OpenRaiser/Intern-Atlas huggingface dataset:
VitalVideos-Europe: A dataset of face videos with PPG and blood pressure ground truths
Venue: arXiv cs.CV 2025
Year: 2025

We collected a large dataset consisting of 850 unique participants. For every participant we recorded two 30 second uncompressed videos, synchronized PPG waveforms and a single blood pressure measurement. Gender, age and skin color were also registered for every participant. The dataset includes roughly equal numbers of males and females, as well as participants of all ages. While the skin color distribution could have been more balanced, the dataset contains individuals from every skin color. The data was collected in a diverse set of locations to ensure a wide variety of backgrounds and lighting conditions. In an effort to assist in the research and development of remote vital sign measurement we are now opening up access to this dataset. vitalvideos.org for all datasets.
Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World
Venue: arXiv cs.AI 2025
Year: 2025

Autonomous agents operating within real-world environments often rely on automated planners to ascertain optimal actions towards desired goals or the optimization of a specified objective function. Integral to these agents are common architectural components such as schedulers, tasked with determining the timing for executing planned actions, and execution engines, responsible for carrying out these scheduled actions while monitoring their outcomes. We address the significant challenge that arises when unexpected phenomena, termed \textit{novelties}, emerge within the environment, altering its fundamental characteristics, composition, and dynamics. This challenge is inherent in all deployed real-world applications and may manifest suddenly and without prior notice or explanation. The introduction of novelties into the environment can lead to inaccuracies within the planner's internal model, rendering previously generated plans obsolete. Recent research introduced agent design aimed at detecting and adapting to such novelties. However, these designs lack consideration for action scheduling in continuous time-space, coordination of concurrent actions by multiple agents, or memory-based novelty accommodation. Additionally, the application has been primarily demonstrated in lower fidelity environments. In our study, we propose a general purpose AI agent framework designed to detect, characterize, and adapt to novelties in highly noisy, complex, and stochastic environments that support concurrent actions and external scheduling. We showcase the efficacy of our agent through experimentation within a high-fidelity simulator for realistic military scenarios.
Special Delivery: Programming with Mailbox Types (Extended Version)
Venue: arXiv cs.PL 2025
Year: 2025

The asynchronous and unidirectional communication model supported by mailboxes is a key reason for the success of actor languages like Erlang and Elixir for implementing reliable and scalable distributed systems. While many actors may send messages to some actor, only the actor may receive from its mailbox. Although actors eliminate many of the issues stemming from shared memory concurrency, they remain vulnerable to communication errors such as protocol violations and deadlocks. Mailbox types are a novel behavioural type system for mailboxes first introduced for a process calculus by de'Liguoro and Padovani in 2018, which capture the contents of a mailbox as a commutative regular expression. Due to aliasing and nested evaluation contexts, moving from a process calculus to a programming language is challenging. This paper presents Pat, the first programming language design incorporating mailbox types, and describes an algorithmic type system. We make essential use of quasi-linear typing to tame some of the complexity introduced by aliasing. Our algorithmic type system is necessarily co-contextual, achieved through a novel use of backwards bidirectional typing, and we prove it sound and complete with respect to our declarative type system. We extend Pat with sums, products and higher-order functions, and also interfaces that allow finer-grained reasoning about mailbox contents. We implement a prototype type checker, and use it to demonstrate the expressiveness of Pat on a factory automation case study and a series of examples from the Savina actor benchmark suite.
Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons
Venue: arXiv cs.LG 2025
Year: 2025

Graph neural networks (GNNs) have demonstrated remarkable capabilities in learning from graph-structured data, often outperforming traditional Multilayer Perceptrons (MLPs) in numerous graph-based tasks. Although existing works have demonstrated the benefits of graph convolution through Laplacian smoothing, expressivity or separability, there remains a lack of quantitative analysis comparing GNNs and MLPs from an optimization and generalization perspective. This study aims to address this gap by examining the role of graph convolution through feature learning theory. Using a signal-noise data model, we conduct a comparative analysis of the optimization and generalization between two-layer graph convolutional networks (GCNs) and their MLP counterparts. Our approach tracks the trajectory of signal learning and noise memorization in GNNs, characterizing their post-training generalization. We reveal that GNNs significantly prioritize signal learning, thus enhancing the regime of {low test error} over MLPs by $D^{q-2}$ times, where $D$ denotes a node's expected degree and $q$ is the power of ReLU activation function with $q>2$. This finding highlights a substantial and quantitative discrepancy between GNNs and MLPs in terms of optimization and generalization, a conclusion further supported by our empirical simulations on both synthetic and real-world datasets.
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
Venue: arXiv cs.LG 2025
Year: 2025

The intersection of Foundation Model (FM) and Federated Learning (FL) presents a unique opportunity to unlock new possibilities for real-world applications. On the one hand, FL, as a collaborative learning paradigm, help address challenges in FM development by expanding data availability, enabling computation sharing, facilitating the collaborative development of FMs, tackling continuous data update, avoiding FM monopoly, response delay and FM service down. On the other hand, FM, equipped with pre-trained knowledge and exceptional performance, can serve as a robust starting point for FL. It can also generate synthetic data to enrich data diversity and enhance overall performance of FL. Meanwhile, FM unlocks new sharing paradigm and multi-task and multi-modality capabilities for FL. By examining the interplay between FL and FM, this paper presents the motivations, challenges, and future directions of empowering FL with FM and empowering FM with FL. We hope that this work provides a good foundation to inspire future research efforts to drive advancements in both fields.
RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$
Venue: arXiv cs.LG 2025
Year: 2025

Meta reinforcement learning (Meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution. However, they show poor asymptotic performance and struggle with out-of-distribution tasks because they rely on sequence models, such as recurrent neural networks or transformers, to process experiences rather than summarize them using general-purpose RL components such as value functions. In contrast, traditional RL algorithms are data-inefficient as they do not use domain knowledge, but do converge to an optimal policy in the limit. We propose RL$^3$, a principled hybrid approach that incorporates action-values, learned per task via traditional RL, in the inputs to Meta-RL. We show that RL$^3$ earns a greater cumulative reward in the long term compared to RL$^2$ while drastically reducing meta-training time and generalizes better to out-of-distribution tasks. Experiments are conducted on Meta-RL benchmarks and custom discrete domains that exhibit a range of short-term, long-term, and complex dependencies.
Discerning Reliable Cyber Threat Indicators for Timely Cyber Threat Intelligence
Venue: arXiv cs.CR 2025
Year: 2025

In today's dynamic cybersecurity landscape, timely and accurate threat intelligence is essential for proactive defense. This study explores the potential of social media platforms as a valuable resource for extracting actionable Indicators of Compromise (IoCs). Utilizing a Convolutional Neural Network (CNN), we achieved an F1-score of 98.80% and a detection rate of 99.65%, filtering vast social media data to identify key IoCs, including IP addresses, URLs, file hashes, domain addresses, and CVE IDs. These indicators are critical for detecting potential threats and vulnerabilities, and their relevance was evaluated using metrics such as correctness, timeliness, and overlap. Our analysis shows that URLs emerged as the most frequently shared IoC, with 48.67% representing valid threats. To further investigate the role of automated accounts in disseminating IoCs, we applied several machine learning models, with XGBoost delivering the highest performance achieving a macro F1-score of 0.814 and a weighted F1-score of 0.925. These findings highlight the growing significance of social media as a reliable source of actionable threat intelligence, offering valuable insights for cybersecurity professionals to stay ahead of emerging threats.
Deciding Predicate Logical Theories of Real-Valued Functions
Venue: arXiv cs.LO 2025
Year: 2025

The notion of a real-valued function is central to mathematics, computer science, and many other scientific fields. Despite this importance, there are hardly any positive results on decision procedures for predicate logical theories that reason about real-valued functions. This paper defines a first-order predicate language for reasoning about multi-dimensional smooth real-valued functions and their derivatives, and demonstrates that - despite the obvious undecidability barriers - certain positive decidability results for such a language are indeed possible.
PFB-Diff: Progressive Feature Blending Diffusion for Text-driven Image Editing
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have demonstrated their ability to generate diverse and high-quality images, sparking considerable interest in their potential for real image editing applications. However, existing diffusion-based approaches for local image editing often suffer from undesired artifacts due to the latent-level blending of the noised target images and diffusion latent variables, which lack the necessary semantics for maintaining image consistency. To address these issues, we propose PFB-Diff, a Progressive Feature Blending method for Diffusion-based image editing. Unlike previous methods, PFB-Diff seamlessly integrates text-guided generated content into the target image through multi-level feature blending. The rich semantics encoded in deep features and the progressive blending scheme from high to low levels ensure semantic coherence and high quality in edited images. Additionally, we introduce an attention masking mechanism in the cross-attention layers to confine the impact of specific words to desired regions, further improving the performance of background editing and multi-object replacement. PFB-Diff can effectively address various editing tasks, including object/background replacement and object attribute editing. Our method demonstrates its superior performance in terms of editing accuracy and image quality without the need for fine-tuning or training. Our implementation is available at https://github.com/CMACH508/PFB-Diff.
Integrating Large Language Model for Improved Causal Discovery
Venue: arXiv cs.AI 2025
Year: 2025

Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causal discovery usually relies on expert validation or prior analysis to improve the reliability of recovered causality, which is yet limited by the scarcity of expert resources. Recently, Large Language Models (LLM) have been used for causal analysis across various domain-specific scenarios, suggesting its potential as autonomous expert roles in guiding data-based structure learning. However, integrating LLMs into causal discovery faces challenges due to inaccuracies in LLM-based reasoning on revealing the actual causal structure. To address this challenge, we propose an error-tolerant LLM-driven causal discovery framework. The error-tolerant mechanism is designed three-fold with sufficient consideration on potential inaccuracies. In the LLM-based reasoning process, an accuracy-oriented prompting strategy restricts causal analysis to a reliable range. Next, a knowledge-to-structure transition aligns LLM-derived causal statements with structural causal interactions. In the structure learning process, the goodness-of-fit to data and adherence to LLM-derived priors are balanced to further address prior inaccuracies. Evaluation of eight real-world causal structures demonstrates the efficacy of our LLM-driven approach in improving data-based causal discovery, along with its robustness to inaccurate LLM-derived priors. Codes are available at https://github.com/tyMadara/LLM-CD.
Online Coalition Formation under Random Arrival or Coalition Dissolution
Venue: arXiv cs.GT 2025
Year: 2025

Coalition formation explores how to partition a set of $n$ agents into disjoint coalitions according to their preferences. We consider a cardinal utility model with an additively separable aggregation of preferences and study the online variant of coalition formation, where the agents arrive in sequence. The goal is to achieve competitive social welfare. In the basic model, agents arrive in an arbitrary order and have to be assigned to coalitions immediately and irrevocably. There, the natural greedy algorithm is known to achieve an optimal competitive ratio, which heavily relies on the range of utilities. We complement this result by considering two related models. First, we study a model where agents arrive in a random order. We find that the competitive ratio of the greedy algorithm is $Θ\left(\frac{1}{n^2}\right)$. In contrast, an alternative algorithm, which is based on alternating between waiting and greedy phases, can achieve a competitive ratio of $Θ\left(\frac{1}{n}\right)$. Second, we relax the irrevocability of decisions by allowing the dissolution of coalitions into singleton coalitions. We achieve an asymptotically optimal competitive ratio of $Θ\left(\frac 1n\right)$ by drawing a close connection to a general model of online matching. Hence, in both models, we obtain a competitive ratio that removes the unavoidable utility dependencies in the basic model and essentially matches the best possible approximation ratio by polynomial-time algorithms.
Beyond-Voice: Towards Continuous 3D Hand Pose Tracking on Commercial Home Assistant Devices
Venue: arXiv cs.SD 2025
Year: 2025

The surging popularity of home assistants and their voice user interface (VUI) have made them an ideal central control hub for smart home devices. However, current form factors heavily rely on VUI, which poses accessibility and usability issues; some latest ones are equipped with additional cameras and displays, which are costly and raise privacy concerns. These concerns jointly motivate Beyond-Voice, a novel high-fidelity acoustic sensing system that allows commodity home assistant devices to track and reconstruct hand poses continuously. It transforms the home assistant into an active sonar system using its existing onboard microphones and speakers. We feed a high-resolution range profile to the deep learning model that can analyze the motions of multiple body parts and predict the 3D positions of 21 finger joints, bringing the granularity for acoustic hand tracking to the next level. It operates across different environments and users without the need for personalized training data. A user study with 11 participants in 3 different environments shows that Beyond-Voice can track joints with an average mean absolute error of 16.47mm without any training data provided by the testing subject.
Personality Traits in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs increasingly powerconversational agents used by the general public world-wide, the synthetic personality traits embedded in these models, by virtue of training on large amounts of human data, is becoming increasingly important. Since personality is a key factor determining the effectiveness of communication, we present a novel and comprehensive psychometrically valid and reliable methodology for administering and validating personality tests on widely-used LLMs, as well as for shaping personality in the generated text of such LLMs. Applying this method to 18 LLMs, we found: 1) personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; 2) evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction fine-tuned models; and 3) personality in LLM outputs can be shaped along desired dimensions to mimic specific human personality profiles. We discuss the application and ethical implications of the measurement and shaping method, in particular regarding responsible AI.
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
Venue: arXiv cs.LG 2025
Year: 2025

Neural network quantization is a very promising solution in the field of model compression, but its resulting accuracy highly depends on a training/fine-tuning process and requires the original data. This not only brings heavy computation and time costs but also is not conducive to privacy and sensitive information protection. Therefore, a few recent works are starting to focus on data-free quantization. However, data-free quantization does not perform well while dealing with ultra-low precision quantization. Although researchers utilize generative methods of synthetic data to address this problem partially, data synthesis needs to take a lot of computation and time. In this paper, we propose a data-free mixed-precision compensation (DF-MPC) method to recover the performance of an ultra-low precision quantized model without any data and fine-tuning process. By assuming the quantized error caused by a low-precision quantized layer can be restored via the reconstruction of a high-precision quantized layer, we mathematically formulate the reconstruction loss between the pre-trained full-precision model and its layer-wise mixed-precision quantized model. Based on our formulation, we theoretically deduce the closed-form solution by minimizing the reconstruction loss of the feature maps. Since DF-MPC does not require any original/synthetic data, it is a more efficient method to approximate the full-precision model. Experimentally, our DF-MPC is able to achieve higher accuracy for an ultra-low precision quantized model compared to the recent methods without any data and fine-tuning process.
SDC-HSDD-NDSA: Structure Detecting Cluster by Hierarchical Secondary Directed Differential with Normalized Density and Self-Adaption
Venue: arXiv cs.LG 2025
Year: 2025

Density-based clustering is the most popular clustering algorithm since it can identify clusters of arbitrary shape as long as they are separated by low-density regions. However, a high-density region that is not separated by low-density ones might also have different structures belonging to multiple clusters. As far as we know, all previous density-based clustering algorithms fail to detect such structures. In this paper, we provide a novel density-based clustering scheme to address this problem. It is the rst clustering algorithm that can detect meticulous structures in a high-density region that is not separated by low-density ones and thus extends the range of applications of clustering. The algorithm employs secondary directed differential, hierarchy, normalized density, as well as the self-adaption coefficient, called Structure Detecting Cluster by Hierarchical Secondary Directed Differential with Normalized Density and Self-Adaption, dubbed SDC-HSDD-NDSA. Experiments on synthetic and real datasets are implemented to verify the effectiveness, robustness, and granularity independence of the algorithm, and the scheme is compared to unsupervised schemes in the Python package Scikit-learn. Results demonstrate that our algorithm outperforms previous ones in many situations, especially significantly when clusters have regular internal structures. For example, averaging over the eight noiseless synthetic datasets with structures employing ARI and NMI criteria, previous algorithms obtain scores below 0.6 and 0.7, while the presented algorithm obtains scores higher than 0.9 and 0.95, respectively.
Current policies governing editorial conflicts of interest are ineffective
Venue: arXiv cs.DL 2025
Year: 2025

Research-active editors face a potential conflict of interest (COI) when handling submissions from authors who share the same affiliation or those who recently collaborated with the editor. Since perception of COIs arising from such editor-author associations may erode trust in science, some policies recommend, and others demand, recusal in such incidents. However, the effectiveness of such measures is unknown to date. To fill this gap, we analyze half a million papers from six publishers who specify the handling editor of each paper. We find numerous papers with editor-author associations, and demonstrate that such papers tend to be accepted faster. A quasi-experimental design exploiting policy changes at PNAS and PLOS reveals the limited effectiveness of current COI policies. A network neural embedding model reveals that requiring editors with potential COIs to recuse may compromise the suitability of the handling editor. Finally, an online survey experiment demonstrates that such COIs influence trust in the paper's finding, but public disclosure eliminates this effect.
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding
Venue: arXiv cs.CV 2025
Year: 2025

Vision-language tasks, such as VQA, SNLI-VE, and VCR are challenging because they require the model's reasoning ability to understand the semantics of the visual world and natural language. Supervised methods working for vision-language tasks have been well-studied. However, solving these tasks in a zero-shot setting is less explored. Since Contrastive Language-Image Pre-training (CLIP) has shown remarkable zero-shot performance on image-text matching, previous works utilized its strong zero-shot ability by converting vision-language tasks into an image-text matching problem, and they mainly consider global-level matching (e.g., the whole image or sentence). However, we find visual and textual fine-grained information, e.g., keywords in the sentence and objects in the image, can be fairly informative for semantics understanding. Inspired by this, we propose a unified framework to take advantage of the fine-grained information for zero-shot vision-language learning, covering multiple tasks such as VQA, SNLI-VE, and VCR. Our experiments show that our framework outperforms former zero-shot methods on VQA and achieves substantial improvement on SNLI-VE and VCR. Furthermore, our ablation studies confirm the effectiveness and generalizability of our proposed method.