OpenRaiser/Intern-Atlas huggingface dataset:
Simulating Student Success in the Age of GenAI: A Kantian-Axiomatic Perspective
Venue: arXiv cs.CY 2025
Year: 2025

This study reinterprets a Monte Carlo simulation of students' perceived success with generative AI (GenAI) through a Kantian-axiomatic lens. Building on prior work, theme-level survey statistics Ease of Use and Learnability, System Efficiency and Learning Burden, and Perceived Complexity and Integration from a representative dataset are used to generate 10,000 synthetic scores per theme on the [1,5] Likert scale. The simulated outputs are evaluated against the axioms of dense linear order without endpoints (DLO): irreflexivity, transitivity, total comparability (connectedness), no endpoints (no greatest and no least; A4-A5), and density (A6). At the data level, the basic ordering axioms (A1-A3) are satisfied, whereas no-endpoints (A4-A5) and density (A6) fail as expected. Likert clipping introduces minimum and maximum observed values, and a finite, discretized sample need not contain a value strictly between any two distinct scores. These patterns are read not as methodological defects but as markers of an epistemological boundary. Following Kant and Friedman, the findings suggest that what simulations capture finite, quantized observations cannot instantiate the ideal properties of an unbounded, dense continuum. Such properties belong to constructive intuition rather than to finite sampling alone. A complementary visualization contrasts the empirical histogram with a sine-curve proxy to clarify this divide. The contribution is interpretive rather than data-expansive: it reframes an existing simulation as a probe of the synthetic a priori structure underlying students' perceptions, showing how formal order-theoretic coherence coexists with principled failures of endpoint-freeness and density in finite empirical models.
The Formation of Trust in Autonomous Vehicles after Interacting with Robotaxis on Public Roads
Venue: arXiv cs.HC 2025
Year: 2025

This study investigates how pedestrian trust, receptivity, and behavior evolve during interactions with Level-4 autonomous vehicles (AVs) at uncontrolled urban intersections in a naturalistic setting. While public acceptance is critical for AV adoption, most prior studies relied on simplified simulations or field tests. We conducted a real-world experiment in a commercial Robotaxi operation zone, where 33 participants repeatedly crossed an uncontrolled intersection with frequent Level-4 Robotaxi traffic. Participants completed the Pedestrian Behavior Questionnaire (PBQ), Pedestrian Receptivity Questionnaire for Fully AVs (PRQF), pre- and post-experiment Trust in AVs Scale, and Personal Innovativeness Scale (PIS). Results showed that trust in AVs significantly increased post-experiment, with the increase positively associated with the Interaction component of PRQF. Additionally, both the Positive and Error subscales of the PBQ significantly influenced trust change. This study reveals how trust forms in real-world pedestrian-AV encounters, offering insights beyond lab-based research by accounting for population heterogeneity.
BigBang-Proton Technical Report: Next-Word-Prediction is Scientific Multitask Learner
Venue: arXiv cs.LG 2025
Year: 2025

We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world scientific tasks to construct a scientific multi-task learner. BigBang-Proton incorporates three fundamental innovations compared to mainstream general-purpose LLMs: Theory-Experiment Learning paradigm aligns large-scale numerical experimental data with theoretical text corpora; Binary Patch Encoding replaces byte pair encoding(BPE) tokenization; Monte Carlo Attention substitutes traditional transformer architectures. Through next-word-prediction pretraining on cross-discipline scientific datasets of real-world problems mixed with general textual corpus, followed by fine-tuning and inference on downstream tasks, BigBang-Proton demonstrates 100\% accuracy in up to 50-digit arithmetic addition operations, performance on par with leading specialized models in particle physics jet tagging, matching MAE of specialized models in inter-atomic potential simulation, performance comparable to traditional spatiotemporal models in water quality prediction, and benchmark-exceeding performance in genome modeling. These results prove that language-guided scientific computing can match or exceed the performance of task-specific scientific models while maintaining multitask learning capabilities. We further hypothesize to scale the pretraining to the universe scale as a fundamental step toward developing material world foundational model.
Large Language Models Inference Engines based on Spiking Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Foundational models based on the transformer architecture are currently the state-of-the-art in general language modeling, as well as in scientific areas such as material science and climate. However, training and deploying these models is computationally challenging as the time and space complexity has a quadratic relation to the input sequence length. Several efforts exploring efficient computational paradigms and model architectures to address these limitations have been made. In this work, we explore spiking neural networks (SNNs) to design transformer models. A challenge in training large-scale SNNs, using existing surrogate learning methods is inefficient and time-consuming. On the other hand, techniques to convert existing transformer-based models to their SNN equivalent are not scalable, as achieving optimal performance comes at the cost of a large number of spike time-steps, i.e. increased latency. To address this, we propose NeurTransformer, a methodology for designing transformer-based SNN for inference using a supervised fine-tuning approach with existing conversion methods. The proposed methodology works by: (1) replacing the self-attention mechanism with a spike-based self-attention (SSA), (2) converting the feed-forward block of the trained transformer model to its equivalent SNN, and (3) fine-tuning the SSA block using SNN-based surrogate learning algorithms. We benchmark the proposed methodology and demonstrate its accuracy and scalability using three variants of the GPT-2 model of increasing model size. We observe that the converted GPT-2 small models demonstrate a 5-12% loss in cosine similarity and a 9.7% reduction in perplexity. Finally, we demonstrate the energy efficiency of the SSA block compared to the ASA block and show between 64.71% and 85.28% reductions in estimated energy consumption when implementing the self-attention mechanism on a digital hardware.
Dynamic Necklace Splitting
Venue: arXiv cs.GT 2025
Year: 2025

The necklace splitting problem is a classic problem in fair division with many applications, including data-informed fair hash maps. We extend necklace splitting to a dynamic setting, allowing for relocation, insertion, and deletion of beads. We present linear-time, optimal algorithms for the two-color case that support all dynamic updates. For more than two colors, we give linear-time, optimal algorithms for relocation subject to a restriction on the number of agents. Finally, we propose a randomized algorithm for the two-color case that handles all dynamic updates, guarantees approximate fairness with high probability, and runs in polylogarithmic time when the number of agents is small.
Privacy-Preserving Learning-Augmented Data Structures
Venue: arXiv cs.IR 2025
Year: 2025

Learning-augmented data structures use predicted frequency estimates to retrieve frequently occurring database elements faster than standard data structures. Recent work has developed data structures that optimally exploit these frequency estimates while maintaining robustness to adversarial prediction errors. However, the privacy and security implications of this setting remain largely unexplored. In the event of a security breach, data structures should reveal minimal information beyond their current contents. This is even more crucial for learning-augmented data structures, whose layout adapts to the data. A data structure is history independent if its memory representation reveals no information about past operations except what is inferred from its current contents. In this work, we take the first step towards privacy and security guarantees in this setting by proposing the first learning-augmented data structure that is strongly history independent, robust, and supports dynamic updates. To achieve this, we introduce two techniques: thresholding, which automatically makes any learning-augmented data structure robust, and pairing, a simple technique that provides strong history independence in the dynamic setting. Our experimental results demonstrate a tradeoff between security and efficiency but are still competitive with the state of the art.
Object-Centric Case-Based Reasoning via Argumentation
Venue: arXiv cs.AI 2025
Year: 2025

We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orders, a One-Vs-Rest strategy for extending AA-CBR to multi-class classification, and an application of Supported AA-CBR, a bipolar variant of AA-CBR. We demonstrate that SAA-CBR is an effective classifier on the CLEVR-Hans datasets, showing competitive performance against baseline models.
Thinkquel: A Model Dedicated to Text-to-dbt Using Synthetic Data and a Span-Aware Objective
Venue: arXiv cs.AI 2025
Year: 2025

Transforming natural-language requests into reliable, production-ready data transformations remains challenging: correctness depends on precise schema linking and warehouse-specific SQL dialects, while the strongest supervision available during training--execution success and result matching--are provided only at the sequence level. At the same time, assembling large, execution-validated corpora is costly, and token-level objectives misalign with these global signals, yielding unstable optimization and limited portability. We introduce Thinkquel, a fine-tuned model for producing robust, portable, and execution-validated database queries. Methodologies in Thinkquel integrates a novel synthetic data pipeline, TS-SQL, that leverages dbt as a portable intermediate representation with a span-aware reinforcement learning objective, and Token-Sequence GRPO (TS-GRPO), specifically designed to bridge the gap between token-level training signals and sequence-level execution rewards when finetuning LLMs. On the 500-example TS-SQL test set, Thinkquel (32B) reaches 93.2% execution success and 61.8% exact-result match with a two-stage SFT curriculum, improving over the base model by 67.2% (exec.) and 44.4% (match). In Spider (14B) experiments, TS-GRPO increases training stability and speeds convergence of the execution-match reward relative to GRPO and GSPO.
A Novel Robust Control Method Combining DNN-Based NMPC Approximation and PI Control: Application to Exoskeleton Squat Movements
Venue: arXiv cs.RO 2025
Year: 2025

Nonlinear Model Predictive Control (NMPC) is a precise controller, but its heavy computational load often prevents application in robotic systems. Some studies have attempted to approximate NMPC using deep neural networks (NMPC-DNN). However, in the presence of unexpected disturbances or when operating conditions differ from training data, this approach lacks robustness, leading to large tracking errors. To address this issue, for the first time, the NMPC-DNN output is combined with a PI controller (Hybrid NMPC-DNN-PI). The proposed controller is validated by applying it to an exoskeleton robot during squat movement, which has a complex dynamic model and has received limited attention regarding robust nonlinear control design. A human-robot dynamic model with three active joints (ankle, knee, hip) is developed, and more than 5.3 million training samples are used to train the DNN. The results show that, under unseen conditions for the DNN, the tracking error in Hybrid NMPC-DNN-PI is significantly lower compared to NMPC-DNN. Moreover, human joint torques are greatly reduced with the use of the exoskeleton, with RMS values for the studied case reduced by 30.9%, 41.8%, and 29.7% at the ankle, knee, and hip, respectively. In addition, the computational cost of Hybrid NMPC-DNN-PI is 99.93% lower than that of NMPC.
Perceived Weight of Mediated Reality Sticks
Venue: arXiv cs.HC 2025
Year: 2025

Mediated reality, where augmented reality (AR) and diminished reality (DR) meet, enables visual modifications to real-world objects. A physical object with a mediated reality visual change retains its original physical properties. However, it is perceived differently from the original when interacted with. We present such a mediated reality object, a stick with different lengths or a stick with a missing portion in the middle, to investigate how users perceive its weight and center of gravity. We conducted two user studies (N=10), each of which consisted of two substudies. We found that the length of mediated reality sticks influences the perceived weight. A longer stick is perceived as lighter, and vice versa. The stick with a missing portion tends to be recognized as one continuous stick. Thus, its weight and center of gravity (COG) remain the same. We formulated the relationship between inertia based on the reported COG and perceived weight in the context of dynamic touch.
Data Melodification FM: Where Musical Rhetoric Meets Sonification
Venue: arXiv cs.HC 2025
Year: 2025

We propose a design space for data melodification, where standard visualization idioms and fundamental data characteristics map to rhetorical devices of music for a more affective experience of data. Traditional data sonification transforms data into sound by mapping it to different parameters such as pitch, volume, and duration. Often and regrettably, this mapping leaves behind melody, harmony, rhythm and other musical devices that compose the centuries-long persuasive and expressive power of music. What results is the occasional, unintentional sense of tinnitus and horror film-like impending doom caused by a disconnect between the semantics of data and sound. Through this work we ask, can the aestheticization of sonification through (classical) music theory make data simultaneously accessible, meaningful, and pleasing to one's ears?
Can AI agents understand spoken conversations about data visualizations in online meetings?
Venue: arXiv cs.HC 2025
Year: 2025

In this short paper, we present work evaluating an AI agent's understanding of spoken conversations about data visualizations in an online meeting scenario. There is growing interest in the development of AI-assistants that support meetings, such as by providing assistance with tasks or summarizing a discussion. The quality of this support depends on a model that understands the conversational dialogue. To evaluate this understanding, we introduce a dual-axis testing framework for diagnosing the AI agent's comprehension of spoken conversations about data. Using this framework, we designed a series of tests to evaluate understanding of a novel corpus of 72 spoken conversational dialogues about data visualizations. We examine diverse pipelines and model architectures, LLM vs VLM, and diverse input formats for visualizations (the chart image, its underlying source code, or a hybrid of both) to see how this affects model performance on our tests. Using our evaluation methods, we found that text-only input modalities achieved the best performance (96%) in understanding discussions of visualizations in online meetings.
Visualization Was Here: Reorienting Research When Visualizations Fade into the Background
Venue: arXiv cs.HC 2025
Year: 2025

Visualization research often centers on how visual representations generate insight, guide interpretation, or support decision-making. But in many real-world domains, visualizations do not stand out--they recede into the background, stabilized and trusted as part of the everyday infrastructure of work. This paper explores what it means to take such quiet roles seriously. Drawing on theoretical traditions from joint cognitive systems, naturalistic decision making, and infrastructure studies, I examine how visualization can become embedded in the rhythms of expert practice--less a site of intervention than a scaffold for attention, coordination, and judgment. I illustrate this reorientation with examples from mission control operations at NASA, where visualizations are deeply integrated but rarely interrogated. Rather than treat invisibility as a failure of design or innovation, I argue that visualization's infrastructural presence demands new concepts, methods, and critical sensibilities. The goal is not to diminish visualization's importance, but to broaden the field's theoretical repertoire--to recognize and support visualization-in-use even when it fades from view.
MOLM: Mixture of LoRA Markers
Venue: arXiv cs.CV 2025
Year: 2025

Generative models can generate photorealistic images at scale. This raises urgent concerns about the ability to detect synthetically generated images and attribute these images to specific sources. While watermarking has emerged as a possible solution, existing methods remain fragile to realistic distortions, susceptible to adaptive removal, and expensive to update when the underlying watermarking key changes. We propose a general watermarking framework that formulates the encoding problem as key-dependent perturbation of the parameters of a generative model. Within this framework, we introduce Mixture of LoRA Markers (MOLM), a routing-based instantiation in which binary keys activate lightweight LoRA adapters inside residual and attention blocks. This design avoids key-specific re-training and achieves the desired properties such as imperceptibility, fidelity, verifiability, and robustness. Experiments on Stable Diffusion and FLUX show that MOLM preserves image quality while achieving robust key recovery against distortions, compression and regeneration, averaging attacks, and black-box adversarial attacks on the extractor.
MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement
Venue: arXiv cs.CR 2025
Year: 2025

The widespread adoption of open-source software (OSS) necessitates the mitigation of vulnerability risks. Most vulnerability detection (VD) methods are limited by inadequate contextual understanding, restrictive single-round interactions, and coarse-grained evaluations, resulting in undesired model performance and biased evaluation results. To address these challenges, we propose MAVUL, a novel multi-agent VD system that integrates contextual reasoning and interactive refinement. Specifically, a vulnerability analyst agent is designed to flexibly leverage tool-using capabilities and contextual reasoning to achieve cross-procedural code understanding and effectively mine vulnerability patterns. Through iterative feedback and refined decision-making within cross-role agent interactions, the system achieves reliable reasoning and vulnerability prediction. Furthermore, MAVUL introduces multi-dimensional ground truth information for fine-grained evaluation, thereby enhancing evaluation accuracy and reliability. Extensive experiments conducted on a pairwise vulnerability dataset demonstrate MAVUL's superior performance. Our findings indicate that MAVUL significantly outperforms existing multi-agent systems with over 62% higher pairwise accuracy and single-agent systems with over 600% higher average performance. The system's effectiveness is markedly improved with increased communication rounds between the vulnerability analyst agent and the security architect agent, underscoring the importance of contextual reasoning in tracing vulnerability flows and the crucial feedback role. Additionally, the integrated evaluation agent serves as a critical, unbiased judge, ensuring a more accurate and reliable estimation of the system's real-world applicability by preventing misleading binary comparisons.
Cutting the Skip: Training Residual-Free Transformers
Venue: arXiv cs.LG 2025
Year: 2025

Transformers have achieved remarkable success across a wide range of applications, a feat often attributed to their scalability. Yet training them without skip (residual) connections remains notoriously difficult. While skips stabilize optimization, they also disrupt the hierarchical structure of representations, raising the long-standing question of whether transformers can be trained efficiently without them. In this work, we address this problem by analyzing the Jacobian of a skipless transformer block, showing why skips improve conditioning and revealing that their stabilization benefits can be recovered through a principled initialization strategy. Building on this insight, we introduce the first method that enables stable and efficient training of skipless transformers without altering the standard architecture. We validate our approach on Vision Transformers (ViTs) in both supervised and self-supervised settings, demonstrating that skipless ViTs trained with our initialization overcome the usual optimization barriers, learn richer hierarchical representations, and outperform strong baselines, that incorporate skip connections, on dense prediction benchmarks. These results show that skip connections are not a fundamental requirement for training ViTs and open new avenues for hierarchical representation learning in vision models.
Initial Distribution Sensitivity of Constrained Markov Decision Processes
Venue: arXiv cs.LG 2025
Year: 2025

Constrained Markov Decision Processes (CMDPs) are notably more complex to solve than standard MDPs due to the absence of universally optimal policies across all initial state distributions. This necessitates re-solving the CMDP whenever the initial distribution changes. In this work, we analyze how the optimal value of CMDPs varies with different initial distributions, deriving bounds on these variations using duality analysis of CMDPs and perturbation analysis in linear programming. Moreover, we show how such bounds can be used to analyze the regret of a given policy due to unknown variations of the initial distribution.
Continual Learning with Query-Only Attention
Venue: arXiv cs.LG 2025
Year: 2025

Continual learning involves learning from a stream of data without repetition of data points, a scenario that is inherently complex due to distributional shift across tasks. We propose a query-only attention mechanism that discards keys and values, yet preserves the core inductive bias of transformer architectures. In continual learning scenarios, this simplified mechanism significantly mitigates both loss of plasticity and catastrophic forgetting, outperforming baselines such as selective re-initialization. We establish a conceptual link between query-only attention, full transformer attention, and model agnostic meta-learning, framing them as instances of meta-learning. We further provide intuition for why query-based models and attention networks help preserve plasticity in continual settings. Finally, through preliminary Hessian spectrum analysis, we observe that models maintaining higher curvature rank across tasks tend to retain plasticity. Our findings suggest that full attention may not be essential for capturing the benefits of meta-learning in continual learning.