OpenRaiser/Intern-Atlas huggingface dataset:
Quadrupedal Robot Skateboard Mounting via Reverse Curriculum Learning
Venue: arXiv cs.RO 2025
Year: 2025

The aim of this work is to enable quadrupedal robots to mount skateboards using Reverse Curriculum Reinforcement Learning. Although prior work has demonstrated skateboarding for quadrupeds that are already positioned on the board, the initial mounting phase still poses a significant challenge. A goal-oriented methodology was adopted, beginning with the terminal phases of the task and progressively increasing the complexity of the problem definition to approximate the desired objective. The learning process was initiated with the skateboard rigidly fixed within the global coordinate frame and the robot positioned directly above it. Through gradual relaxation of these initial conditions, the learned policy demonstrated robustness to variations in skateboard position and orientation, ultimately exhibiting a successful transfer to scenarios involving a mobile skateboard. The code, trained models, and reproducible examples are available at the following link: https://github.com/dancher00/quadruped-skateboard-mounting
Gaussian Wave Splatting for Computer-Generated Holography
Venue: arXiv cs.GR 2025
Year: 2025

State-of-the-art neural rendering methods optimize Gaussian scene representations from a few photographs for novel-view synthesis. Building on these representations, we develop an efficient algorithm, dubbed Gaussian Wave Splatting, to turn these Gaussians into holograms. Unlike existing computer-generated holography (CGH) algorithms, Gaussian Wave Splatting supports accurate occlusions and view-dependent effects for photorealistic scenes by leveraging recent advances in neural rendering. Specifically, we derive a closed-form solution for a 2D Gaussian-to-hologram transform that supports occlusions and alpha blending. Inspired by classic computer graphics techniques, we also derive an efficient approximation of the aforementioned process in the Fourier domain that is easily parallelizable and implement it using custom CUDA kernels. By integrating emerging neural rendering pipelines with holographic display technology, our Gaussian-based CGH framework paves the way for next-generation holographic displays.
Batch Augmentation with Unimodal Fine-tuning for Multimodal Learning
Venue: arXiv cs.CV 2025
Year: 2025

This paper proposes batch augmentation with unimodal fine-tuning to detect the fetus's organs from ultrasound images and associated clinical textual information. We also prescribe pre-training initial layers with investigated medical data before the multimodal training. At first, we apply a transferred initialization with the unimodal image portion of the dataset with batch augmentation. This step adjusts the initial layer weights for medical data. Then, we apply neural networks (NNs) with fine-tuned initial layers to images in batches with batch augmentation to obtain features. We also extract information from descriptions of images. We combine this information with features obtained from images to train the head layer. We write a dataloader script to load the multimodal data and use existing unimodal image augmentation techniques with batch augmentation for the multimodal data. The dataloader brings a new random augmentation for each batch to get a good generalization. We investigate the FPU23 ultrasound and UPMC Food-101 multimodal datasets. The multimodal large language model (LLM) with the proposed training provides the best results among the investigated methods. We receive near state-of-the-art (SOTA) performance on the UPMC Food-101 dataset. We share the scripts of the proposed method with traditional counterparts at the following repository: github.com/dipuk0506/multimodal
Integrating Video and Text: A Balanced Approach to Multimodal Summary Generation and Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

Vision-Language Models (VLMs) often struggle to balance visual and textual information when summarizing complex multimodal inputs, such as entire TV show episodes. In this paper, we propose a zero-shot video-to-text summarization approach that builds its own screenplay representation of an episode, effectively integrating key video moments, dialogue, and character information into a unified document. Unlike previous approaches, we simultaneously generate screenplays and name the characters in zero-shot, using only the audio, video, and transcripts as input. Additionally, we highlight that existing summarization metrics can fail to assess the multimodal content in summaries. To address this, we introduce MFactSum, a multimodal metric that evaluates summaries with respect to both vision and text modalities. Using MFactSum, we evaluate our screenplay summaries on the SummScreen3D dataset, demonstrating superiority against state-of-the-art VLMs such as Gemini 1.5 by generating summaries containing 20% more relevant visual information while requiring 75% less of the video as input.
Attention Is Not All You Need: The Importance of Feedforward Networks in Transformer Models
Venue: arXiv cs.CL 2025
Year: 2025

Decoder-only transformer networks have become incredibly popular for language modeling tasks. State-of-the-art models can have over a hundred transformer blocks, containing billions of trainable parameters, and are trained on trillions of tokens of text. Each transformer block typically consists of a multi-head attention (MHA) mechanism and a two-layer fully connected feedforward network (FFN). In this paper, we examine the importance of the FFN during the model pre-training process through a series of experiments, confirming that the FFN is important to model performance. Furthermore, we show that models using a transformer block configuration with three-layer FFNs with fewer such blocks outperform the standard two-layer configuration delivering lower training loss with fewer total parameters in less time.
3D Characterization of Smoke Plume Dispersion Using Multi-View Drone Swarm
Venue: arXiv cs.RO 2025
Year: 2025

This study presents an advanced multi-view drone swarm imaging system for the three-dimensional characterization of smoke plume dispersion dynamics. The system comprises a manager drone and four worker drones, each equipped with high-resolution cameras and precise GPS modules. The manager drone uses image feedback to autonomously detect and position itself above the plume, then commands the worker drones to orbit the area in a synchronized circular flight pattern, capturing multi-angle images. The camera poses of these images are first estimated, then the images are grouped in batches and processed using Neural Radiance Fields (NeRF) to generate high-resolution 3D reconstructions of plume dynamics over time. Field tests demonstrated the ability of the system to capture critical plume characteristics including volume dynamics, wind-driven directional shifts, and lofting behavior at a temporal resolution of about 1 s. The 3D reconstructions generated by this system provide unique field data for enhancing the predictive models of smoke plume dispersion and fire spread. Broadly, the drone swarm system offers a versatile platform for high resolution measurements of pollutant emissions and transport in wildfires, volcanic eruptions, prescribed burns, and industrial processes, ultimately supporting more effective fire control decisions and mitigating wildfire risks.
Practical Reasoning Interruption Attacks on Reasoning Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

Reasoning large language models (RLLMs) have demonstrated outstanding performance across a variety of tasks, yet they also expose numerous security vulnerabilities. Most of these vulnerabilities have centered on the generation of unsafe content. However, recent work has identified a distinct "thinking-stopped" vulnerability in DeepSeek-R1: under adversarial prompts, the model's reasoning process ceases at the system level and produces an empty final answer. Building upon this vulnerability, researchers developed a novel prompt injection attack, termed reasoning interruption attack, and also offered an initial analysis of its root cause. Through extensive experiments, we verify the previous analyses, correct key errors based on three experimental findings, and present a more rigorous explanation of the fundamental causes driving the vulnerability. Moreover, existing attacks typically require over 2,000 tokens, impose significant overhead, reduce practicality, and are easily detected. To overcome these limitations, we propose the first practical reasoning interruption attack. It succeeds with just 109 tokens by exploiting our newly uncovered "reasoning token overflow" (RTO) effect to overwrite the model's final answer, forcing it to return an invalid response. Experimental results demonstrate that our proposed attack is highly effective. Furthermore, we discover that the method for triggering RTO differs between the official DeepSeek-R1 release and common unofficial deployments. As a broadened application of RTO, we also construct a novel jailbreak attack that enables the transfer of unsafe content within the reasoning tokens into final answer, thereby exposing it to the user. Our work carries significant implications for enhancing the security of RLLMs.
A Formal Verification Approach to Safeguard Controller Variables from Single Event Upset
Venue: arXiv cs.SE 2025
Year: 2025

We present a method based on program analysis and formal verification to identify conditionally relevant variables (CRVs) - variables which could lead to violation of safety properties in control software when affected by single event upsets (SEUs). Traditional static analysis can distinguish between relevant and irrelevant variables. However, it would fail to take into account the conditions specific to the control software in question. This can lead to false positives. Our algorithm employs formal verification to avoid false positives. We have conducted experiments that demonstrate that CRVs indeed are fewer in number than what traditional static analysis can detect and that our algorithm is able to identify this fact. The information provided by our algorithm could prove helpful to a compiler while it does register allocation during the compilation of the control software. In turn, this could cause significant reduction in the cost of controller chips.
Enfoque Odychess: Un método dialéctico, constructivista y adaptativo para la enseñanza del ajedrez con inteligencias artificiales generativas
Venue: arXiv cs.CY 2025
Year: 2025

Chess teaching has evolved through different approaches, however, traditional methodologies, often based on memorization, contrast with the new possibilities offered by generative artificial intelligence, a technology still little explored in this field. This study seeks to empirically validate the effectiveness of the Odychess Approach in improving chess knowledge, strategic understanding, and metacognitive skills in students. A quasi-experimental study was conducted with a pre-test/post-test design and a control group (N=60). The experimental intervention implemented the Odychess Approach, incorporating a Llama 3.3 language model that was specifically adapted using Parameter-Efficient Fine-Tuning (PEFT) techniques to act as a Socratic chess tutor. Quantitative assessment instruments were used to measure chess knowledge, strategic understanding, and metacognitive skills before and after the intervention. The results of the quasi-experimental study showed significant improvements in the experimental group compared to the control group in the three variables analyzed: chess knowledge, strategic understanding, and metacognitive skills. The complementary qualitative analysis revealed greater analytical depth, more developed dialectical reasoning, and increased intrinsic motivation in students who participated in the Odychess method-based intervention. The Odychess Approach represents an effective pedagogical methodology for teaching chess, demonstrating the potential of the synergistic integration of constructivist and dialectical principles with generative artificial intelligence. The implications of this work are relevant for educators and institutions interested in adopting innovative pedagogical technologies and for researchers in the field of AI applied to education, highlighting the transferability of the language model adaptation methodology to other educational domains.
A Gpu-based solution for large-scale skeletal animation simulation
Venue: arXiv cs.GR 2025
Year: 2025

Skeletal animations of large-scale characters are widely used in video games. However, with a large number of characters are involved, relying on the CPU to calculate skeletal animations leads to significant performance problems. There are two main types of traditional GPU- based solutions. One is referred to as pre-baked animation texture technology. The problem with this solution is that it can only play animations from the pre-baked animation. It is impossible to perform interpolation, blending and other calculations on the animation, which affects the quality of the animations. The other solution is referred to as dedicated processing with a simple skeleton hierarchy (the number of skeleton levels < 64). This option does not need to simulate and bake animation data in advance. However, performance is dramatically impaired when processing complex skeletons with too many skeleton levels (such as fluttering clothing, soft plants, dragon-like creatures, etc.). In order to solve these issues, we developed a parallel prefix tree update solution to optimize the animation update process of complex skeletons with too many levels, and combined traditional solutions to implement a GPU-based skeletal animation solution. This solution does not need to simulate and bake animation results. In addition, the performance is superior to traditional solutions for complex skeletons with too many levels. Our work can provide a new option for optimizing the performance of large-scale skeletal animation simulations, providing GPU-based skeletal animations a wider range of application scenarios.
On Finding Randomly Planted Cliques in Arbitrary Graphs
Venue: arXiv cs.CC 2025
Year: 2025

We study a planted clique model introduced by Feige where a complete graph of size $c\cdot n$ is planted uniformly at random in an arbitrary $n$-vertex graph. We give a simple deterministic algorithm that, in almost linear time, recovers a clique of size $(c/3)^{O(1/c)} \cdot n$ as long as the original graph has maximum degree at most $(1-p)n$ for some fixed $p>0$. The proof hinges on showing that the degrees of the final graph are correlated with the planted clique, in a way similar to (but more intricate than) the classical $G(n,\frac{1}{2})+K_{\sqrt{n}}$ planted clique model. Our algorithm suggests a separation from the worst-case model, where, assuming the Unique Games Conjecture, no polynomial algorithm can find cliques of size $Ω(n)$ for every fixed $c>0$, even if the input graph has maximum degree $(1-p)n$. Our techniques extend beyond the planted clique model. For example, when the planted graph is a balanced biclique, we recover a balanced biclique of size larger than the best guarantees known for the worst case.
Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning
Venue: arXiv cs.LG 2025
Year: 2025

Coded computing is one of the techniques that can be used for privacy protection in Federated Learning. However, most of the constructions used for coded computing work only under the assumption that the computations involved are exact, generally restricted to special classes of functions, and require quantized inputs. This paper considers the use of Private Berrut Approximate Coded Computing (PBACC) as a general solution to add strong but non-perfect privacy to federated learning. We derive new adapted PBACC algorithms for centralized aggregation, secure distributed training with centralized data, and secure decentralized training with decentralized data, thus enlarging significantly the applications of the method and the existing privacy protection tools available for these paradigms. Particularly, PBACC can be used robustly to attain privacy guarantees in decentralized federated learning for a variety of models. Our numerical results show that the achievable quality of different learning models (convolutional neural networks, variational autoencoders, and Cox regression) is minimally altered by using these new computing schemes, and that the privacy leakage can be bounded strictly to less than a fraction of one bit per participant. Additionally, the computational cost of the encoding and decoding processes depends only of the degree of decentralization of the data.
Improving 5G/B5G Network Performance with RFID-Enabled Resource Management Systems
Venue: arXiv cs.NI 2025
Year: 2025

In the rapidly evolving landscape of 5G and B5G (beyond 5G) networks, efficient resource optimization is critical to addressing the escalating demands for high-speed, low-latency, and energy efficient communication. This study explores the integration of Radio Frequency Identification (RFID) technology as a novel approach to enhance resource management in 5G/B5G networks. The motivation behind this research lies in overcoming persistent challenges such as spectrum congestion, high latency, and inefficient load balancing, which impede the performance of traditional resource allocation methods. To achieve this, RFID tags were embedded in critical network components, including user devices, base stations, and Internet of Things (IoT) nodes, enabling the collection of real-time data on device status, location, and resource utilization. RFID readers strategically placed across the network continuously captured this data, which was processed by a centralized controller using a custom-designed optimization algorithm. This algorithm dynamically managed key network resources, including spectrum allocation, load balancing, and energy consumption, ensuring efficient operation under varying network conditions. Simulations were conducted to evaluate the performance of the RFID-based model against traditional 4G dynamic resource allocation techniques. The results demonstrated substantial improvements in key performance metrics.
Value Iteration with Guessing for Markov Chains and Markov Decision Processes
Venue: arXiv cs.AI 2025
Year: 2025

Two standard models for probabilistic systems are Markov chains (MCs) and Markov decision processes (MDPs). Classic objectives for such probabilistic models for control and planning problems are reachability and stochastic shortest path. The widely studied algorithmic approach for these problems is the Value Iteration (VI) algorithm which iteratively applies local updates called Bellman updates. There are many practical approaches for VI in the literature but they all require exponentially many Bellman updates for MCs in the worst case. A preprocessing step is an algorithm that is discrete, graph-theoretical, and requires linear space. An important open question is whether, after a polynomial-time preprocessing, VI can be achieved with sub-exponentially many Bellman updates. In this work, we present a new approach for VI based on guessing values. Our theoretical contributions are twofold. First, for MCs, we present an almost-linear-time preprocessing algorithm after which, along with guessing values, VI requires only subexponentially many Bellman updates. Second, we present an improved analysis of the speed of convergence of VI for MDPs. Finally, we present a practical algorithm for MDPs based on our new approach. Experimental results show that our approach provides a considerable improvement over existing VI-based approaches on several benchmark examples from the literature.
Utilizing LLMs to Investigate the Disputed Role of Evidence in Electronic Cigarette Health Policy Formation in Australia and the UK
Venue: arXiv cs.CL 2025
Year: 2025

Australia and the UK have developed contrasting approaches to the regulation of electronic cigarettes, with - broadly speaking - Australia adopting a relatively restrictive approach and the UK adopting a more permissive approach. Notably, these divergent policies were developed from the same broad evidence base. In this paper, to investigate differences in how the two jurisdictions manage and present evidence, we developed and evaluated a Large Language Model-based sentence classifier to perform automated analyses of electronic cigarette-related policy documents drawn from official Australian and UK legislative processes (109 documents in total). Specifically, we utilized GPT-4 to automatically classify sentences based on whether they contained claims that e-cigarettes were broadly helpful or harmful for public health. Our LLM-based classifier achieved an F-score of 0.9. Further, when applying the classifier to our entire sentence-level corpus, we found that Australian legislative documents show a much higher proportion of harmful statements, and a lower proportion of helpful statements compared to the expected values, with the opposite holding for the UK. In conclusion, this work utilized an LLM-based approach to provide evidence to support the contention that - drawing on the same evidence base - Australian ENDS-related policy documents emphasize the harms associated with ENDS products and UK policy documents emphasize the benefits. Further, our approach provides a starting point for using LLM-based methods to investigate the complex relationship between evidence and health policy formation.
Digital-physical testbed for ship autonomy studies in the Marine Cybernetics Laboratory basin
Venue: arXiv cs.RO 2025
Year: 2025

The algorithms developed for Maritime Autonomous Surface Ships (MASS) are often challenging to test on actual vessels due to high operational costs and safety considerations. Simulations offer a cost-effective alternative and eliminate risks, but they may not accurately represent real-world dynamics for the given tasks. Utilizing small-scale model ships and robotic vessels in conjunction with a laboratory basin provides an accessible testing environment for the early stages of validation processes. However, designing and developing a model vessel for a single test can be costly and cumbersome, and researchers often lack access to such infrastructure. To address these challenges and enable streamlined testing, we have developed an in-house testbed that facilitates the development, testing, verification, and validation of MASS algorithms in a digital-physical laboratory. This infrastructure includes a set of small-scale model vessels, a simulation environment for each vessel, a comprehensive testbed environment, and a digital twin in Unity. With this, we aim to establish a full design and verification pipeline that starts with high-fidelity simulation models of each model vessel, to the model-scale testing in the laboratory basin, allowing possibilities for moving towards semi-fullscale validation with R/V milliAmpere1 and full-scale validation with R/V Gunnerus. In this work, we present our progress on the development of this testbed environment and its components, demonstrating its effectiveness in enabling ship guidance, navigation, and control (GNC), including autonomy.