OpenRaiser/Intern-Atlas huggingface dataset:
Interpretable and Granular Video-Based Quantification of Motor Characteristics from the Finger Tapping Test in Parkinson Disease
Venue: arXiv cs.CV 2025
Year: 2025

Accurately quantifying motor characteristics in Parkinson disease (PD) is crucial for monitoring disease progression and optimizing treatment strategies. The finger-tapping test is a standard motor assessment. Clinicians visually evaluate a patient's tapping performance and assign an overall severity score based on tapping amplitude, speed, and irregularity. However, this subjective evaluation is prone to inter- and intra-rater variability, and does not offer insights into individual motor characteristics captured during this test. This paper introduces a granular computer vision-based method for quantifying PD motor characteristics from video recordings. Four sets of clinically relevant features are proposed to characterize hypokinesia, bradykinesia, sequence effect, and hesitation-halts. We evaluate our approach on video recordings and clinical evaluations of 74 PD patients from the Personalized Parkinson Project. Principal component analysis with varimax rotation shows that the video-based features corresponded to the four deficits. Additionally, video-based analysis has allowed us to identify further granular distinctions within sequence effect and hesitation-halts deficits. In the following, we have used these features to train machine learning classifiers to estimate the Movement Disorder Society Unified Parkinson Disease Rating Scale (MDS-UPDRS) finger-tapping score. Compared to state-of-the-art approaches, our method achieves a higher accuracy in MDS-UPDRS score prediction, while still providing an interpretable quantification of individual finger-tapping motor characteristics. In summary, the proposed framework provides a practical solution for the objective assessment of PD motor characteristics, that can potentially be applied in both clinical and remote settings. Future work is needed to assess its responsiveness to symptomatic treatment and disease progression.
Advanced Applications of Generative AI in Actuarial Science: Case Studies Beyond ChatGPT
Venue: arXiv cs.CY 2025
Year: 2025

This article demonstrates the transformative impact of Generative AI (GenAI) on actuarial science, illustrated by four implemented case studies. It begins with a historical overview of AI, tracing its evolution from early neural networks to modern GenAI technologies. The first case study shows how Large Language Models (LLMs) improve claims cost prediction by deriving significant features from unstructured textual data, significantly reducing prediction errors in the underlying machine learning task. In the second case study, we explore the automation of market comparisons using the GenAI concept of Retrieval-Augmented Generation to identify and process relevant information from documents. A third case study highlights the capabilities of fine-tuned vision-enabled LLMs in classifying car damage types and extracting contextual information. The fourth case study presents a multi-agent system that autonomously analyzes data from a given dataset and generates a corresponding report detailing the key findings. In addition to these case studies, we outline further potential applications of GenAI in the insurance industry, such as the automation of claims processing and fraud detection, and the verification of document compliance with internal or external policies. Finally, we discuss challenges and considerations associated with the use of GenAI, covering regulatory issues, ethical concerns, and technical limitations, among others.
A Comment On "The Illusion of Thinking": Reframing the Reasoning Cliff as an Agentic Gap
Venue: arXiv cs.AI 2025
Year: 2025

The recent work by Shojaee et al. (2025), titled The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity, presents a compelling empirical finding, a reasoning cliff, where the performance of Large Reasoning Models (LRMs) collapses beyond a specific complexity threshold, which the authors posit as an intrinsic scaling limitation of Chain-of-Thought (CoT) reasoning. This commentary, while acknowledging the study's methodological rigor, contends that this conclusion is confounded by experimental artifacts. We argue that the observed failure is not evidence of a fundamental cognitive boundary, but rather a predictable outcome of system-level constraints in the static, text-only evaluation paradigm, including tool use restrictions, context window recall issues, the absence of crucial cognitive baselines, inadequate statistical reporting, and output generation limits. We reframe this performance collapse through the lens of an agentic gap, asserting that the models are not failing at reasoning, but at execution within a profoundly restrictive interface. We empirically substantiate this critique by demonstrating a striking reversal. A model, initially declaring a puzzle impossible when confined to text-only generation, now employs agentic tools to not only solve it but also master variations of complexity far beyond the reasoning cliff it previously failed to surmount. Additionally, our empirical analysis of tool-enabled models like o4-mini and GPT-4o reveals a hierarchy of agentic reasoning, from simple procedural execution to complex meta-cognitive self-correction, which has significant implications for how we define and measure machine intelligence. The illusion of thinking attributed to LRMs is less a reasoning deficit and more a consequence of an otherwise capable mind lacking the tools for action.
UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Venue: arXiv cs.HC 2025
Year: 2025

Decoding human brain activity from electroencephalography (EEG) signals is a central challenge at the intersection of neuroscience and artificial intelligence, enabling diverse applications in mental state assessment, clinical monitoring, and human-machine interaction. Recent efforts have extensively explored EEG-based brain foundation models for generalized brain decoding, employing large-scale training on multiple datasets. However, most of these attempts struggle with generalizability and fail to achieve satisfactory performance without task-specific tuning due to pronounced inherent heterogeneity among decoding tasks. To address these challenges, we present UniMind, a general-purpose EEG foundation model for unified multi-task brain decoding by uniquely unleashing the power of large language models to comprehend complex neural patterns. UniMind offers several advantages. First, we design a Neuro-Language Connector to bridge the modality gap between neural signals and large language models, distilling and transforming the spatiotemporal neural patterns of EEG data into representations understandable by language models. Second, a Task-aware Query Selection module is proposed to inject task-awareness into the cross-modal alignment by dynamically generating task-adaptive query tokens, enabling learning of task-relevant neural patterns across diverse tasks. Extensive experiments across ten datasets demonstrate that UniMind substantially outperforms state-of-the-art multi-task decoding models, with an average gain of 12 percent, while also offering valuable neuroscientific insights into neural functional correlations across tasks. The code will be made publicly available.
Faster Motion Planning via Restarts
Venue: arXiv cs.RO 2025
Year: 2025

Randomized methods such as PRM and RRT are widely used in motion planning. However, in some cases, their running-time suffers from inherent instability, leading to ``catastrophic'' performance even for relatively simple instances. We apply stochastic restart techniques, some of them new, for speeding up Las Vegas algorithms, that provide dramatic speedups in practice (a factor of $3$ [or larger] in many cases). Our experiments demonstrate that the new algorithms have faster runtimes, shorter paths, and greater gains from multi-threading (when compared with straightforward parallel implementation). We prove the optimality of the new variants. Our implementation is open source, available on github, and is easy to deploy and use.
Survey of HPC in US Research Institutions
Venue: arXiv cs.DC 2025
Year: 2025

The rapid growth of AI, data-intensive science, and digital twin technologies has driven an unprecedented demand for high-performance computing (HPC) across the research ecosystem. While national laboratories and industrial hyperscalers have invested heavily in exascale and GPU-centric architectures, university-operated HPC systems remain comparatively under-resourced. This survey presents a comprehensive assessment of the HPC landscape across U.S. universities, benchmarking their capabilities against Department of Energy (DOE) leadership-class systems and industrial AI infrastructures. We examine over 50 premier research institutions, analyzing compute capacity, architectural design, governance models, and energy efficiency. Our findings reveal that university clusters, though vital for academic research, exhibit significantly lower growth trajectories (CAGR $\approx$ 18%) than their national ($\approx$ 43%) and industrial ($\approx$ 78%) counterparts. The increasing skew toward GPU-dense AI workloads has widened the capability gap, highlighting the need for federated computing, idle-GPU harvesting, and cost-sharing models. We also identify emerging paradigms, such as decentralized reinforcement learning, as promising opportunities for democratizing AI training within campus environments. Ultimately, this work provides actionable insights for academic leaders, funding agencies, and technology partners to ensure more equitable and sustainable HPC access in support of national research priorities.
Am I Playing Better Now? The Effects of G-SYNC in 60Hz Gameplay
Venue: arXiv cs.GR 2025
Year: 2025

G-SYNC technology matches formerly regular display refreshes to irregular frame updates, improving frame rates and interactive latency. In a previous study of gaming at the 30Hz frame rates common on consoles, players of Battlefield 4 were unable to discern when G-SYNC was in use, but scored higher with G-SYNC and were affected emotionally. We build on that study with the first examination of G-SYNC's effects at the 60Hz frame rate more common in PC gaming and on emerging consoles. Though G-SYNC's effects are less at 60Hz than they were at 30Hz, G-SYNC can still improve the performance of veteran players, particularly when games are challenging. G-SYNC's effects on emotion and experience were limited.
A Fourier Explanation of AI-music Artifacts
Venue: arXiv cs.SD 2025
Year: 2025

The rapid rise of generative AI has transformed music creation, with millions of users engaging in AI-generated music. Despite its popularity, concerns regarding copyright infringement, job displacement, and ethical implications have led to growing scrutiny and legal challenges. In parallel, AI-detection services have emerged, yet these systems remain largely opaque and privately controlled, mirroring the very issues they aim to address. This paper explores the fundamental properties of synthetic content and how it can be detected. Specifically, we analyze deconvolution modules commonly used in generative models and mathematically prove that their outputs exhibit systematic frequency artifacts -- manifesting as small yet distinctive spectral peaks. This phenomenon, related to the well-known checkerboard artifact, is shown to be inherent to a chosen model architecture rather than a consequence of training data or model weights. We validate our theoretical findings through extensive experiments on open-source models, as well as commercial AI-music generators such as Suno and Udio. We use these insights to propose a simple and interpretable detection criterion for AI-generated music. Despite its simplicity, our method achieves detection accuracy on par with deep learning-based approaches, surpassing 99% accuracy on several scenarios.
Analysis and experiments of the dissipative Twistcar: direction reversal and asymptotic approximations
Venue: arXiv cs.RO 2025
Year: 2025

Underactuated wheeled vehicles are commonly studied as nonholonomic systems with periodic actuation. Twistcar is a classical example inspired by a riding toy, which has been analyzed using a planar model of a dynamical system with nonholonomic constraints. Most of the previous analyses did not account for energy dissipation due to frictional resistance. In this work, we study a theoretical two-link model of the Twistcar while incorporating dissipation due to rolling resistance. We obtain asymptotic expressions for the system's small-amplitude steady-state periodic dynamics, which reveals the possibility of reversing the direction of motion upon varying the geometric and mass properties of the vehicle. Next, we design and construct a robotic prototype of the Twistcar whose center-of-mass position can be shifted by adding and removing a massive block, enabling experimental demonstration of the Twistcar's direction reversal phenomenon. We also conduct parameter fitting for the frictional resistance in order to improve agreement with experiments.
CUPID: Curating Data your Robot Loves with Influence Functions
Venue: arXiv cs.RO 2025
Year: 2025

In robot imitation learning, policy performance is tightly coupled with the quality and composition of the demonstration data. Yet, developing a precise understanding of how individual demonstrations contribute to downstream outcomes - such as closed-loop task success or failure - remains a persistent challenge. We propose CUPID, a robot data curation method based on a novel influence function-theoretic formulation for imitation learning policies. Given a set of evaluation rollouts, CUPID estimates the influence of each training demonstration on the policy's expected return. This enables ranking and selection of demonstrations according to their impact on the policy's closed-loop performance. We use CUPID to curate data by 1) filtering out training demonstrations that harm policy performance and 2) subselecting newly collected trajectories that will most improve the policy. Extensive simulated and hardware experiments show that our approach consistently identifies which data drives test-time performance. For example, training with less than 33% of curated data can yield state-of-the-art diffusion policies on the simulated RoboMimic benchmark, with similar gains observed in hardware. Furthermore, hardware experiments show that our method can identify robust strategies under distribution shift, isolate spurious correlations, and even enhance the post-training of generalist robot policies. Videos and code are made available at: https://cupid-curation.github.io.
SOF: Sorted Opacity Fields for Fast Unbounded Surface Reconstruction
Venue: arXiv cs.GR 2025
Year: 2025

Recent advances in 3D Gaussian representations have significantly improved the quality and efficiency of image-based scene reconstruction. Their explicit nature facilitates real-time rendering and fast optimization, yet extracting accurate surfaces - particularly in large-scale, unbounded environments - remains a difficult task. Many existing methods rely on approximate depth estimates and global sorting heuristics, which can introduce artifacts and limit the fidelity of the reconstructed mesh. In this paper, we present Sorted Opacity Fields (SOF), a method designed to recover detailed surfaces from 3D Gaussians with both speed and precision. Our approach improves upon prior work by introducing hierarchical resorting and a robust formulation of Gaussian depth, which better aligns with the level-set. To enhance mesh quality, we incorporate a level-set regularizer operating on the opacity field and introduce losses that encourage geometrically-consistent primitive shapes. In addition, we develop a parallelized Marching Tetrahedra algorithm tailored to our opacity formulation, reducing meshing time by up to an order of magnitude. As demonstrated by our quantitative evaluation, SOF achieves higher reconstruction accuracy while cutting total processing time by more than a factor of three. These results mark a step forward in turning efficient Gaussian-based rendering into equally efficient geometry extraction.
Thought Anchors: Which LLM Reasoning Steps Matter?
Venue: arXiv cs.LG 2025
Year: 2025

Current frontier large-language models rely on reasoning to achieve state-of-the-art performance. Many existing interpretability are limited in this area, as standard methods have been designed to study single forward passes of a model rather than the multi-token computational steps that unfold during reasoning. We argue that analyzing reasoning traces at the sentence level is a promising approach to understanding reasoning processes. We introduce a black-box method that measures each sentence's counterfactual importance by repeatedly sampling replacement sentences from the model, filtering for semantically different ones, and continuing the chain of thought from that point onwards to quantify the sentence's impact on the distribution of final answers. We discover that certain sentences can have an outsized impact on the trajectory of the reasoning trace and final answer. We term these sentences \textit{thought anchors}. These are generally planning or uncertainty management sentences, and specialized attention heads consistently attend from subsequent sentences to thought anchors. We further show that examining sentence-sentence causal links within a reasoning trace gives insight into a model's behavior. Such information can be used to predict a problem's difficulty and the extent different question domains involve sequential or diffuse reasoning. As a proof-of-concept, we demonstrate that our techniques together provide a practical toolkit for analyzing reasoning models by conducting a detailed case study of how the model solves a difficult math problem, finding that our techniques yield a consistent picture of the reasoning trace's structure. We provide an open-source tool (thought-anchors.com) for visualizing the outputs of our methods on further problems. The convergence across our methods shows the potential of sentence-level analysis for a deeper understanding of reasoning models.
Dataset of Yul Contracts to Support Solidity Compiler Research
Venue: arXiv cs.SE 2025
Year: 2025

The YulCode dataset presents a comprehensive collection of 348,840 Yul-based smart contract instances, comprising approximately 135,013 unique contracts. These contracts were generated through the compilation of Solidity source files that have been deployed on the Ethereum mainnet, making the dataset directly representative of real-world decentralized applications. YulCode provides a rich foundation for a variety of research and development tasks, including but not limited to machine learning applications, formal verification, optimization analysis, and software engineering tool evaluation in the context of low-level smart contract code. To the best of our knowledge at the time of writing, YulCode is the first and only publicly available dataset that focuses specifically on Yul, an intermediate language designed for the Ethereum Virtual Machine (EVM). As such, it fills a critical gap in the current ecosystem of smart contract datasets and opens new avenues for research and tooling aimed at low-level contract analysis and generation.
On reversible and reversible-complementary DNA codes over $\mathbb{F}_{4}$
Venue: arXiv cs.IT 2025
Year: 2025

A method to construct and count all the linear codes (of arbitrary length) in $\mathbb{F}_{4}$ that are invariant under reverse permutation and that contain the repetition code is presented. These codes are suitable for constructing DNA codes that satisfy the reverse and reverse-complement constraints. By analyzing a module-theoretic structure of these codes, their generating matrices are characterized in terms of their isomorphism type, and explicit formulas for counting them are provided. The proposed construction method based on this characterization outperforms the one given by Abualrub et al. for cyclic codes (of odd length) over $\mathbb{F}_{4}$, and the counting method solves a problem that can not be solved using the one given by Fripertinger for invariant subspaces under a linear endomorphism of $\mathbb{F}_{q}^{n}$. Additionally, several upper bounds and an identity for the minimum Hamming distance of certain reversible codes are provided.
Binsparse: A Specification for Cross-Platform Storage of Sparse Matrices and Tensors
Venue: arXiv cs.MS 2025
Year: 2025

Sparse matrices and tensors are ubiquitous throughout multiple subfields of computing. The widespread usage of sparse data has inspired many in-memory and on-disk storage formats, but the only widely adopted storage specifications are the Matrix Market and FROSTT file formats, which both use ASCII text. Due to the inefficiency of text storage, these files typically have larger file sizes and longer parsing times than binary storage formats, which directly store an in-memory representation to disk. This can be a major bottleneck; since sparse computation is often bandwidth-bound, the cost of loading or storing a matrix to disk often exceeds the cost of performing a sparse computation. While it is common practice for practitioners to develop their own, custom, non-portable binary formats for high-performance sparse matrix storage, there is currently no cross-platform binary sparse matrix storage format. We present Binsparse, a cross-platform binary sparse matrix and tensor format specification. Binsparse is a modular, embeddable format, consisting of a JSON descriptor, which describes the matrix or tensor dimensions, type, and format, and a series of binary arrays, which can be stored in all modern binary containers, such as HDF5, Zarr, or NPZ. We provide several reference implementations of Binsparse spanning 5 languages, 5 frameworks, and 4 binary containers. We evaluate our Binsparse format on every matrix in the SuiteSparse Matrix Collection and a selection of tensors from the FROSTT collection. The Binsparse HDF5 CSR format shows file size reductions of 2.4x on average without compression and 7.5x with compression. We evaluate our parser's read/write performance against a state-of-the-art Matrix Market parser, demonstrating warm cache mean read speedups of 26.5x without compression and 2.6x with compression, and write speedups of 31x without compression and 1.4x with compression.