OpenRaiser/Intern-Atlas huggingface dataset:
Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

Reinforcement Learning (RL) has proven highly effective at enhancing the complex reasoning abilities of Large Language Models (LLMs), yet underlying mechanisms driving this success remain largely opaque. Our analysis reveals that puzzling phenomena like ``aha moments", ``length-scaling'' and entropy dynamics are not disparate occurrences but hallmarks of an emergent reasoning hierarchy, akin to the separation of high-level strategic planning from low-level procedural execution in human cognition. We uncover a compelling two-phase dynamic: initially, a model is constrained by procedural correctness and must improve its low-level skills. The learning bottleneck then decisively shifts, with performance gains being driven by the exploration and mastery of high-level strategic planning. This insight exposes a core inefficiency in prevailing RL algorithms like GRPO, which apply optimization pressure agnostically and dilute the learning signal across all tokens. To address this, we propose Hierarchy-Aware Credit Assignment (HICRA), an algorithm that concentrates optimization efforts on high-impact planning tokens. Our extensive experiments validate that HICRA significantly outperforms strong baselines, and offer deep insights into how reasoning advances through the lens of strategic exploration.
Parse Tree Tracking Through Time for Programming Process Analysis at Scale
Venue: arXiv cs.SE 2025
Year: 2025

Background and Context: Programming process data can be utilized to understand the processes students use to write computer programming assignments. Keystroke- and line-level event logs have been used in the past in various ways, primarily in high-level descriptive statistics (e.g., timings, character deletion rate, etc). Analysis of behavior in context (e.g., how much time students spend working on loops) has been cumbersome because of our inability to automatically track high-level code representations, such as abstract syntax trees, through time and unparseable states. Objective: Our study has two goals. The first is to design the first algorithm that tracks parse tree nodes through time. Second, we utilize this algorithm to perform a partial replication study of prior work that used manual tracking of code representations, as well as other novel analyses of student programming behavior that can now be done at scale. Method: We use two algorithms presented in this paper to track parse tree nodes through time and construct tree representations for unparseable code states. We apply these algorithms to a public keystroke data from student coursework in a 2021 CS1 course and conduct analysis on the resulting parse trees. Findings: We discover newly observable statistics at scale, including that code is deleted at similar rates inside and outside of conditionals and loops, a third of commented out code is eventually restored, and that frequency with which students jump around in their code may not be indicative of struggle. Implications: The ability to track parse trees through time opens the door to understanding new dimensions of student programming, such as best practices of structural development of code over time, quantitative measurement of what syntactic constructs students struggle most with, refactoring behavior, and attention shifting within the code.
Promisedland: An XR Narrative Attraction Integrating Diorama-to-Virtual Workflow and Elemental Storytelling
Venue: arXiv cs.HC 2025
Year: 2025

Promisedland is a mixed-reality (MR) narrative attraction that combines cultural storytelling, ecological education, and an innovative hybrid production workflow. Set in a future Earth suffering from elemental imbalance, users embark on an interactive journey guided by symbolic characters to restore harmony through the collection of five classical elements: metal, wood, water, fire, and earth. To prototype this experience, we introduce a low-cost, high-fidelity Diorama-to-Virtual pipeline - handcrafting physical scale models, 3D scanning, and integrating them into Unreal Engine. This process enables rapid spatial prototyping while preserving the material expressiveness and narrative consistency of the physical environment. To further enhance immersion, the experience incorporates a Stewart Platform to provide motion feedback synchronized with the virtual ride dynamics, reinforcing spatial presence and embodied engagement. The final prototype runs on Meta Quest, supporting dynamic interactions and real-time visual feedback. Promisedland offers a replicable design blueprint for future XR narrative installations across museums, cultural exhibitions, and themed entertainment. It proposes a new framework for XR Narrative Attractions - where physical and digital elements converge to deepen immersion, agency, and emotional engagement.
Designing Effective AI Explanations for Misinformation Detection: A Comparative Study of Content, Social, and Combined Explanations
Venue: arXiv cs.HC 2025
Year: 2025

In this paper, we study the problem of AI explanation of misinformation, where the goal is to identify explanation designs that help improve users' misinformation detection abilities and their overall user experiences. Our work is motivated by the limitations of current Explainable AI (XAI) approaches, which predominantly focus on content explanations that elucidate the linguistic features and sentence structures of the misinformation. To address this limitation, we explore various explanations beyond content explanation, such as "social explanation" that considers the broader social context surrounding misinformation, as well as a "combined explanation" where both the content and social explanations are presented in scenarios that are either aligned or misaligned with each other. To evaluate the comparative effectiveness of these AI explanations, we conduct two online crowdsourcing experiments in the COVID-19 (Study 1 on Prolific) and Politics domains (Study 2 on MTurk). Our results show that AI explanations are generally effective in aiding users to detect misinformation, with effectiveness significantly influenced by the alignment between content and social explanations. We also find that the order in which explanation types are presented - specifically, whether a content or social explanation comes first - can influence detection accuracy, with differences found between the COVID-19 and Political domains. This work contributes towards more effective design of AI explanations, fostering a deeper understanding of how different explanation types and their combinations influence misinformation detection.
EmbedOR: Provable Cluster-Preserving Visualizations with Curvature-Based Stochastic Neighbor Embeddings
Venue: arXiv cs.LG 2025
Year: 2025

Stochastic Neighbor Embedding (SNE) algorithms like UMAP and tSNE often produce visualizations that do not preserve the geometry of noisy and high dimensional data. In particular, they can spuriously separate connected components of the underlying data submanifold and can fail to find clusters in well-clusterable data. To address these limitations, we propose EmbedOR, a SNE algorithm that incorporates discrete graph curvature. Our algorithm stochastically embeds the data using a curvature-enhanced distance metric that emphasizes underlying cluster structure. Critically, we prove that the EmbedOR distance metric extends consistency results for tSNE to a much broader class of datasets. We also describe extensive experiments on synthetic and real data that demonstrate the visualization and geometry-preservation capabilities of EmbedOR. We find that, unlike other SNE algorithms and UMAP, EmbedOR is much less likely to fragment continuous, high-density regions of the data. Finally, we demonstrate that the EmbedOR distance metric can be used as a tool to annotate existing visualizations to identify fragmentation and provide deeper insight into the underlying geometry of the data.
PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming
Venue: arXiv cs.AI 2025
Year: 2025

Recent developments in AI governance and safety research have called for red-teaming methods that can effectively surface potential risks posed by AI models. Many of these calls have emphasized how the identities and backgrounds of red-teamers can shape their red-teaming strategies, and thus the kinds of risks they are likely to uncover. While automated red-teaming approaches promise to complement human red-teaming by enabling larger-scale exploration of model behavior, current approaches do not consider the role of identity. As an initial step towards incorporating people's background and identities in automated red-teaming, we develop and evaluate a novel method, PersonaTeaming, that introduces personas in the adversarial prompt generation process to explore a wider spectrum of adversarial strategies. In particular, we first introduce a methodology for mutating prompts based on either "red-teaming expert" personas or "regular AI user" personas. We then develop a dynamic persona-generating algorithm that automatically generates various persona types adaptive to different seed prompts. In addition, we develop a set of new metrics to explicitly measure the "mutation distance" to complement existing diversity measurements of adversarial prompts. Our experiments show promising improvements (up to 144.1%) in the attack success rates of adversarial prompts through persona mutation, while maintaining prompt diversity, compared to RainbowPlus, a state-of-the-art automated red-teaming method. We discuss the strengths and limitations of different persona types and mutation methods, shedding light on future opportunities to explore complementarities between automated and human red-teaming approaches.
How fast can you find a good hypothesis?
Venue: arXiv cs.DS 2025
Year: 2025

In the hypothesis selection problem, we are given sample and query access to finite set of candidate distributions (hypotheses), $\mathcal{H} = \{H_1, \ldots, H_n\}$, and samples from an unknown distribution $P$, both over a domain $\mathcal{X}$. The goal is to output a distribution $Q$ whose distance to $P$ is comparable to that of the nearest hypothesis in $\mathcal{H}$. Specifically, if the minimum distance is $\mathsf{OPT}$, we aim to output $Q$ such that, with probability at least $1-δ$, its total variation distance to $P$ is at most $C \cdot \mathsf{OPT} + \varepsilon$. The optimal approximation for proper algorithms (where $Q \in \mathcal{H}$) is $C=3$ using $Θ(\log(n/δ)/\varepsilon^2)$ samples from $P$ and for improper algorithms (where $Q$ is not necessarily in $\mathcal{H}$) is $C=2$ using $\tildeΘ(\log(n/δ)/\varepsilon^2)$ samples from $P$. In the improper setting, the algorithm achieving $C=2$ [Bousquet, Braverman, Kol, Efremenko, Moran, FOCS 2021] runs in time which grows polynomially with $|\mathcal{X}|$ -- it does not run in finite time for real-valued distributions. A promising path towards improved runtime is to consider improper algorithms which output a mixture $Q$ of the hypotheses as such a distribution can be represented in $n$ words of memory. We show (1) a lower bound that no algorithm which outputs a mixture can achieve approximation better than $C = 3-2/n$ unless the number of samples is polynomial in $|\mathcal{X}|$, as well as (2) an algorithm which runs in time $\text{poly}(n)$ and achieves the same approximation guarantee. In the proper setting, [Aliakbarpour, Bun, Smith, NeurIPS 2024] provided an algorithm with $C=3$ running in $\tilde{O}(n/(δ^3\varepsilon^3))$ time. We improve this time complexity to $\tilde{O}(n/(δ\varepsilon^2))$, significantly reducing the dependence on the confidence and error parameters.
Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support
Venue: arXiv cs.HC 2025
Year: 2025

Eye-tracking offers rich insights into student cognition and engagement, but remains underutilized in classroom-facing educational technology due to challenges in data interpretation and accessibility. In this paper, we present the iterative design and evaluation of a gaze-based learning analytics dashboard for English Language Arts (ELA), developed through five studies involving teachers and students. Guided by user-centered design and data storytelling principles, we explored how gaze data can support reflection, formative assessment, and instructional decision-making. Our findings demonstrate that gaze analytics can be approachable and pedagogically valuable when supported by familiar visualizations, layered explanations, and narrative scaffolds. We further show how a conversational agent, powered by a large language model (LLM), can lower cognitive barriers to interpreting gaze data by enabling natural language interactions with multimodal learning analytics. We conclude with design implications for future EdTech systems that aim to integrate novel data modalities in classroom contexts.
Distributed Download from an External Data Source in Asynchronous Faulty Settings
Venue: arXiv cs.DC 2025
Year: 2025

The distributedData Retrieval (DR) model consists of $k$ peers connected by a complete peer-to-peer communication network, and a trusted external data source that stores an array $\textbf{X}$ of $n$ bits ($n \gg k$). Up to $βk$ of the peers might fail in any execution (for $β\in [0, 1)$). Peers can obtain the information either by inexpensive messages passed among themselves or through expensive queries to the source array $\textbf{X}$. In the DR model, we focus on designing protocols that minimize the number of queries performed by any nonfaulty peer (a measure referred to as query complexity) while maximizing the resilience parameter $β$. The Download problem requires each nonfaulty peer to correctly learn the entire array $\textbf{X}$. Earlier work on this problem focused on synchronous communication networks and established several deterministic and randomized upper and lower bounds. Our work is the first to extend the study of distributed data retrieval to asynchronous communication networks. We address the Download problem under both the Byzantine and crash failure models. We present query-optimal deterministic solutions in an asynchronous model that can tolerate any fixed fraction $β<1$ of crash faults. In the Byzantine failure model, it is known that deterministic protocols incur a query complexity of $Ω(n)$ per peer, even under synchrony. We extend this lower bound to randomized protocols in the asynchronous model for $β\geq 1/2$, and further show that for $β< 1/2$, a randomized protocol exists with near-optimal query complexity. To the best of our knowledge, this is the first work to address the Download problem in asynchronous communication networks.
Learning functions through Diffusion Maps
Venue: arXiv cs.LG 2025
Year: 2025

We propose a data-driven method for approximating real-valued functions on smooth manifolds, building on the Diffusion Maps framework under the manifold hypothesis. Given pointwise evaluations of a function, the method constructs a smooth extension to the ambient space by exploiting diffusion geometry and its connection to the heat equation and the Laplace-Beltrami operator. To address the computational challenges of high-dimensional data, we introduce a dimensionality reduction strategy based on the low-rank structure of the distance matrix, revealed via singular value decomposition (SVD). In addition, we develop an online updating mechanism that enables efficient incorporation of new data, thereby improving scalability and reducing computational cost. Numerical experiments, including applications to sparse CT reconstruction, demonstrate that the proposed methodology outperforms classical feedforward neural networks and interpolation methods in terms of both accuracy and efficiency.
RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs
Venue: arXiv cs.AI 2025
Year: 2025

Accuracy and safety are paramount in Offshore Wind (OSW) maintenance, yet conventional Large Language Models (LLMs) often fail when confronted with highly specialised or unexpected scenarios. We introduce RAGuard, an enhanced Retrieval-Augmented Generation (RAG) framework that explicitly integrates safety-critical documents alongside technical manuals.By issuing parallel queries to two indices and allocating separate retrieval budgets for knowledge and safety, RAGuard guarantees both technical depth and safety coverage. We further develop a SafetyClamp extension that fetches a larger candidate pool, "hard-clamping" exact slot guarantees to safety. We evaluate across sparse (BM25), dense (Dense Passage Retrieval) and hybrid retrieval paradigms, measuring Technical Recall@K and Safety Recall@K. Both proposed extensions of RAG show an increase in Safety Recall@K from almost 0\% in RAG to more than 50\% in RAGuard, while maintaining Technical Recall above 60\%. These results demonstrate that RAGuard and SafetyClamp have the potential to establish a new standard for integrating safety assurance into LLM-powered decision support in critical maintenance contexts.
What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning?
Venue: arXiv cs.LG 2025
Year: 2025

Sparse-reward reinforcement learning (RL) remains fundamentally hard: without structure, any agent needs $Ω(|\mathcal{S}||\mathcal{A}|/p)$ samples to recover rewards. We introduce Policy-Aware Matrix Completion (PAMC) as a first concrete step toward a structural reward learning framework. Our key idea is to exploit approximate low-rank + sparse structure in the reward matrix, under policy-biased (MNAR) sampling. We prove recovery guarantees with inverse-propensity weighting, and establish a visitation-weighted error-to-regret bound linking completion error to control performance. Importantly, when assumptions weaken, PAMC degrades gracefully: confidence intervals widen and the algorithm abstains, ensuring safe fallback to exploration. Empirically, PAMC improves sample efficiency across Atari-26 (10M steps), DM Control, MetaWorld MT50, D4RL offline RL, and preference-based RL benchmarks, outperforming DrQ-v2, DreamerV3, Agent57, T-REX/D-REX, and PrefPPO under compute-normalized comparisons. Our results highlight PAMC as a practical and principled tool when structural rewards exist, and as a concrete first instantiation of a broader structural reward learning perspective.
Fitting Image Diffusion Models on Video Datasets
Venue: arXiv cs.CV 2025
Year: 2025

Image diffusion models are trained on independently sampled static images. While this is the bedrock task protocol in generative modeling, capturing the temporal world through the lens of static snapshots is information-deficient by design. This limitation leads to slower convergence, limited distributional coverage, and reduced generalization. In this work, we propose a simple and effective training strategy that leverages the temporal inductive bias present in continuous video frames to improve diffusion training. Notably, the proposed method requires no architectural modification and can be seamlessly integrated into standard diffusion training pipelines. We evaluate our method on the HandCo dataset, where hand-object interactions exhibit dense temporal coherence and subtle variations in finger articulation often result in semantically distinct motions. Empirically, our method accelerates convergence by over 2$\text{x}$ faster and achieves lower FID on both training and validation distributions. It also improves generative diversity by encouraging the model to capture meaningful temporal variations. We further provide an optimization analysis showing that our regularization reduces the gradient variance, which contributes to faster convergence.
Measuring How (Not Just Whether) VLMs Build Common Ground
Venue: arXiv cs.CL 2025
Year: 2025

Large vision language models (VLMs) increasingly claim reasoning skills, yet current benchmarks evaluate them in single-turn or question answering settings. However, grounding is an interactive process in which people gradually develop shared understanding through ongoing communication. We introduce a four-metric suite (grounding efficiency, content alignment, lexical adaptation, and human-likeness) to systematically evaluate VLM performance in interactive grounding contexts. We deploy the suite on 150 self-play sessions of interactive referential games between three proprietary VLMs and compare them with human dyads. All three models diverge from human patterns on at least three metrics, while GPT4o-mini is the closest overall. We find that (i) task success scores do not indicate successful grounding and (ii) high image-utterance alignment does not necessarily predict task success. Our metric suite and findings offer a framework for future research on VLM grounding.
A Versatile and Programmable UAV Platform for Radio Access Network and End-to-End Cellular Measurements
Venue: arXiv cs.NI 2025
Year: 2025

In this work, we develop a measurement platform to capture mobile network performance metrics including coverage and quality of service in regions where conventional coverage testing approaches are frequently time-intensive, labor-demanding, and occasionally hazardous. Traditionally, crowd-sourcing methods are used to collect cellular network performance metrics. However, these approaches are inadequate in rural areas due to low-density population, and difficult terrain. The platform described here is a UAV-based and is designed to investigate the mobile network performance through aerial operations and gather Radio Access Network (RAN) signal alongside end-to-end network performance metrics. Our platform gathers metrics through the integration of an onboard computation unit and commercial off-the-shelf cellular modem. The gathered data are subsequently analyzed and displayed using geospatial mapping utilities and statistical techniques to deliver key observations on cellular network performance. Experimental results showed that the received signal power improves at higher altitudes due to enhanced line-of-sight (LoS) conditions as expected. However, the signal quality degrades as a result of increased interference from neighboring cells. The analysis reveals that for most of the geographic area covered in the initial experiments the system maintained acceptable signal quality, with adequate throughput performance for both uplink and downlink communications, while maintaining satisfactory round-trip time characteristics. Notably, the experiment showed that a strong radio signal metric for a given cell does not necessarily translate to consistent spatial coverage across the tested region.
Predicting Traffic Accident Severity with Deep Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Traffic accidents can be studied to mitigate the risk of further events. Recent advances in machine learning have provided an alternative way to study data associated with traffic accidents. New models achieve good generalization and high predictive power over imbalanced data. In this research, we study neural network-based models on data related to traffic accidents. We begin analyzing relative feature colinearity and unsupervised dimensionality reduction through autoencoders, followed by a dense network. The features are related to traffic accident data and the target is to classify accident severity. Our experiments show cross-validated results of up to 92% accuracy when classifying accident severity using the proposed deep neural network.