OpenRaiser/Intern-Atlas huggingface dataset:
Neural Associative Skill Memories for safer robotics and modelling human sensorimotor repertoires
Venue: arXiv cs.RO 2025
Year: 2025

Modern robots face challenges shared by humans, where machines must learn multiple sensorimotor skills and express them adaptively. Equipping robots with a human-like memory of how it feels to do multiple stereotypical movements can make robots more aware of normal operational states and help develop self-preserving safer robots. Associative Skill Memories (ASMs) aim to address this by linking movement primitives to sensory feedback, but existing implementations rely on hard-coded libraries of individual skills. A key unresolved problem is how a single neural network can learn a repertoire of skills while enabling fault detection and context-aware execution. Here we introduce Neural Associative Skill Memories (ASMs), a framework that utilises self-supervised predictive coding for temporal prediction to unify skill learning and expression, using biologically plausible learning rules. Unlike traditional ASMs which require explicit skill selection, Neural ASMs implicitly recognize and express skills through contextual inference, enabling fault detection across learned behaviours without an explicit skill selection mechanism. Compared to recurrent neural networks trained via backpropagation through time, our model achieves comparable qualitative performance in skill memory expression while using local learning rules and predicts a biologically relevant speed-accuracy trade-off during skill memory expression. This work advances the field of neurorobotics by demonstrating how predictive coding principles can model adaptive robot control and human motor preparation. By unifying fault detection, reactive control, skill memorisation and expression into a single energy-based architecture, Neural ASMs contribute to safer robotics and provide a computational lens to study biological sensorimotor learning.
Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints
Venue: arXiv cs.CL 2025
Year: 2025

This case study applies a phased hyperparameter optimization process to compare multitask natural language model variants that utilize multiphase learning rate scheduling and optimizer parameter grouping. We employ short, Bayesian optimization sessions that leverage multi-fidelity, hyperparameter space pruning, progressive halving, and a degree of human guidance. We utilize the Optuna TPE sampler and Hyperband pruner, as well as the Scikit-Learn Gaussian process minimization. Initially, we use efficient low-fidelity sprints to prune the hyperparameter space. Subsequent sprints progressively increase their model fidelity and employ hyperband pruning for efficiency. A second aspect of our approach is using a meta-learner to tune threshold values to resolve classification probabilities during inference. We demonstrate our method on a collection of variants of the 2021 Joint Entity and Relation Extraction model proposed by Eberts and Ulges.
What Makes a Fairness Tool Project Sustainable in Open Source?
Venue: arXiv cs.SE 2025
Year: 2025

As society becomes increasingly reliant on artificial intelligence, the need to mitigate risk and harm is paramount. In response, researchers and practitioners have developed tools to detect and reduce undesired bias, commonly referred to as fairness tools. Many of these tools are publicly available for free use and adaptation. While the growing availability of such tools is promising, little is known about the broader landscape beyond well-known examples like AI Fairness 360 and Fairlearn. Because fairness is an ongoing concern, these tools must be built for long-term sustainability. Using an existing set of fairness tools as a reference, we systematically searched GitHub and identified 50 related projects. We then analyzed various aspects of their repositories to assess community engagement and the extent of ongoing maintenance. Our findings show diverse forms of engagement with these tools, suggesting strong support for open-source development. However, we also found significant variation in how well these tools are maintained. Notably, 53 percent of fairness projects become inactive within the first three years. By examining sustainability in fairness tooling, we aim to promote more stability and growth in this critical area.
New results in canonical polyadic decomposition over finite fields
Venue: arXiv cs.CC 2025
Year: 2025

Canonical polyadic decomposition (CPD) is at the core of fast matrix multiplication, a computational problem with widespread implications across several seemingly unrelated problems in computer science. Much recent progress in this field has used randomized heuristic search to find new CPDs, often over a finite field. However, if these techniques fail to find a CPD with low enough rank, they cannot prove that no such CPD exists. Consequently, these methods fail to resolve certain long-standing questions, such as whether the tensor corresponding to $3\times 3$ matrix multiplication has rank less than 23. To make progress on these problems, we develop a novel algorithm that preserves exactness, i.e. they can provably verify whether or not a given tensor has a specified rank. Compared to brute force, when searching for a rank-$R$ CPD of a $n_0\times\dots\times n_{D-1}$-shaped tensor over a finite field $\mathbb{F}$, where $n_0\ge \dots\ge n_{D-1}$, our algorithm saves a multiplicative factor of roughly $|\mathbb{F}|^{R(n_0-1)+n_0(\sum_{d\ge 1} n_d)}$. Additionally, our algorithm runs in polynomial time. We also find a novel algorithm to search border CPDs, a variant of CPDs that is also important in fast matrix multiplication. Finally, we study the maximum rank problem and give new upper and lower bounds, both for families of tensor shapes and specific shapes. Although our CPD search algorithms are still too slow to resolve the rank of $3\times 3$ matrix multiplication, we are able to utilize them in this problem by adding extra search pruners that do not affect exactness or increase asymptotic running time.
Dyadic Mamba: Long-term Dyadic Human Motion Synthesis
Venue: arXiv cs.CV 2025
Year: 2025

Generating realistic dyadic human motion from text descriptions presents significant challenges, particularly for extended interactions that exceed typical training sequence lengths. While recent transformer-based approaches have shown promising results for short-term dyadic motion synthesis, they struggle with longer sequences due to inherent limitations in positional encoding schemes. In this paper, we introduce Dyadic Mamba, a novel approach that leverages State-Space Models (SSMs) to generate high-quality dyadic human motion of arbitrary length. Our method employs a simple yet effective architecture that facilitates information flow between individual motion sequences through concatenation, eliminating the need for complex cross-attention mechanisms. We demonstrate that Dyadic Mamba achieves competitive performance on standard short-term benchmarks while significantly outperforming transformer-based approaches on longer sequences. Additionally, we propose a new benchmark for evaluating long-term motion synthesis quality, providing a standardized framework for future research. Our results demonstrate that SSM-based architectures offer a promising direction for addressing the challenging task of long-term dyadic human motion synthesis from text descriptions.
Do Large Language Models Know Conflict? Investigating Parametric vs. Non-Parametric Knowledge of LLMs for Conflict Forecasting
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have shown impressive performance across natural language tasks, but their ability to forecast violent conflict remains underexplored. We investigate whether LLMs possess meaningful parametric knowledge-encoded in their pretrained weights-to predict conflict escalation and fatalities without external data. This is critical for early warning systems, humanitarian planning, and policy-making. We compare this parametric knowledge with non-parametric capabilities, where LLMs access structured and unstructured context from conflict datasets (e.g., ACLED, GDELT) and recent news reports via Retrieval-Augmented Generation (RAG). Incorporating external information could enhance model performance by providing up-to-date context otherwise missing from pretrained weights. Our two-part evaluation framework spans 2020-2024 across conflict-prone regions in the Horn of Africa and the Middle East. In the parametric setting, LLMs predict conflict trends and fatalities relying only on pretrained knowledge. In the non-parametric setting, models receive summaries of recent conflict events, indicators, and geopolitical developments. We compare predicted conflict trend labels (e.g., Escalate, Stable Conflict, De-escalate, Peace) and fatalities against historical data. Our findings highlight the strengths and limitations of LLMs for conflict forecasting and the benefits of augmenting them with structured external knowledge.
Advancing Mobile UI Testing by Learning Screen Usage Semantics
Venue: arXiv cs.SE 2025
Year: 2025

The demand for quality in mobile applications has increased greatly given users' high reliance on them for daily tasks. Developers work tirelessly to ensure that their applications are both functional and user-friendly. In pursuit of this, Automated Input Generation (AIG) tools have emerged as a promising solution for testing mobile applications by simulating user interactions and exploring app functionalities. However, these tools face significant challenges in navigating complex Graphical User Interfaces (GUIs), and developers often have trouble understanding their output. More specifically, AIG tools face difficulties in navigating out of certain screens, such as login pages and advertisements, due to a lack of contextual understanding which leads to suboptimal testing coverage. Furthermore, while AIG tools can provide interaction traces consisting of action and screen details, there is limited understanding of its coverage of higher level functionalities, such as logging in, setting alarms, or saving notes. Understanding these covered use cases are essential to ensure comprehensive test coverage of app functionalities. Difficulty in testing mobile UIs can lead to the design of complex interfaces, which can adversely affect users of advanced age who often face usability barriers due to small buttons, cluttered layouts, and unintuitive navigation. There exists many studies that highlight these issues, but automated solutions for improving UI accessibility needs more attention. This research seeks to enhance automated UI testing techniques by learning the screen usage semantics of mobile apps and helping them navigate more efficiently, offer more insights about tested functionalities and also improve the usability of a mobile app's interface by identifying and mitigating UI design issues.
DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Domain adaptation addresses the challenge of model performance degradation caused by domain gaps. In the typical setup for unsupervised domain adaptation, labeled data from a source domain and unlabeled data from a target domain are used to train a target model. However, access to labeled source domain data, particularly in medical datasets, can be restricted due to privacy policies. As a result, research has increasingly shifted to source-free domain adaptation (SFDA), which requires only a pretrained model from the source domain and unlabeled data from the target domain data for adaptation. Existing SFDA methods often rely on domain-specific image style translation and self-supervision techniques to bridge the domain gap and train the target domain model. However, the quality of domain-specific style-translated images and pseudo-labels produced by these methods still leaves room for improvement. Moreover, training the entire model during adaptation can be inefficient under limited supervision. In this paper, we propose a novel SFDA framework to address these challenges. Specifically, to effectively mitigate the impact of domain gap in the initial training phase, we introduce preadaptation to generate a preadapted model, which serves as an initialization of target model and allows for the generation of high-quality enhanced pseudo-labels without introducing extra parameters. Additionally, we propose a data-dependent frequency prompt to more effectively translate target domain images into a source-like style. To further enhance adaptation, we employ a style-related layer fine-tuning strategy, specifically designed for SFDA, to train the target model using the prompted target domain images and pseudo-labels. Extensive experiments on cross-modality abdominal and cardiac SFDA segmentation tasks demonstrate that our proposed method outperforms existing state-of-the-art methods.
Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
Venue: arXiv cs.CL 2025
Year: 2025

The advent of large language models (LLMs) has allowed numerous applications, including the generation of queried responses, to be leveraged in chatbots and other conversational assistants. Being trained on a plethora of data, LLMs often undergo high levels of over-fitting, resulting in the generation of extra and incorrect data, thus causing hallucinations in output generation. One of the root causes of such problems is the lack of timely, factual, and personalized information fed to the LLM. In this paper, we propose an approach to address these problems by introducing retrieval augmented generation (RAG) using knowledge graphs (KGs) to assist the LLM in personalized response generation tailored to the users. KGs have the advantage of storing continuously updated factual information in a structured way. While our KGs can be used for a variety of frequently updated personal data, such as calendar, contact, and location data, we focus on calendar data in this paper. Our experimental results show that our approach works significantly better in understanding personal information and generating accurate responses compared to the baseline LLMs using personal data as text inputs, with a moderate reduction in response time.
Sybil-based Virtual Data Poisoning Attacks in Federated Learning
Venue: arXiv cs.CR 2025
Year: 2025

Federated learning is vulnerable to poisoning attacks by malicious adversaries. Existing methods often involve high costs to achieve effective attacks. To address this challenge, we propose a sybil-based virtual data poisoning attack, where a malicious client generates sybil nodes to amplify the poisoning model's impact. To reduce neural network computational complexity, we develop a virtual data generation method based on gradient matching. We also design three schemes for target model acquisition, applicable to online local, online global, and offline scenarios. In simulation, our method outperforms other attack algorithms since our method can obtain a global target model under non-independent uniformly distributed data.
ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
Venue: arXiv cs.DC 2025
Year: 2025

With the widespread adoption of Large Language Models (LLMs), serving LLM inference requests has become an increasingly important task, attracting active research advancements. Practical workloads play an essential role in this process: they are critical for motivating and benchmarking serving techniques and systems. However, the existing understanding of real-world LLM serving workloads is limited due to the lack of a comprehensive workload characterization. Prior analyses remain insufficient in scale and scope, thus failing to fully capture intricate workload characteristics. In this paper, we fill the gap with an in-depth characterization of LLM serving workloads collected from our worldwide cloud inference serving service, covering not only language models but also emerging multimodal and reasoning models, and unveiling important new findings in each case. Moreover, based on our findings, we propose ServeGen, a principled framework for generating realistic LLM serving workloads by composing them on a per-client basis. A practical use case in production validates that ServeGen avoids 50% under-provisioning compared to naive workload generation, demonstrating ServeGen's advantage in performance benchmarking. ServeGen is available at https://github.com/alibaba/ServeGen.
Topology-driven identification of repetitions in multi-variate time series
Venue: arXiv cs.CG 2025
Year: 2025

Many multi-variate time series obtained in the natural sciences and engineering possess a repetitive behavior, as for instance state-space trajectories of industrial machines in discrete automation. Recovering the times of recurrence from such a multi-variate time series is of a fundamental importance for many monitoring and control tasks. For a periodic time series this is equivalent to determining its period length. In this work we present a persistent homology framework to estimate recurrence times in multi-variate time series with different generalizations of cyclic behavior (periodic, repetitive, and recurring). To this end, we provide three specialized methods within our framework that are provably stable and validate them using real-world data, including a new benchmark dataset from an injection molding machine.
Boosting Text-to-Chart Retrieval through Training with Synthesized Semantic Insights
Venue: arXiv cs.IR 2025
Year: 2025

Charts are crucial for data analysis and decision-making.Text-to-chart retrieval systems have become increasingly important for Business Intelligence (BI), where users need to find relevant charts that match their analytical needs. These needs can be categorized into precise queries that are well-specified and fuzzy queries that are more exploratory -- both require understanding the semantics and context of the charts. However, existing text-to-chart retrieval solutions often fail to capture the semantic content and contextual information of charts, primarily due to the lack of comprehensive metadata (or semantic insights). To address this limitation, we propose a training data development pipeline that automatically synthesizes hierarchical semantic insights for charts, covering visual patterns (visual-oriented), statistical properties (statistics-oriented), and practical applications (task-oriented), which produces 207,498 semantic insights for 69,166 charts. Based on these, we train a CLIP-based model named ChartFinder to learn better representations of charts for text-to-chart retrieval. Our method leverages rich semantic insights during the training phase to develop a model that understands both visual and semantic aspects of charts.To evaluate text-to-chart retrieval performance, we curate the first benchmark, CRBench, for this task with 21,862 charts and 326 text queries from real-world BI applications, with ground-truth labels verified by the crowd workers.Experiments show that ChartFinder significantly outperforms existing methods in text-to-chart retrieval tasks across various settings. For precise queries, ChartFinder achieves up to 66.9% NDCG@10, which is 11.58% higher than state-of-the-art models. In fuzzy query tasks, our method also demonstrates consistent improvements, with an average increase of 5% across nearly all metrics.
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
Venue: arXiv cs.LG 2025
Year: 2025

Traditional machine learning models often prioritize predictive accuracy, often at the expense of model transparency and interpretability. The lack of transparency makes it difficult for organizations to comply with regulatory requirements and gain stakeholders trust. In this research, we propose a fraud detection framework that combines a stacking ensemble of well-known gradient boosting models: XGBoost, LightGBM, and CatBoost. In addition, explainable artificial intelligence (XAI) techniques are used to enhance the transparency and interpretability of the model's decisions. We used SHAP (SHapley Additive Explanations) for feature selection to identify the most important features. Further efforts were made to explain the model's predictions using Local Interpretable Model-Agnostic Explanation (LIME), Partial Dependence Plots (PDP), and Permutation Feature Importance (PFI). The IEEE-CIS Fraud Detection dataset, which includes more than 590,000 real transaction records, was used to evaluate the proposed model. The model achieved a high performance with an accuracy of 99% and an AUC-ROC score of 0.99, outperforming several recent related approaches. These results indicate that combining high prediction accuracy with transparent interpretability is possible and could lead to a more ethical and trustworthy solution in financial fraud detection.
Dark LLMs: The Growing Threat of Unaligned AI Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) rapidly reshape modern life, advancing fields from healthcare to education and beyond. However, alongside their remarkable capabilities lies a significant threat: the susceptibility of these models to jailbreaking. The fundamental vulnerability of LLMs to jailbreak attacks stems from the very data they learn from. As long as this training data includes unfiltered, problematic, or 'dark' content, the models can inherently learn undesirable patterns or weaknesses that allow users to circumvent their intended safety controls. Our research identifies the growing threat posed by dark LLMs models deliberately designed without ethical guardrails or modified through jailbreak techniques. In our research, we uncovered a universal jailbreak attack that effectively compromises multiple state-of-the-art models, enabling them to answer almost any question and produce harmful outputs upon request. The main idea of our attack was published online over seven months ago. However, many of the tested LLMs were still vulnerable to this attack. Despite our responsible disclosure efforts, responses from major LLM providers were often inadequate, highlighting a concerning gap in industry practices regarding AI safety. As model training becomes more accessible and cheaper, and as open-source LLMs proliferate, the risk of widespread misuse escalates. Without decisive intervention, LLMs may continue democratizing access to dangerous knowledge, posing greater risks than anticipated.
Designing and Contextualising Probes for African Languages
Venue: arXiv cs.CL 2025
Year: 2025

Pretrained language models (PLMs) for African languages are continually improving, but the reasons behind these advances remain unclear. This paper presents the first systematic investigation into probing PLMs for linguistic knowledge about African languages. We train layer-wise probes for six typologically diverse African languages to analyse how linguistic features are distributed. We also design control tasks, a way to interpret probe performance, for the MasakhaPOS dataset. We find PLMs adapted for African languages to encode more linguistic information about target languages than massively multilingual PLMs. Our results reaffirm previous findings that token-level syntactic information concentrates in middle-to-last layers, while sentence-level semantic information is distributed across all layers. Through control tasks and probing baselines, we confirm that performance reflects the internal knowledge of PLMs rather than probe memorisation. Our study applies established interpretability techniques to African-language PLMs. In doing so, we highlight the internal mechanisms underlying the success of strategies like active learning and multilingual adaptation.