OpenRaiser/Intern-Atlas huggingface dataset:
HyDRA: A Hybrid-Driven Reasoning Architecture for Verifiable Knowledge Graphs
Venue: arXiv cs.LG 2025
Year: 2025

The synergy between symbolic knowledge, often represented by Knowledge Graphs (KGs), and the generative capabilities of neural networks is central to advancing neurosymbolic AI. A primary bottleneck in realizing this potential is the difficulty of automating KG construction, which faces challenges related to output reliability, consistency, and verifiability. These issues can manifest as structural inconsistencies within the generated graphs, such as the formation of disconnected $\textit{isolated islands}$ of data or the inaccurate conflation of abstract classes with specific instances. To address these challenges, we propose HyDRA, a $\textbf{Hy}$brid-$\textbf{D}$riven $\textbf{R}$easoning $\textbf{A}$rchitecture designed for verifiable KG automation. Given a domain or an initial set of documents, HyDRA first constructs an ontology via a panel of collaborative neurosymbolic agents. These agents collaboratively agree on a set of competency questions (CQs) that define the scope and requirements the ontology must be able to answer. Given these CQs, we build an ontology graph that subsequently guides the automated extraction of triplets for KG generation from arbitrary documents. Inspired by design-by-contracts (DbC) principles, our method leverages verifiable contracts as the primary control mechanism to steer the generative process of Large Language Models (LLMs). To verify the output of our approach, we extend beyond standard benchmarks and propose an evaluation framework that assesses the functional correctness of the resulting KG by leveraging symbolic verifications as described by the neurosymbolic AI framework, $\textit{SymbolicAI}$. This work contributes a hybrid-driven architecture for improving the reliability of automated KG construction and the exploration of evaluation methods for measuring the functional integrity of its output. The code is publicly available.
Does More Inference-Time Compute Really Help Robustness?
Venue: arXiv cs.AI 2025
Year: 2025

Recently, Zaremba et al. demonstrated that increasing inference-time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-scale, open-source models (e.g., DeepSeek R1, Qwen3, Phi-reasoning) can also benefit from inference-time scaling using a simple budget forcing strategy. More importantly, we reveal and critically examine an implicit assumption in prior work: intermediate reasoning steps are hidden from adversaries. By relaxing this assumption, we identify an important security risk, intuitively motivated and empirically verified as an inverse scaling law: if intermediate reasoning steps become explicitly accessible, increased inference-time computation consistently reduces model robustness. Finally, we discuss practical scenarios where models with hidden reasoning chains are still vulnerable to attacks, such as models with tool-integrated reasoning and advanced reasoning extraction attacks. Our findings collectively demonstrate that the robustness benefits of inference-time scaling depend heavily on the adversarial setting and deployment context. We urge practitioners to carefully weigh these subtle trade-offs before applying inference-time scaling in security-sensitive, real-world applications.
Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
Venue: arXiv cs.LG 2025
Year: 2025

Physics-informed neural networks (PINNs) have gained prominence in recent years and are now effectively used in a number of applications. However, their performance remains unstable due to the complex landscape of the loss function. To address this issue, we reformulate PINN training as a nonconvex-strongly concave saddle-point problem. After establishing the theoretical foundation for this approach, we conduct an extensive experimental study, evaluating its effectiveness across various tasks and architectures. Our results demonstrate that the proposed method outperforms the current state-of-the-art techniques.
Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications
Venue: arXiv cs.LG 2025
Year: 2025

We present an autoregressive end-to-end learning approach for probabilistic shaping on nonlinear fiber channels. Our proposed scheme learns the joint symbol distribution and provides a 0.3-bits/2D achievable information rate gain over an optimized marginal distribution for dual-polarized 64-QAM transmission over a single-span 205 km link.
AI, Expert or Peer? -- Examining the Impact of Perceived Feedback Source on Pre-Service Teachers Feedback Perception and Uptake
Venue: arXiv cs.HC 2025
Year: 2025

Feedback plays a central role in learning, yet pre-service teachers' engagement with feedback depends not only on its quality but also on their perception of the feedback content and source. Large Language Models (LLMs) are increasingly used to provide educational feedback; however, negative perceptions may limit their practical use, and little is known about how pre-service teachers' perceptions and behavioral responses differ by feedback source. This study investigates how the perceived source of feedback - LLM, expert, or peer - influences feedback perception and uptake, and whether recognition accuracy and feedback quality moderate these effects. In a randomized experiment with 273 pre-service teachers, participants received written feedback on a mathematics learning goal, identified its source, rated feedback perceptions across five dimensions (fairness, usefulness, acceptance, willingness to improve, positive and negative affect), and revised the learning goal according to the feedback (i.e. feedback uptake). Results revealed that LLM-generated feedback received the highest ratings in fairness and usefulness, leading to the highest uptake (52%). Recognition accuracy significantly moderated the effect of feedback source on perception, with particularly positive evaluations when LLM feedback was falsely ascribed to experts. Higher-quality feedback was consistently assigned to experts, indicating an expertise heuristic in source judgments. Regression analysis showed that only feedback quality significantly predicted feedback uptake. Findings highlight the need to address source-related biases and promote feedback and AI literacy in teacher education.
Belief Alignment vs Opinion Leadership: Understanding Cross-linguistic Digital Activism in K-pop and BLM Communities
Venue: arXiv cs.SI 2025
Year: 2025

The internet has transformed activism, giving rise to more organic, diverse, and dynamic social movements that transcend geo-political boundaries. Despite extensive research on the role of social media and the internet in cross-cultural activism, the fundamental motivations driving these global movements remain poorly understood. This study examines two plausible explanations for cross-cultural activism: first, that it is driven by influential online opinion leaders, and second, that it results from individuals resonating with emergent sets of beliefs, values, and norms. We conduct a case study of the interaction between K-pop fans and the Black Lives Matter (BLM) movement on Twitter following the murder of George Floyd. Our findings provide strong evidence that belief alignment, where people resonate with common beliefs, is a primary driver of cross-cultural interactions in digital activism. We also demonstrate that while the actions of potential opinion leaders--in this case, K-pop entertainers--may amplify activism and lead to further expressions of love and admiration from fans, they do not appear to be a direct cause of activism. Finally, we report some initial evidence that the interaction between BLM and K-pop led to slight increases in their overall belief similarity.
A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Venue: arXiv cs.AI 2025
Year: 2025

Constraint Logic Programming (CLP) is a logic programming formalism used to solve problems requiring the consideration of constraints, like resource allocation and automated planning and scheduling. It has previously been extended in various directions, for example to support fuzzy constraint satisfaction, uncertainty, or negation, with different notions of semiring being used as a unifying abstraction for these generalizations. None of these extensions have studied clauses with negation allowed in the body. We investigate an extension of CLP which unifies many of these extensions and allows negation in the body. We provide semantics for such programs, using the framework of approximation fixpoint theory, and give a detailed overview of the impacts of properties of the semirings on the resulting semantics. As such, we provide a unifying framework that captures existing approaches and allows extending them with a more expressive language.
Compositional Coordination for Multi-Robot Teams with Large Language Models
Venue: arXiv cs.RO 2025
Year: 2025

Multi-robot coordination has traditionally relied on a mission-specific and expert-driven pipeline, where natural language mission descriptions are manually translated by domain experts into mathematical formulation, algorithm design, and executable code. This conventional process is labor-intensive, inaccessible to non-experts, and inflexible to changes in mission requirements. Here, we propose LAN2CB (Language to Collective Behavior), a novel framework that leverages large language models (LLMs) to streamline and generalize the multi-robot coordination pipeline. LAN2CB transforms natural language (NL) mission descriptions into executable Python code for multi-robot systems through two core modules: (1) Mission Analysis, which parses mission descriptions into behavior trees, and (2) Code Generation, which leverages the behavior tree and a structured knowledge base to generate robot control code. We further introduce a dataset of natural language mission descriptions to support development and benchmarking. Experiments in both simulation and real-world environments demonstrate that LAN2CB enables robust and flexible multi-robot coordination from natural language, significantly reducing manual engineering effort and supporting broad generalization across diverse mission types. Website: https://sites.google.com/view/lan-cb
Querying Graph-Relational Data
Venue: arXiv cs.PL 2025
Year: 2025

For applications that store structured data in relational databases, there is an impedance mismatch between the flat representations encouraged by relational data models and the deeply nested information that applications expect to receive. In this work, we present the graph-relational database model, which provides a flexible, compositional, and strongly-typed solution to this "object-relational mismatch." We formally define the graph-relational database model and present a static and dynamic semantics for queries. In addition, we discuss the realization of the graph-relational database model in EdgeQL, a general-purpose SQL-style query language, and the Gel system, which compiles EdgeQL schemas and queries into PostgreSQL queries. Gel facilitates the kind of object-shaped data manipulation that is frequently provided inefficiently by object-relational mapping (ORM) technologies, while achieving most of the efficiency that comes from writing complex PostgreSQL queries directly.
Online Joint Replenishment Problem with Arbitrary Holding and Backlog Costs
Venue: arXiv cs.DS 2025
Year: 2025

In their seminal paper Moseley, Niaparast, and Ravi introduced the Joint Replenishment Problem (JRP) with holding and backlog costs that models the trade-off between ordering costs, holding costs, and backlog costs in supply chain planning systems. Their model generalized the classical the make-to-order version as well make-to-stock version. For the case where holding costs function of all items are the same and all backlog costs are the same, they provide a constant competitive algorithm, leaving designing a constant competitive algorithm for arbitrary functions open. Moreover, they noticed that their algorithm does not work for arbitrary (request dependent) holding costs and backlog costs functions. We resolve their open problem and design a constant competitive algorithm that works for arbitrary request dependent functions. Specifically, we establish a 4-competitive algorithm for the single-item case and a 16-competitive for the general (multi-item) version. The algorithm of Moseley, Niaparast, and Ravi is based on fixed priority on the requests to items, and request to an item are always served by order of deadlines. In contrast, we design an algorithm with dynamic priority over the requests such that instead of servicing a prefix by deadline of requests, we may need to service a general subset of the requests.
Universal Wavelet Units in 3D Retinal Layer Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents the first study to apply tunable wavelet units (UwUs) for 3D retinal layer segmentation from Optical Coherence Tomography (OCT) volumes. To overcome the limitations of conventional max-pooling, we integrate three wavelet-based downsampling modules, OrthLattUwU, BiorthLattUwU, and LS-BiorthLattUwU, into a motion-corrected MGU-Net architecture. These modules use learnable lattice filter banks to preserve both low- and high-frequency features, enhancing spatial detail and structural consistency. Evaluated on the Jacobs Retina Center (JRC) OCT dataset, our framework shows significant improvement in accuracy and Dice score, particularly with LS-BiorthLattUwU, highlighting the benefits of tunable wavelet filters in volumetric medical image segmentation.
Disability Across Cultures: A Human-Centered Audit of Ableism in Western and Indic LLMs
Venue: arXiv cs.CY 2025
Year: 2025

People with disabilities (PwD) experience disproportionately high levels of discrimination and hate online, particularly in India, where entrenched stigma and limited resources intensify these challenges. Large language models (LLMs) are increasingly used to identify and mitigate online hate, yet most research on online ableism focuses on Western audiences with Western AI models. Are these models adequately equipped to recognize ableist harm in non-Western places like India? Do localized, Indic language models perform better? To investigate, we adopted and translated a publicly available ableist speech dataset to Hindi, and prompted eight LLMs--four developed in the U.S. (GPT-4, Gemini, Claude, Llama) and four in India (Krutrim, Nanda, Gajendra, Airavata)--to score and explain ableism. In parallel, we recruited 175 PwD from both the U.S. and India to perform the same task, revealing stark differences between groups. Western LLMs consistently overestimated ableist harm, while Indic LLMs underestimated it. Even more concerning, all LLMs were more tolerant of ableism when it was expressed in Hindi and asserted Western framings of ableist harm. In contrast, Indian PwD interpreted harm through intention, relationality, and resilience--emphasizing a desire to inform and educate perpetrators. This work provides groundwork for global, inclusive standards of ableism, demonstrating the need to center local disability experiences in the design and evaluation of AI systems.
Equivariant Goal Conditioned Contrastive Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Contrastive Reinforcement Learning (CRL) provides a promising framework for extracting useful structured representations from unlabeled interactions. By pulling together state-action pairs and their corresponding future states, while pushing apart negative pairs, CRL enables learning nontrivial policies without manually designed rewards. In this work, we propose Equivariant CRL (ECRL), which further structures the latent space using equivariant constraints. By leveraging inherent symmetries in goal-conditioned manipulation tasks, our method improves both sample efficiency and spatial generalization. Specifically, we formally define Goal-Conditioned Group-Invariant MDPs to characterize rotation-symmetric robotic manipulation tasks, and build on this by introducing a novel rotation-invariant critic representation paired with a rotation-equivariant actor for Contrastive RL. Our approach consistently outperforms strong baselines across a range of simulated tasks in both state-based and image-based settings. Finally, we extend our method to the offline RL setting, demonstrating its effectiveness across multiple tasks.
A Single-step Accurate Fingerprint Registration Method Based on Local Feature Matching
Venue: arXiv cs.CV 2025
Year: 2025

Distortion of the fingerprint images leads to a decline in fingerprint recognition performance, and fingerprint registration can mitigate this distortion issue by accurately aligning two fingerprint images. Currently, fingerprint registration methods often consist of two steps: an initial registration based on minutiae, and a dense registration based on matching points. However, when the quality of fingerprint image is low, the number of detected minutiae is reduced, leading to frequent failures in the initial registration, which ultimately causes the entire fingerprint registration process to fail. In this study, we propose an end-to-end single-step fingerprint registration algorithm that aligns two fingerprints by directly predicting the semi-dense matching points correspondences between two fingerprints. Thus, our method minimizes the risk of minutiae registration failure and also leverages global-local attentions to achieve end-to-end pixel-level alignment between the two fingerprints. Experiment results prove that our method can achieve the state-of-the-art matching performance with only single-step registration, and it can also be used in conjunction with dense registration algorithms for further performance improvements.
LOCOFY Large Design Models -- Design to code conversion solution
Venue: arXiv cs.SE 2025
Year: 2025

Despite rapid advances in Large Language Models and Multimodal Large Language Models (LLMs), numerous challenges related to interpretability, scalability, resource requirements and repeatability remain, related to their application in the design-to-code space. To address this, we introduce the Large Design Models (LDMs) paradigm specifically trained on designs and webpages to enable seamless conversion from design-to-code. We have developed a training and inference pipeline by incorporating data engineering and appropriate model architecture modification. The training pipeline consists of the following: 1)Design Optimiser: developed using a proprietary ground truth dataset and addresses sub-optimal designs; 2)Tagging and feature detection: using pre-trained and fine-tuned models, this enables the accurate detection and classification of UI elements; and 3)Auto Components: extracts repeated UI structures into reusable components to enable creation of modular code, thus reducing redundancy while enhancing code reusability. In this manner, each model addresses distinct but key issues for design-to-code conversion. Separately, our inference pipeline processes real-world designs to produce precise and interpretable instructions for code generation and ensures reliability. Additionally, our models illustrated exceptional end-to-end design-to-code conversion accuracy using a novel preview match score metric. Comparative experiments indicated superior performance of LDMs against LLMs on accuracy of node positioning, responsiveness and reproducibility. Moreover, our custom-trained tagging and feature detection model demonstrated high precision and consistency in identifying UI elements across a wide sample of test designs. Thus, our proposed LDMs are a reliable and superior solution to understanding designs that subsequently enable the generation of efficient and reliable production-ready code.
Robust Bioacoustic Detection via Richly Labelled Synthetic Soundscape Augmentation
Venue: arXiv cs.SD 2025
Year: 2025

Passive Acoustic Monitoring (PAM) analysis is often hindered by the intensive manual effort needed to create labelled training data. This study introduces a synthetic data framework to generate large volumes of richly labelled training data from very limited source material, improving the robustness of bioacoustic detection models. Our framework synthesises realistic soundscapes by combining clean background noise with isolated target vocalisations (little owl), automatically generating dynamic labels like bounding boxes during synthesis. A model fine-tuned on this data generalised well to real-world soundscapes, with performance remaining high even when the diversity of source vocalisations was drastically reduced, indicating the model learned generalised features without overfitting. This demonstrates that synthetic data generation is a highly effective strategy for training robust bioacoustic detectors from small source datasets. The approach significantly reduces manual labelling effort, overcoming a key bottleneck in computational bioacoustics and enhancing ecological assessment capabilities.
From Contracts to Code: Automating Smart Contract Generation with Multi-Level Finite State Machines
Venue: arXiv cs.CR 2025
Year: 2025

In an increasingly complex contractual landscape, the demand for transparency, security, and efficiency has intensified. Blockchain technology, with its decentralized and immutable nature, addresses these challenges by reducing intermediary costs, minimizing fraud risks, and enhancing system compatibility. Smart contracts, initially conceptualized by Nick Szabo and later implemented on the Ethereum blockchain, automate and secure contractual clauses, offering a robust solution for various industries. However, their complexity and the requirement for advanced programming skills present significant barriers to widespread adoption. This study introduces a multi-level finite state machine model designed to represent and track the execution of smart contracts. Our model aims to simplify smart contract development by providing a formalized framework that abstracts underlying technical complexities, making it accessible to professionals without deep technical expertise. The hierarchical structure of the multi-level finite state machine enhances contract modularity and traceability, facilitating detailed representation and evaluation of functional properties. The paper explores the potential of this multi-level approach, reviewing existing methodologies and tools, and detailing the smart contract generation process with an emphasis on reusable components and modularity. We also conduct a security analysis to evaluate potential vulnerabilities in our model, ensuring the robustness and reliability of the generated smart contracts.