OpenRaiser/Intern-Atlas huggingface dataset:
Towards Bridging Formal Methods and Human Interpretability
Venue: arXiv cs.SE 2025
Year: 2025

Labeled Transition Systems (LTS) are integral to model checking and design repair tools. System engineers frequently examine LTS designs during model checking or design repair to debug, identify inconsistencies, and validate system behavior. Despite LTS's significance, no prior research has examined human comprehension of these designs. To address this, we draw on traditional software engineering and graph theory, identifying 7 key metrics: cyclomatic complexity, state space size, average branching factor, maximum depth, Albin complexity, modularity, and redundancy. We created a dataset of 148 LTS designs, sampling 48 for 324 paired comparisons, and ranked them using the Bradley-Terry model. Through Kendall's Tau correlation analysis, we found that Albin complexity ($τ= 0.444$), state space size ($τ= 0.420$), cyclomatic complexity ($τ= 0.366$), and redundancy ($τ= 0.315$) most accurately reflect human comprehension of LTS designs. To showcase the metrics' utility, we applied the Albin complexity metric within the Fortis design repair tool, ranking system redesigns. This ranking reduced annotators' comprehension time by 39\%, suggesting that metrics emphasizing human factors can enhance formal design interpretability.
Where Journalism Silenced Voices: Exploring Discrimination in the Representation of Indigenous Communities in Bangladesh
Venue: arXiv cs.CY 2025
Year: 2025

In this paper, we examine the intersections of indigeneity and media representation in shaping perceptions of indigenous communities in Bangladesh. Using a mixed-methods approach, we combine quantitative analysis of media data with qualitative insights from focus group discussions (FGD). First, we identify a total of 4,893 indigenous-related articles from our initial dataset of 2.2 million newspaper articles, using a combination of keyword-based filtering and LLM, achieving 77% accuracy and an F1-score of 81.9\%. From manually inspecting 3 prominent Bangla newspapers, we identify 15 genres that we use as our topics for semi-supervised topic modeling using CorEx. Results show indigenous news articles have higher representation of culture and entertainment (19%, 10% higher than general news articles), and a disproportionate focus on conflict and protest (9%, 7% higher than general news). On the other hand, sentiment analysis reveals that 57% of articles on indigenous topics carry a negative tone, compared to 27% for non-indigenous related news. Drawing from communication studies, we further analyze framing, priming, and agenda-setting (frequency of themes) to support the case for discrimination in representation of indigenous news coverage. For the qualitative part of our analysis, we facilitated FGD, where participants further validated these findings. Participants unanimously expressed their feeling of being under-represented, and that critical issues affecting their communities (such as education, healthcare, and land rights) are systematically marginalized in news media coverage. By highlighting 8 cases of discrimination and media misrepresentation that were frequently mentioned by participants in the FGD, this study emphasizes the urgent need for more equitable media practices that accurately reflect the experiences and struggles of marginalized communities.
Generalizing Supervised Contrastive learning: A Projection Perspective
Venue: arXiv cs.LG 2025
Year: 2025

Self-supervised contrastive learning (SSCL) has emerged as a powerful paradigm for representation learning and has been studied from multiple perspectives, including mutual information and geometric viewpoints. However, supervised contrastive (SupCon) approaches have received comparatively little attention in this context: for instance, while InfoNCE used in SSCL is known to form a lower bound on mutual information (MI), the relationship between SupCon and MI remains unexplored. To address this gap, we introduce ProjNCE, a generalization of the InfoNCE loss that unifies supervised and self-supervised contrastive objectives by incorporating projection functions and an adjustment term for negative pairs. We prove that ProjNCE constitutes a valid MI bound and affords greater flexibility in selecting projection strategies for class embeddings. Building on this flexibility, we further explore the centroid-based class embeddings in SupCon by exploring a variety of projection methods. Extensive experiments on image and audio datasets demonstrate that ProjNCE consistently outperforms both SupCon and standard cross-entropy training. Our work thus refines SupCon along two complementary perspectives--information-theoretic and projection viewpoints--and offers broadly applicable improvements whenever SupCon serves as the foundational contrastive objective.
CoRT: Code-integrated Reasoning within Thinking
Venue: arXiv cs.CL 2025
Year: 2025

Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable progress in natural language reasoning with long chain-of-thought (CoT), yet they remain inefficient or inaccurate when handling complex mathematical operations. Addressing these limitations through computational tools (e.g., computation libraries and symbolic solvers) is promising, but it introduces a technical challenge: Code Interpreter (CI) brings external knowledge beyond the model's internal text representations, thus the direct combination is not efficient. This paper introduces CoRT, a post-training framework for teaching LRMs to leverage CI effectively and efficiently. As a first step, we address the data scarcity issue by synthesizing code-integrated reasoning data through Hint-Engineering, which strategically inserts different hints at appropriate positions to optimize LRM-CI interaction. We manually create 30 high-quality samples, upon which we post-train models ranging from 1.5B to 32B parameters, with supervised fine-tuning, rejection fine-tuning and reinforcement learning. Our experimental results demonstrate that Hint-Engineering models achieve 4\% and 8\% absolute improvements on DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Qwen-1.5B respectively, across five challenging mathematical reasoning datasets. Furthermore, Hint-Engineering models use about 30\% fewer tokens for the 32B model and 50\% fewer tokens for the 1.5B model compared with the natural language models. The models and code are available at https://github.com/ChengpengLi1003/CoRT.
Frosty for partial synchrony
Venue: arXiv cs.DC 2025
Year: 2025

Snowman is the consensus protocol used by blockchains on Avalanche. Recent work has shown both how to augment Snowman with a `liveness' module called `Frosty' that protects against liveness attacks, and also how to modify Snowman so as to be consistent in partial synchrony. Since Frosty assumes (a strong form of) synchrony, the aim of this note is to show how to modify Frosty to deal with the partially synchronous version of Snowman.
A look at adversarial attacks on radio waveforms from discrete latent space
Venue: arXiv cs.LG 2025
Year: 2025

Having designed a VQVAE that maps digital radio waveforms into discrete latent space, and yields a perfectly classifiable reconstruction of the original data, we here analyze the attack suppressing properties of VQVAE when an adversarial attack is performed on high-SNR radio-frequency (RF) data-points. To target amplitude modulations from a subset of digitally modulated waveform classes, we first create adversarial attacks that preserve the phase between the in-phase and quadrature component whose values are adversarially changed. We compare them with adversarial attacks of the same intensity where phase is not preserved. We test the classification accuracy of such adversarial examples on a classifier trained to deliver 100% accuracy on the original data. To assess the ability of VQVAE to suppress the strength of the attack, we evaluate the classifier accuracy on the reconstructions by VQVAE of the adversarial datapoints and show that VQVAE substantially decreases the effectiveness of the attack. We also compare the I/Q plane diagram of the attacked data, their reconstructions and the original data. Finally, using multiple methods and metrics, we compare the probability distribution of the VQVAE latent space with and without attack. Varying the attack strength, we observe interesting properties of the discrete space, which may help detect the attacks.
From Theory to Practice: Advancing Multi-Robot Path Planning Algorithms and Applications
Venue: arXiv cs.RO 2025
Year: 2025

The labeled MRPP (Multi-Robot Path Planning) problem involves routing robots from start to goal configurations efficiently while avoiding collisions. Despite progress in solution quality and runtime, its complexity and industrial relevance continue to drive research. This dissertation introduces scalable MRPP methods with provable guarantees and practical heuristics. First, we study dense MRPP on 2D grids, relevant to warehouse and parcel systems. We propose the Rubik Table method, achieving $(1 + δ)$-optimal makespan (with $δ\in (0, 0.5]$) for up to $\frac{m_1 m_2}{2}$ robots, solving large instances efficiently and setting a new theoretical benchmark. Next, we address real-world MRPP. We design optimal layouts for structured environments (e.g., warehouses, parking systems) and propose a puzzle-based system for dense, deadlock-free autonomous vehicle parking. We also extend MRPP to Reeds-Shepp robots, introducing motion primitives and smoothing techniques to ensure feasible, efficient paths under nonholonomic constraints. Simulations and real-world tests validate the approach in urban driving and robotic transport scenarios.
MetricHMSR:Metric Human Mesh and Scene Recovery from Monocular Images
Venue: arXiv cs.CV 2025
Year: 2025

We introduce MetricHMSR (Metric Human Mesh and Scene Recovery), a novel approach for metric human mesh and scene recovery from monocular images. Due to unrealistic assumptions in the camera model and inherent challenges in metric perception, existing approaches struggle to achieve human pose and metric 3D position estimation through a unified module. To address this limitation, MetricHMSR incorporates camera rays to comprehensively encode both the bounding box information and the intrinsic parameters of perspective projection. Then we proposed Human Mixture-of-Experts (MoE), the model dynamically routes image features and ray features to task-specific experts for specialized understanding of different data aspects, enabling a unified framework that simultaneously perceives the local pose and the global 3D position. Based on the results above, we further refine the existing monocular metric depth estimation method to achieve more accurate results, ultimately enabling the seamless overlay of humans and scenes in 3D space. Comprehensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on both human mesh and scene recovery.
Course Project Report: Comparing MCMC and Variational Inference for Bayesian Probabilistic Matrix Factorization on the MovieLens Dataset
Venue: arXiv cs.LG 2025
Year: 2025

This is a course project report with complete methodology, experiments, references and mathematical derivations. Matrix factorization [1] is a widely used technique in recommendation systems. Probabilistic Matrix Factorization (PMF) [2] extends traditional matrix factorization by incorporating probability distributions over latent factors, allowing for uncertainty quantification. However, computing the posterior distribution is intractable due to the high-dimensional integral. To address this, we employ two Bayesian inference methods: Markov Chain Monte Carlo (MCMC) [3, 4] and Variational Inference (VI) [5, 6] to approximate the posterior. We evaluate their performance on MovieLens dataset [7] and compare their convergence speed, predictive accuracy, and computational efficiency. Experimental results demonstrate that VI offers faster convergence, while MCMC provides more accurate posterior estimates.
Fluoroscopic Shape and Pose Tracking of Catheters with Custom Radiopaque Markers
Venue: arXiv cs.RO 2025
Year: 2025

Safe navigation of steerable and robotic catheters in the cerebral vasculature requires awareness of the catheters shape and pose. Currently, a significant perception burden is placed on interventionalists to mentally reconstruct and predict catheter motions from biplane fluoroscopy images. Efforts to track these catheters are limited to planar segmentation or bulky sensing instrumentation, which are incompatible with microcatheters used in neurointervention. In this work, a catheter is equipped with custom radiopaque markers arranged to enable simultaneous shape and pose estimation under biplane fluoroscopy. A design measure is proposed to guide the arrangement of these markers to minimize sensitivity to marker tracking uncertainty. This approach was deployed for microcatheters smaller than 2mm OD navigating phantom vasculature with shape tracking errors less than 1mm and catheter roll errors below 40 degrees. This work can enable steerable catheters to autonomously navigate under biplane imaging.
Microservices and Real-Time Processing in Retail IT: A Review of Open-Source Toolchains and Deployment Strategies
Venue: arXiv cs.SE 2025
Year: 2025

With the rapid pace of digital transformation, the retail industry is increasingly depending on real-time, scalable, and resilient systems to manage financial transactions, analyze customer behavior, and streamline order processing. This literature review explores how modern event-driven and microservices-based architectures, particularly those leveraging Apache Kafka, Spring Boot, MongoDB, and Kubernetes are transforming retail and financial systems. By systematically reviewing academic publications, technical white papers, and industry reports from recent years, this study synthesizes key themes and implementation strategies. The analysis reveals that technologies like Kafka and Spring Boot are instrumental in building low-latency, event-driven applications that support real-time analytics and fraud detection, while MongoDB, when deployed on Kubernetes, ensures fault tolerance and high availability in inventory and transaction systems. Kubernetes itself plays a crucial role in automating deployment and scaling of microservices. These findings provide valuable insights for industry practitioners aiming to design scalable infrastructures, identify research opportunities in hybrid deployment models, and offer educators a foundation to integrate modern system architectures into professional and technical communication training.
Vectorized Region Based Brush Strokes for Artistic Rendering
Venue: arXiv cs.CV 2025
Year: 2025

Creating a stroke-by-stroke evolution process of a visual artwork tries to bridge the emotional and educational gap between the finished static artwork and its creation process. Recent stroke-based painting systems focus on capturing stroke details by predicting and iteratively refining stroke parameters to maximize the similarity between the input image and the rendered output. However, these methods often struggle to produce stroke compositions that align with artistic principles and intent. To address this, we explore an image-to-painting method that (i) facilitates semantic guidance for brush strokes in targeted regions, (ii) computes the brush stroke parameters, and (iii) establishes a sequence among segments and strokes to sequentially render the final painting. Experimental results on various input image types, such as face images, paintings, and photographic images, show that our method aligns with a region-based painting strategy while rendering a painting with high fidelity and superior stroke quality.
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Venue: arXiv cs.AI 2025
Year: 2025

A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.
A Survey of Data Compression Algorithms and their Applications
Venue: arXiv cs.IT 2025
Year: 2025

Today, with the growing demands of information storage and data transfer, data compression is becoming increasingly important. Data Compression is a technique which is used to decrease the size of data. This is very useful when some huge files have to be transferred over networks or being stored on a data storage device and the size is more than the capacity of the data storage or would consume so much bandwidth for transmission in a network. With the advent of the Internet and mobile devices with limited resources, data compression has gained even more importance. It can be effectively used to save both storage and bandwidth, thus to decrease download duration. Data compression can be achieved by a host of techniques. During this survey, I'm going to thoroughly discuss some of important data compression algorithms, their performance evaluation, and their major applications along with today's issues and recent research approaches.
Multimodal Cinematic Video Synthesis Using Text-to-Image and Audio Generation Models
Venue: arXiv cs.CV 2025
Year: 2025

Advances in generative artificial intelligence have altered multimedia creation, allowing for automatic cinematic video synthesis from text inputs. This work describes a method for creating 60-second cinematic movies incorporating Stable Diffusion for high-fidelity image synthesis, GPT-2 for narrative structuring, and a hybrid audio pipeline using gTTS and YouTube-sourced music. It uses a five-scene framework, which is augmented by linear frame interpolation, cinematic post-processing (e.g., sharpening), and audio-video synchronization to provide professional-quality results. It was created in a GPU-accelerated Google Colab environment using Python 3.11. It has a dual-mode Gradio interface (Simple and Advanced), which supports resolutions of up to 1024x768 and frame rates of 15-30 FPS. Optimizations such as CUDA memory management and error handling ensure reliability. The experiments demonstrate outstanding visual quality, narrative coherence, and efficiency, furthering text-to-video synthesis for creative, educational, and industrial applications.
Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space
Venue: arXiv cs.MM 2025
Year: 2025

Audio-driven emotional 3D facial animation encounters two significant challenges: (1) reliance on single-modal control signals (videos, text, or emotion labels) without leveraging their complementary strengths for comprehensive emotion manipulation, and (2) deterministic regression-based mapping that constrains the stochastic nature of emotional expressions and non-verbal behaviors, limiting the expressiveness of synthesized animations. To address these challenges, we present a diffusion-based framework for controllable expressive 3D facial animation. Our approach introduces two key innovations: (1) a FLAME-centered multimodal emotion binding strategy that aligns diverse modalities (text, audio, and emotion labels) through contrastive learning, enabling flexible emotion control from multiple signal sources, and (2) an attention-based latent diffusion model with content-aware attention and emotion-guided layers, which enriches motion diversity while maintaining temporal coherence and natural facial dynamics. Extensive experiments demonstrate that our method outperforms existing approaches across most metrics, achieving a 21.6\% improvement in emotion similarity while preserving physiologically plausible facial dynamics. Project Page: https://kangweiiliu.github.io/Control_3D_Animation.
Multimodal Emotion Coupling via Speech-to-Facial and Bodily Gestures in Dyadic Interaction
Venue: arXiv cs.MM 2025
Year: 2025

Human emotional expression emerges through coordinated vocal, facial, and gestural signals. While speech face alignment is well established, the broader dynamics linking emotionally expressive speech to regional facial and hand motion remains critical for gaining a deeper insight into how emotional and behavior cues are communicated in real interactions. Further modulating the coordination is the structure of conversational exchange like sequential turn taking, which creates stable temporal windows for multimodal synchrony, and simultaneous speech, often indicative of high arousal moments, disrupts this alignment and impacts emotional clarity. Understanding these dynamics enhances realtime emotion detection by improving the accuracy of timing and synchrony across modalities in both human interactions and AI systems. This study examines multimodal emotion coupling using region specific motion capture from dyadic interactions in the IEMOCAP corpus. Speech features included low level prosody, MFCCs, and model derived arousal, valence, and categorical emotions (Happy, Sad, Angry, Neutral), aligned with 3D facial and hand marker displacements. Expressive activeness was quantified through framewise displacement magnitudes, and speech to gesture prediction mapped speech features to facial and hand movements. Nonoverlapping speech consistently elicited greater activeness particularly in the lower face and mouth. Sadness showed increased expressivity during nonoverlap, while anger suppressed gestures during overlaps. Predictive mapping revealed highest accuracy for prosody and MFCCs in articulatory regions while arousal and valence had lower and more context sensitive correlations. Notably, hand speech synchrony was enhanced under low arousal and overlapping speech, but not for valence.
From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
Venue: arXiv cs.PL 2025
Year: 2025

We propose a novel architecture for integrating large language models (LLMs) with a persistent, interactive Lisp environment. This setup enables LLMs to define, invoke, and evolve their own tools through programmatic interaction with a live REPL. By embedding Lisp expressions within generation and intercepting them via a middleware layer, the system allows for stateful external memory, reflective programming, and dynamic tool creation. We present a design framework and architectural principles to guide future implementations of interactive AI systems that integrate symbolic programming with neural language generation.