrasting explanations for the same prediction, introducing trade-offs across conflicting quality metrics. Although available aggregation approaches improve robustness, reducing explanations' variability, very limited research employed a multi-criteria decision-making approach. To address this gap, this paper introduces a multi-criteria rank-based weighted aggregation method that balances multiple quality metrics simultaneously to produce an ensemble of explanation models. Furthermore, we propose rank-based versions of existing XAI metrics (complexity, faithfulness and stability) to better evaluate ranked feature importance explanations. Extensive experiments on publicly available datasets demonstrate the robustness of the proposed model across these metrics. Comparative analyses of various multi-criteria decision-making and rank aggregation algorithms showed that TOPSIS and WSUM are the best candidates for this use case.
Harnessing Large Language Models for Scientific Novelty Detection
Venue: arXiv cs.CL 2025
Year: 2025

In an era of exponential scientific growth, identifying novel research ideas is crucial and challenging in academia. Despite potential, the lack of an appropriate benchmark dataset hinders the research of novelty detection. More importantly, simply adopting existing NLP technologies, e.g., retrieving and then cross-checking, is not a one-size-fits-all solution due to the gap between textual similarity and idea conception. In this paper, we propose to harness large language models (LLMs) for scientific novelty detection (ND), associated with two new datasets in marketing and NLP domains. To construct the considerate datasets for ND, we propose to extract closure sets of papers based on their relationship, and then summarize their main ideas based on LLMs. To capture idea conception, we propose to train a lightweight retriever by distilling the idea-level knowledge from LLMs to align ideas with similar conception, enabling efficient and accurate idea retrieval for LLM novelty detection. Experiments show our method consistently outperforms others on the proposed benchmark datasets for idea retrieval and ND tasks. Codes and data are available at https://anonymous.4open.science/r/NoveltyDetection-10FB/.
Efficient Text Encoders for Labor Market Analysis
Venue: arXiv cs.CL 2025
Year: 2025

Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skill extraction achieve strong performance, they depend on large language models (LLMs), which are computationally expensive and slow. In this paper, we propose \textbf{ConTeXT-match}, a novel contrastive learning approach with token-level attention that is well-suited for the extreme multi-label classification task of skill classification. \textbf{ConTeXT-match} significantly improves skill extraction efficiency and performance, achieving state-of-the-art results with a lightweight bi-encoder model. To support robust evaluation, we introduce \textbf{Skill-XL}, a new benchmark with exhaustive, sentence-level skill annotations that explicitly address the redundancy in the large label space. Finally, we present \textbf{JobBERT V2}, an improved job title normalization model that leverages extracted skills to produce high-quality job title representations. Experiments demonstrate that our models are efficient, accurate, and scalable, making them ideal for large-scale, real-time labor market analysis.
Intrinsic static/dynamic triboelectric pressure sensor for continuous and event-triggered control
Venue: arXiv cs.RO 2025
Year: 2025

Conventional pressure sensors often integrate two distinct mechanisms to detect static and dynamic stimuli, hindering the development of high fidelity human-machine interfaces. Here, we present an intrinsic static/dynamic triboelectric sensor (iSD Sensor) capable of reliably perceiving both continuous static pressure and transient mechanical shocks through a DC/AC signal decoupling strategy. By pairing hydrophobic expanded polytetrafluoroethylene (ePTFE) with elastic conductive sponge, a pressure-adaptive triboelectric interface is formed, where microscale and large-scale separations enable static and dynamic pressure sensing, respectively. Furthermore, by employing a charge excitation strategy, the device delivers enhanced voltage outputs over 25X in static and 15X in dynamic modes. Combined with a 3D gradient conductive sponge structure, the sensor achieves multi-region sensitivities of 34.7 V/kPa (static) and 48.4 V/kPa (dynamic) under low pressure (less than 1.8 kPa), and a detection limit as low as 6.13 Pa. By perceiving continuous static pressure and transient shocks applied by the human hand, the iSD Sensor enables robotic arm control via proportional grasping and dynamic, trigger-based sign language communication. This work advances high-sensitivity, self-powered pressure sensors toward intelligent, closed-loop human-machine interaction.
Multiple LLM Agents Debate for Equitable Cultural Alignment
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) need to adapt their predictions to diverse cultural contexts to benefit diverse communities across the world. While previous efforts have focused on single-LLM, single-turn approaches, we propose to exploit the complementary strengths of multiple LLMs to promote cultural adaptability. We introduce a Multi-Agent Debate framework, where two LLM-based agents debate over a cultural scenario and collaboratively reach a final decision. We propose two variants: one where either LLM agents exclusively debate and another where they dynamically choose between self-reflection and debate during their turns. We evaluate these approaches on 7 open-weight LLMs (and 21 LLM combinations) using the NormAd-ETI benchmark for social etiquette norms in 75 countries. Experiments show that debate improves both overall accuracy and cultural group parity over single-LLM baselines. Notably, multi-agent debate enables relatively small LLMs (7-9B) to achieve accuracies comparable to that of a much larger model (27B parameters).
Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Venue: arXiv cs.NI 2025
Year: 2025

Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, multi-institutional settings. This paper introduces a modular, domain-agnostic architecture for provenance tracking in federated environments, leveraging permissioned blockchain infrastructure to guarantee integrity, immutability, and auditability. The system supports decentralized interaction, persistent identifiers for artifact traceability, and a provenance versioning model that preserves the history of updates. Designed to interoperate with diverse scientific domains, the architecture promotes transparency, accountability, and reproducibility across organizational boundaries. Ongoing work focuses on validating the system through a distributed prototype and exploring its performance in collaborative settings.
Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms
Venue: arXiv cs.LG 2025
Year: 2025

Canonical algorithms for multi-armed bandits typically assume a stationary reward environment where the size of the action space (number of arms) is small. More recently developed methods typically relax only one of these assumptions: existing non-stationary bandit policies are designed for a small number of arms, while Lipschitz, linear, and Gaussian process bandit policies are designed to handle a large (or infinite) number of arms in stationary reward environments under constraints on the reward function. In this manuscript, we propose a novel policy to learn reward environments over a continuous space using Gaussian interpolation. We show that our method efficiently learns continuous Lipschitz reward functions with $\mathcal{O}^*(\sqrt{T})$ cumulative regret. Furthermore, our method naturally extends to non-stationary problems with a simple modification. We finally demonstrate that our method is computationally favorable (100-10000x faster) and experimentally outperforms sliding Gaussian process policies on datasets with non-stationarity and an extremely large number of arms.
Next Generation Authentication for Data Spaces: An Authentication Flow Based On Grant Negotiation And Authorization Protocol For Verifiable Presentations (GNAP4VP)
Venue: arXiv cs.CR 2025
Year: 2025

Identity verification in Data Spaces is a fundamental aspect of ensuring security and privacy in digital environments. This paper presents an identity verification protocol tailored for shared data environments within Data Spaces. This protocol extends the Grant Negotiation and Authorization Protocol (GNAP) and integrates OpenID Connect for Verifiable Presentations (OIDC4VP) along with support for Linked Verifiable Presentations (LVP), providing a robust foundation for secure and privacy-preserving interactions. The proposed solution adheres to the principles of Self-Sovereign Identity (SSI) to facilitate decentralized, user-centric identity management while maintaining flexibility through protocol negotiation. Two alternative interaction flows are introduced: a "Wallet-Driven Interaction" utilizing OIDC4VP, and a "LVP Authorization" model for fully automated machine-to-machine communication. These flows address critical challenges encountered in Data Spaces, including privacy, interoperability, and regulatory compliance while simultaneously ensuring scalability and minimizing trust assumptions. The paper provides a detailed technical design, outlining the implementation considerations, and demonstrating how the proposed flows guarantee verifiable, secure, and efficient interactions between participants. This work contributes towards the establishment of a more trustworthy and sovereign digital infrastructure, in alignment with emerging European data governance initiatives.
On Symmetric Losses for Robust Policy Optimization with Noisy Preferences
Venue: arXiv cs.LG 2025
Year: 2025

Optimizing policies based on human preferences is key to aligning language models with human intent. This work focuses on reward modeling, a core component in reinforcement learning from human feedback (RLHF), and offline preference optimization, such as direct preference optimization. Conventional approaches typically assume accurate annotations. However, real-world preference data often contains noise due to human errors or biases. We propose a principled framework for robust policy optimization under noisy preferences, viewing reward modeling as a classification problem. This allows us to leverage symmetric losses, known for their robustness to label noise in classification, leading to our Symmetric Preference Optimization (SymPO) method. We prove that symmetric losses enable successful policy optimization even under noisy labels, as the resulting reward remains rank-preserving -- a property sufficient for policy improvement. Experiments on synthetic and real-world tasks demonstrate the effectiveness of SymPO.
Robust Federated Learning against Model Perturbation in Edge Networks
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) is a promising paradigm for realizing edge intelligence, allowing collaborative learning among distributed edge devices by sharing models instead of raw data. However, the shared models are often assumed to be ideal, which would be inevitably violated in practice due to various perturbations, leading to significant performance degradation. To overcome this challenge, we propose a novel method, termed Sharpness-Aware Minimization-based Robust Federated Learning (SMRFL), which aims to improve model robustness against perturbations by exploring the geometrical property of the model landscape. Specifically, SMRFL solves a min-max optimization problem that promotes model convergence towards a flat minimum by minimizing the maximum loss within a neighborhood of the model parameters. In this way, model sensitivity to perturbations is reduced, and robustness is enhanced since models in the neighborhood of the flat minimum also enjoy low loss values. The theoretical result proves that SMRFL can converge at the same rate as FL without perturbations. Extensive experimental results show that SMRFL significantly enhances robustness against perturbations compared to three baseline methods on two real-world datasets under three perturbation scenarios.
Authentication and authorization in Data Spaces: A relationship-based access control approach for policy specification based on ODRL
Venue: arXiv cs.CR 2025
Year: 2025

Data has become a crucial resource in the digital economy, fostering initiatives for secure and sovereign data sharing frameworks such as Data Spaces. However, these distributed environments require fine-grained access control mechanisms that balance openness with sovereignty and security. This paper proposes an extension of the Open Digital Rights Language (ODRL) standard, the ODRL Data Spaces (ODS) profile, aimed at supporting authorization and complementing existing authentication mechanisms throughout the data lifecycle. Additionally, a policy execution engine is introduced to translate ODRL policies into executable formats, enabling effective enforcement. The approach is validated through a use case involving OpenFGA, demonstrating its applicability to relationship-based access control scenarios.
REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
Venue: arXiv cs.LG 2025
Year: 2025

We introduce Reasoning Gym (RG), a library of reasoning environments for reinforcement learning with verifiable rewards. It provides over 100 data generators and verifiers spanning multiple domains including algebra, arithmetic, computation, cognition, geometry, graph theory, logic, and various common games. Its key innovation is the ability to generate virtually infinite training data with adjustable complexity, unlike most previous reasoning datasets, which are typically fixed. This procedural generation approach allows for continuous evaluation across varying difficulty levels. Our experimental results demonstrate the efficacy of RG in both evaluating and reinforcement learning of reasoning models.