OpenRaiser/Intern-Atlas huggingface dataset:
Learning Particle Dynamics Subject to Rigid Body Manipulations Using Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Simulating particle dynamics with high fidelity is crucial for solving real-world interaction and control tasks involving liquids in design, graphics, and robotics. Recently, data-driven approaches, particularly those based on graph neural networks (GNNs), have shown progress in tackling such problems. However, these approaches are often limited to learning fluid behavior in static free-fall environments or simple manipulation settings involving primitive objects, often overlooking complex interactions with dynamically moving kinematic rigid bodies. Here, we propose a GNN-based framework designed from the ground up to learn the dynamics of liquids under rigid body interactions and active manipulations, where particles are represented as graph nodes and particle-object collisions are handled using surface representations with the bounding volume hierarchy (BVH) algorithm. Our approach accurately captures fluid behavior in dynamic settings and can also function as a simulator in static free-fall environments. Despite being trained on single-object manipulation tasks, our model generalizes effectively to environments with novel objects and novel manipulation tasks. Finally, we show that the learned dynamics can be leveraged to solve control and manipulation tasks using gradient-based optimization methods.
SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data
Venue: arXiv cs.HC 2025
Year: 2025

The ability to track a user's arm pose could be valuable in a wide range of applications, including fitness, rehabilitation, augmented reality input, life logging, and context-aware assistants. Unfortunately, this capability is not readily available to consumers. Systems either require cameras, which carry privacy issues, or utilize multiple worn IMUs or markers. In this work, we describe how an off-the-shelf smartphone and smartwatch can work together to accurately estimate arm pose. Moving beyond prior work, we take advantage of more recent ultra-wideband (UWB) functionality on these devices to capture absolute distance between the two devices. This measurement is the perfect complement to inertial data, which is relative and suffers from drift. We quantify the performance of our software-only approach using off-the-shelf devices, showing it can estimate the wrist and elbow joints with a \hl{median positional error of 11.0~cm}, without the user having to provide training data.
Geometric Foundations of Tuning without Forgetting in Neural ODEs
Venue: arXiv cs.LG 2025
Year: 2025

In our earlier work, we introduced the principle of Tuning without Forgetting (TwF) for sequential training of neural ODEs, where training samples are added iteratively and parameters are updated within the subspace of control functions that preserves the end-point mapping at previously learned samples on the manifold of output labels in the first-order approximation sense. In this letter, we prove that this parameter subspace forms a Banach submanifold of finite codimension under nonsingular controls, and we characterize its tangent space. This reveals that TwF corresponds to a continuation/deformation of the control function along the tangent space of this Banach submanifold, providing a theoretical foundation for its mapping-preserving (not forgetting) during the sequential training exactly, beyond first-order approximation.
Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning enables collaborative model training across numerous edge devices without requiring participants to share data; however, memory and communication constraints on these edge devices may preclude their participation in training. We consider a setting in which a subset of edge devices are below a critical memory or communication threshold required to conduct model updates. Under typical federated optimization algorithms, these devices are excluded from training which renders their data inaccessible and increases system induced bias. We are inspired by MeZO, a zeroth-order method used for memory-efficient fine-tuning. The increased variance inherent to zeroth-order gradient approximations has relegated previous zeroth-order optimizers exclusively to the domain of fine tuning; a limitation we seek to correct. We devise a federated, memory-efficient zeroth-order optimizer, ZOWarmUp that permits zeroth-order training from a random initialization. ZOWarmUp leverages differing client capabilities and careful variance reduction techniques to facilitate participation of under-represented, low-resource clients in model training. Like other federated zeroth-order methods, ZOWarmUp eliminates the need for edge devices to transmit their full gradients to the server and instead relies on only a small set of random seeds, rendering the up-link communication cost negligible. We present experiments using various datasets and model architectures to show that ZOWarmUp is a robust algorithm that can can be applied under a wide variety of circumstances. For systems with a high proportion of edge devices that would otherwise be excluded from training, this algorithm provides access to a greater volume and diversity of data, thus improving training outcomes.
Multimodal Proposal for an AI-Based Tool to Increase Cross-Assessment of Messages
Venue: arXiv cs.CL 2025
Year: 2025

Earnings calls represent a uniquely rich and semi-structured source of financial communication, blending scripted managerial commentary with unscripted analyst dialogue. Although recent advances in financial sentiment analysis have integrated multi-modal signals, such as textual content and vocal tone, most systems rely on flat document-level or sentence-level models, failing to capture the layered discourse structure of these interactions. This paper introduces a novel multi-modal framework designed to generate semantically rich and structurally aware embeddings of earnings calls, by encoding them as hierarchical discourse trees. Each node, comprising either a monologue or a question-answer pair, is enriched with emotional signals derived from text, audio, and video, as well as structured metadata including coherence scores, topic labels, and answer coverage assessments. A two-stage transformer architecture is proposed: the first encodes multi-modal content and discourse metadata at the node level using contrastive learning, while the second synthesizes a global embedding for the entire conference. Experimental results reveal that the resulting embeddings form stable, semantically meaningful representations that reflect affective tone, structural logic, and thematic alignment. Beyond financial reporting, the proposed system generalizes to other high-stakes unscripted communicative domains such as tele-medicine, education, and political discourse, offering a robust and explainable approach to multi-modal discourse representation. This approach offers practical utility for downstream tasks such as financial forecasting and discourse evaluation, while also providing a generalizable method applicable to other domains involving high-stakes communication.
Prebiotic Functional Programs: Endogenous Selection in an Artificial Chemistry
Venue: arXiv cs.FL 2025
Year: 2025

Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control. This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus. Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives. The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.
A software security review on Uganda's Mobile Money Services: Dr. Jim Spire's tweets sentiment analysis
Venue: arXiv cs.CY 2025
Year: 2025

The proliferation of mobile money in Uganda has been a cornerstone of financial inclusion, yet its security mechanisms remain a critical concern. This study investigates a significant public response to perceived security failures: the #StopAirtelThefty Twitter campaign of August 2025 Sparked by an incident publicized by Dr. Jim Spire Ssentongo where a phone thief accessed a victim's account, withdrew funds, and procured a loan, the campaign revealed deep seated public anxiety over the safety of mobile money. This research employs qualitative analysis to systematically examine the complaints raised during this campaign, extracting key themes related to security vulnerabilities and user dissatisfaction. By synthesizing these public sentiments, the paper provides crucial insights into the specific security gaps experienced by users and situates these findings within the larger framework of Uganda's mobile money regulatory and operational environment. The study concludes with implications for providers, policymakers, and the future of secure digital finance in Uganda.
A Cegar-centric Bounded Reachability Analysis for Compositional Affine Hybrid Systems
Venue: arXiv cs.LO 2025
Year: 2025

Reachability analysis of compositional hybrid systems, where individual components are modeled as hybrid automata, poses unique challenges. In addition to preserving the compositional semantics while computing system behaviors, algorithms have to cater to the explosion in the number of locations in the parallel product automaton. In this paper, we propose a bounded reachability analysis algorithm for compositional hybrid systems with piecewise affine dynamics, based on the principle of counterexample guided abstraction refinement (CEGAR). In particular, the algorithm searches for a counterexample in the discrete abstraction of the composition model, without explicitly computing a product automaton. When a counterexample is discovered in the abstraction, its validity is verified by a refinement of the state-space guided by the abstract counterexample. The state-space refinement is through a symbolic reachability analysis, particularly using a state-of-the-art algorithm with support functions as the continuous state representation. In addition, the algorithm mixes different semantics of composition with the objective of improved efficiency. Step compositional semantics is followed while exploring the abstract (discrete) state-space, while shallow compositional semantics is followed during state-space refinement with symbolic reachability analysis. Optimizations such as caching the results of the symbolic reachability analysis, which can be later reused, have been proposed. We implement this algorithm in the tool SAT-Reach and demonstrate the scalability benefits.
treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds
Venue: arXiv cs.CV 2025
Year: 2025

Close-range laser scanning provides detailed 3D captures of forest stands but requires efficient software for processing 3D point cloud data and extracting individual trees. Although recent studies have introduced deep learning methods for tree instance segmentation, these approaches require large annotated datasets and substantial computational resources. As a resource-efficient alternative, we present a revised version of the treeX algorithm, an unsupervised method that combines clustering-based stem detection with region growing for crown delineation. While the original treeX algorithm was developed for personal laser scanning (PLS) data, we provide two parameter presets, one for ground-based laser scanning (stationary terrestrial - TLS and PLS), and one for UAV-borne laser scanning (ULS). We evaluated the method on six public datasets (FOR-instance, ForestSemantic, LAUTx, NIBIO MLS, TreeLearn, Wytham Woods) and compared it to six open-source methods (original treeX, treeiso, RayCloudTools, ForAINet, SegmentAnyTree, TreeLearn). Compared to the original treeX algorithm, our revision reduces runtime and improves accuracy, with instance detection F$_1$-score gains of +0.11 to +0.49 for ground-based data. For ULS data, our preset achieves an F$_1$-score of 0.58, whereas the original algorithm fails to segment any correct instances. For TLS and PLS data, our algorithm achieves accuracy similar to recent open-source methods, including deep learning. Given its algorithmic design, we see two main applications for our method: (1) as a resource-efficient alternative to deep learning approaches in scenarios where the data characteristics align with the method design (sufficient stem visibility and point density), and (2) for the semi-automatic generation of labels for deep learning models. To enable broader adoption, we provide an open-source Python implementation in the pointtree package.
CausalARC: Abstract Reasoning with Causal World Models
Venue: arXiv cs.AI 2025
Year: 2025

On-the-fly reasoning often requires adaptation to novel problems under limited data and distribution shift. This work introduces CausalARC: an experimental testbed for AI reasoning in low-data and out-of-distribution regimes, modeled after the Abstraction and Reasoning Corpus (ARC). Each CausalARC reasoning task is sampled from a fully specified causal world model, formally expressed as a structural causal model. Principled data augmentations provide observational, interventional, and counterfactual feedback about the world model in the form of few-shot, in-context learning demonstrations. As a proof-of-concept, we illustrate the use of CausalARC for four language model evaluation settings: (1) abstract reasoning with test-time training, (2) counterfactual reasoning with in-context learning, (3) program synthesis, and (4) causal discovery with logical reasoning. Within- and between-model performance varied heavily across tasks, indicating room for significant improvement in language model reasoning.
An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification
Venue: arXiv cs.AI 2025
Year: 2025

Explainable AI (XAI) has become an increasingly important topic for understanding and attributing the predictions made by complex Time Series Classification (TSC) models. Among attribution methods, SHapley Additive exPlanations (SHAP) is widely regarded as an excellent attribution method; but its computational complexity, which scales exponentially with the number of features, limits its practicality for long time series. To address this, recent studies have shown that aggregating features via segmentation, to compute a single attribution value for a group of consecutive time points, drastically reduces SHAP running time. However, the choice of the optimal segmentation strategy remains an open question. In this work, we investigated eight different Time Series Segmentation algorithms to understand how segment compositions affect the explanation quality. We evaluate these approaches using two established XAI evaluation methodologies: InterpretTime and AUC Difference. Through experiments on both Multivariate (MTS) and Univariate Time Series (UTS), we find that the number of segments has a greater impact on explanation quality than the specific segmentation method. Notably, equal-length segmentation consistently outperforms most of the custom time series segmentation algorithms. Furthermore, we introduce a novel attribution normalisation technique that weights segments by their length and we show that it consistently improves attribution quality.
Combining Performance and Productivity: Accelerating the Network Sensing Graph Challenge with GPUs and Commodity Data Science Software
Venue: arXiv cs.DC 2025
Year: 2025

The HPEC Graph Challenge is a collection of benchmarks representing complex workloads that test the hardware and software components of HPC systems, which traditional benchmarks, such as LINPACK, do not. The first benchmark, Subgraph Isomorphism, focused on several compute-bound and memory-bound kernels. The most recent of the challenges, the Anonymized Network Sensing Graph Challenge, represents a shift in direction, as it represents a longer end-to-end workload that requires many more software components, including, but not limited to, data I/O, data structures for representing graph data, and a wide range of functions for data preparation and network analysis. A notable feature of this new graph challenge is the use of GraphBLAS to represent the computational aspects of the problem statement. In this paper, we show an alternative interpretation of the GraphBLAS formulations using the language of data science. With this formulation, we show that the new graph challenge can be implemented using off-the-shelf ETL tools available in open-source, enterprise software such as NVIDIA's RAPIDS ecosystem. Using off-the-shelf software, RAPIDS cuDF and cupy, we enable significant software acceleration without requiring any specific HPC code and show speedups, over the same code running with Pandas on the CPU, of 147x-509x on an NVIDIA A100 GPU, 243x-1269X for an NVIDIA H100 GPU, and 332X-2185X for an NVIDIA H200 GPU.
Efficient Virtuoso: A Latent Diffusion Transformer Model for Goal-Conditioned Trajectory Planning
Venue: arXiv cs.RO 2025
Year: 2025

The ability to generate a diverse and plausible distribution of future trajectories is a critical capability for autonomous vehicle planning systems. While recent generative models have shown promise, achieving high fidelity, computational efficiency, and precise control remains a significant challenge. In this paper, we present the Efficient Virtuoso, a conditional latent diffusion model for goal-conditioned trajectory planning. Our approach introduces a novel two-stage normalization pipeline that first scales trajectories to preserve their geometric aspect ratio and then normalizes the resulting PCA latent space to ensure a stable training target. The denoising process is performed efficiently in this low-dimensional latent space by a simple MLP denoiser, which is conditioned on a rich scene context fused by a powerful Transformer-based StateEncoder. We demonstrate that our method achieves state-of-the-art performance on the Waymo Open Motion Dataset, achieving a minimum Average Displacement Error (minADE) of 0.25. Furthermore, through a rigorous ablation study on goal representation, we provide a key insight: while a single endpoint goal can resolve strategic ambiguity, a richer, multi-step sparse route is essential for enabling the precise, high-fidelity tactical execution that mirrors nuanced human driving behavior.
ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems
Venue: arXiv cs.IR 2025
Year: 2025

Recommender systems often must maximize a primary objective while ensuring secondary ones satisfy minimum thresholds, or "guardrails." This is critical for maintaining a consistent user experience and platform ecosystem, but enforcing these guardrails despite orthogonal system changes is challenging and often requires manual hyperparameter tuning. We introduce the Automated Constraint Targeting (ACT) framework, which automatically finds the minimal set of hyperparameter changes needed to satisfy these guardrails. ACT uses an offline pairwise evaluation on unbiased data to find solutions and continuously retrains to adapt to system and user behavior changes. We empirically demonstrate its efficacy and describe its deployment in a large-scale production environment.
Entanglement Purification With Finite Latency Classical Communication in Quantum Networks
Venue: arXiv cs.NI 2025
Year: 2025

Quantum networks rely on high fidelity entangled pairs distributed to nodes, but maintaining their fidelity is challenged by environmental decoherence during storage. Entanglement purification is used to restore fidelity, but the idle periods imposed by the associated classical communication delays counteract this goal by exposing the states to further decoherence. In this work, we analyze the practical viability of entanglement purification protocols (BBPSSW, DEJMPS), under non-instantaneous classical coordination over Internet protocol (IP) communications networks. We present a comprehensive performance evaluation of these protocols in various network conditions for a range of quantum memory technologies. We employ a microscopic Lindblad treatment of the underlying quantum dynamics, and use current-generation metropolitan IP network latency statistics and parameters drawn from quantum memory testbeds. In doing so we identify the regions in which entanglement purification succeeds and fails, delineated by break-even iso-fidelity contours in the phase space. We then determine the total number of entangled pairs required to complete a multi-round purification protocol, and the steady-state throughput of entangled pairs with purified fidelities that exceed application-specific thresholds. This provides latency budgets, memory quality targets, and resource-overhead estimates for deploying purification on current and near-future networks.
Low-Cost Open-Source Ambidextrous Robotic Hand with 23 Direct-Drive servos for American Sign Language Alphabet
Venue: arXiv cs.RO 2025
Year: 2025

Accessible communication through sign language is vital for deaf communities, 1 yet robotic solutions are often costly and limited. This study presents VulcanV3, a low- 2 cost, open-source, 3D-printed ambidextrous robotic hand capable of reproducing the full 3 American Sign Language (ASL) alphabet (52 signs for right- and left-hand configurations). 4 The system employs 23 direct-drive servo actuators for precise finger and wrist movements, 5 controlled by an Arduino Mega with dual PCA9685 modules. Unlike most humanoid upper- 6 limb systems, which rarely employ direct-drive actuation, VulcanV3 achieves complete ASL 7 coverage with a reversible design. All CAD files and code are released under permissive 8 open-source licenses to enable replication. Empirical tests confirmed accurate reproduction 9 of all 52 ASL handshapes, while a participant study (n = 33) achieved 96.97% recognition 10 accuracy, improving to 98.78% after video demonstration. VulcanV3 advances assistive 11 robotics by combining affordability, full ASL coverage, and ambidexterity in an openly 12 shared platform, contributing to accessible communication technologies and inclusive 13 innovation.
Graph Random Features for Scalable Gaussian Processes
Venue: arXiv cs.LG 2025
Year: 2025

We study the application of graph random features (GRFs) - a recently introduced stochastic estimator of graph node kernels - to scalable Gaussian processes on discrete input spaces. We prove that (under mild assumptions) Bayesian inference with GRFs enjoys $O(N^{3/2})$ time complexity with respect to the number of nodes $N$, compared to $O(N^3)$ for exact kernels. Substantial wall-clock speedups and memory savings unlock Bayesian optimisation on graphs with over $10^6$ nodes on a single computer chip, whilst preserving competitive performance.