OpenRaiser/Intern-Atlas huggingface dataset:
Enabling Privacy-preserving Model Evaluation in Federated Learning via Fully Homomorphic Encryption
Venue: arXiv cs.CR 2025
Year: 2025

Federated learning has become increasingly widespread due to its ability to train models collaboratively without centralizing sensitive data. While most research on FL emphasizes privacy-preserving techniques during training, the evaluation phase also presents significant privacy risks that have not been adequately addressed in the literature. In particular, the state-of-the-art solution for computing the area under the curve (AUC) in FL systems employs differential privacy, which not only fails to protect against a malicious aggregator but also suffers from severe performance degradation on smaller datasets. To overcome these limitations, we propose a novel evaluation method that leverages fully homomorphic encryption. To the best of our knowledge, this is the first work to apply FHE to privacy-preserving model evaluation in federated learning while providing verifiable security guarantees. In our approach, clients encrypt their true-positive and false-positive counts based on predefined thresholds and submit them to an aggregator, which then performs homomorphic operations to compute the global AUC without ever seeing intermediate or final results in plaintext. We offer two variants of our protocol: one secure against a semi-honest aggregator and one that additionally detects and prevents manipulations by a malicious aggregator. Besides providing verifiable security guarantees, our solution achieves superior accuracy across datasets of any size and distribution, eliminating the performance issues faced by the existing state-of-the-art method on small datasets and its runtime is negligibly small and independent of the test-set size. Experimental results confirm that our method can compute the AUC among 100 parties in under two seconds with near-perfect (99.93%) accuracy while preserving complete data privacy.
Fully Evaluated Left-Sequential Logics
Venue: arXiv cs.LO 2025
Year: 2025

We consider a family of two-valued "fully evaluated left-sequential logics" (FELs), of which Free FEL (defined by Staudt in 2012) is most distinguishing (weakest) and immune to atomic side effects. Next is Memorising FEL, in which evaluations of subexpressions are memorised. The following stronger logic is Conditional FEL (inspired by Guzmán and Squier's Conditional logic, 1990). The strongest FEL is static FEL, a sequential version of propositional logic. We use evaluation trees as a simple, intuitive semantics and provide complete axiomatisations for closed terms (left-sequential propositional expressions). For each FEL except Static FEL, we also define its three-valued version, with a constant U for "undefinedness" and again provide complete, independent axiomatisations, each one containing two additional axioms for U on top of the axiomatisations of the two-valued case. In this setting, the strongest FEL is equivalent to Bochvar's strict logic.
Multi-agent Task-Driven Exploration via Intelligent Map Compression and Sharing
Venue: arXiv cs.RO 2025
Year: 2025

This paper investigates the task-driven exploration of unknown environments with mobile sensors communicating compressed measurements. The sensors explore the area and transmit their compressed data to another robot, assisting it to reach its goal location. We propose a novel communication framework and a tractable multi-agent exploration algorithm to select the sensors' actions. The algorithm uses a task-driven measure of uncertainty, resulting from map compression, as a reward function. We validate the efficacy of our algorithm through numerical simulations conducted on a realistic map and compare it with alternative approaches. The results indicate that the proposed algorithm effectively decreases the time required for the robot to reach its target without causing excessive load on the communication network.
Survey on Modeling of Human-made Articulated Objects
Venue: arXiv cs.CV 2025
Year: 2025

3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics. Its objective is to understand the shape and motion of the articulated components, represent the geometry and mobility of object parts, and create realistic models that reflect articulated objects in the real world. This survey provides a comprehensive overview of the current state-of-the-art in 3D modeling of articulated objects, with a specific focus on the task of articulated part perception and articulated object creation (reconstruction and generation). We systematically review and discuss the relevant literature from two perspectives: geometry modeling (i.e., structure and shape of articulated parts) and articulation modeling (i.e., dynamics and motion of parts). Through this survey, we highlight the substantial progress made in these areas, outline the ongoing challenges, and identify gaps for future research. Our survey aims to serve as a foundational reference for researchers and practitioners in computer vision and graphics, offering insights into the complexities of articulated object modeling.
Addressing Label Leakage in Knowledge Tracing Models
Venue: arXiv cs.CY 2025
Year: 2025

Knowledge Tracing (KT) is concerned with predicting students' future performance on learning items in intelligent tutoring systems. Learning items are tagged with skill labels called knowledge concepts (KCs). Many KT models expand the sequence of item-student interactions into KC-student interactions by replacing learning items with their constituting KCs. This approach addresses the issue of sparse item-student interactions and minimises the number of model parameters. However, we identified a label leakage problem with this approach. The model's ability to learn correlations between KCs belonging to the same item can result in the leakage of ground truth labels, which leads to decreased performance, particularly on datasets with a high number of KCs per item. In this paper, we present methods to prevent label leakage in knowledge tracing (KT) models. Our model variants that utilize these methods consistently outperform their original counterparts. This further underscores the impact of label leakage on model performance. Additionally, these methods enhance the overall performance of KT models, with one model variant surpassing all tested baselines on different benchmarks. Notably, our methods are versatile and can be applied to a wide range of KT models.
Single-token vs Two-token Blockchain Tokenomics
Venue: arXiv cs.GT 2025
Year: 2025

We study long-term equilibria that arise in the token monetary policy, or tokenomics, design of proof-of-stake (PoS) blockchain systems that engage utility maximizing users and validators. Validators are system maintainers who get rewarded with tokens for performing the work necessary for the system to function properly, while users compete and pay with such tokens for getting a desired portion of the system service. We study how the system service provision and suitable rewards schemes together can lead to equilibria with the following desirable characteristics (1) viability: the system keeps parties engaged, (2) decentralization and skin-in-the-game: multiple sufficiently invested validators are participating, (3) stability: the price path of the underlying token used to transact with the system does not change widely over time, and (4) feasibility: the mechanism is easy to implement as a smart contract, e.g., it does not require a fiat reserve on-chain to perform token {\em buybacks} or to perform bookkeeping of exponentially growing token holdings. Our analysis enables us to put forward a novel generic mechanism for blockchain monetary policy that we call quantitative rewarding (QR). We investigate how to implement QR in single-token and two-token proof of stake (PoS) blockchain systems. The latter are systems that utilize one token for the users to pay the transaction fees and a different token for the validators to participate in the PoS protocol and get rewarded. Our approach demonstrates a concrete advantage of the two-token setting in terms of the ability of the QR mechanism to be realized effectively and provide good equilibria. Our analysis also reveals an inherent limitation of the single token setting in terms of implementing an effective blockchain monetary policy - a distinction that is, to the best of our knowledge, highlighted for the first time.
EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents Ego-Centric Intersection-over-Union (EC-IoU), addressing the limitation of the standard IoU measure in characterizing safety-related performance for object detectors in navigating contexts. Concretely, we propose a weighting mechanism to refine IoU, allowing it to assign a higher score to a prediction that covers closer points of a ground-truth object from the ego agent's perspective. The proposed EC-IoU measure can be used in typical evaluation processes to select object detectors with better safety-related performance for downstream tasks. It can also be integrated into common loss functions for model fine-tuning. While geared towards safety, our experiment with the KITTI dataset demonstrates the performance of a model trained on EC-IoU can be better than that of a variant trained on IoU in terms of mean Average Precision as well.
Initialisation and Network Effects in Decentralised Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Fully decentralised federated learning enables collaborative training of individual machine learning models on a distributed network of communicating devices while keeping the training data localised on each node. This approach avoids central coordination, enhances data privacy and eliminates the risk of a single point of failure. Our research highlights that the effectiveness of decentralised federated learning is significantly influenced by the network topology of connected devices and the learning models' initial conditions. We propose a strategy for uncoordinated initialisation of the artificial neural networks based on the distribution of eigenvector centralities of the underlying communication network, leading to a radically improved training efficiency. Additionally, our study explores the scaling behaviour and the choice of environmental parameters under our proposed initialisation strategy. This work paves the way for more efficient and scalable artificial neural network training in a distributed and uncoordinated environment, offering a deeper understanding of the intertwining roles of network structure and learning dynamics.
Edit3K: Universal Representation Learning for Video Editing Components
Venue: arXiv cs.CV 2025
Year: 2025

This paper focuses on understanding the predominant video creation pipeline, i.e., compositional video editing with six main types of editing components, including video effects, animation, transition, filter, sticker, and text. In contrast to existing visual representation learning of visual materials (i.e., images/videos), we aim to learn visual representations of editing actions/components that are generally applied on raw materials. We start by proposing the first large-scale dataset for editing components of video creation, which covers about $3,094$ editing components with $618,800$ videos. Each video in our dataset is rendered by various image/video materials with a single editing component, which supports atomic visual understanding of different editing components. It can also benefit several downstream tasks, e.g., editing component recommendation, editing component recognition/retrieval, etc. Existing visual representation methods perform poorly because it is difficult to disentangle the visual appearance of editing components from raw materials. To that end, we benchmark popular alternative solutions and propose a novel method that learns to attend to the appearance of editing components regardless of raw materials. Our method achieves favorable results on editing component retrieval/recognition compared to the alternative solutions. A user study is also conducted to show that our representations cluster visually similar editing components better than other alternatives. Furthermore, our learned representations used to transition recommendation tasks achieve state-of-the-art results on the AutoTransition dataset. The code and dataset are available at https://github.com/GX77/Edit3K .
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History
Venue: arXiv cs.LG 2025
Year: 2025

Traditional imitation learning focuses on modeling the behavioral mechanisms of experts, which requires a large amount of interaction history generated by some fixed expert. However, in many streaming applications, such as streaming recommender systems, online decision-makers typically engage in online learning during the decision-making process, meaning that the interaction history generated by online decision-makers includes their behavioral evolution from novice expert to experienced expert. This poses a new challenge for existing imitation learning approaches that can only utilize data from experienced experts. To address this issue, this paper proposes an inverse batched contextual bandit (IBCB) framework that can efficiently perform estimations of environment reward parameters and learned policy based on the expert's behavioral evolution history. Specifically, IBCB formulates the inverse problem into a simple quadratic programming problem by utilizing the behavioral evolution history of the batched contextual bandit with inaccessible rewards. We demonstrate that IBCB is a unified framework for both deterministic and randomized bandit policies. The experimental results indicate that IBCB outperforms several existing imitation learning algorithms on synthetic and real-world data and significantly reduces running time. Additionally, empirical analyses reveal that IBCB exhibits better out-of-distribution generalization and is highly effective in learning the bandit policy from the interaction history of novice experts.
Coupled Requirements-driven Testing of CPS: From Simulation To Reality
Venue: arXiv cs.SE 2025
Year: 2025

Failures in safety-critical Cyber-Physical Systems (CPS), both software and hardware-related, can lead to severe incidents impacting physical infrastructure or even harming humans. As a result, extensive simulations and field tests need to be conducted, as part of the verification and validation of system requirements, to ensure system safety. However, current simulation and field testing practices, particularly in the domain of small Unmanned Aerial Systems (sUAS), are ad-hoc and lack a thorough, structured testing process. Furthermore, there is a dearth of standard processes and methodologies to inform the design of comprehensive simulation and field tests. This gap in the testing process leads to the deployment of sUAS applications that are: (a) tested in simulation environments which do not adequately capture the real-world complexity, such as environmental factors, due to a lack of tool support; (b) not subjected to a comprehensive range of scenarios during simulation testing to validate the system requirements, due to the absence of a process defining the relationship between requirements and simulation tests; and (c) not analyzed through standard safety analysis processes, because of missing traceability between simulation testing artifacts and safety analysis artifacts. To address these issues, we have developed an initial framework for validating CPS, specifically focusing on sUAS and robotic applications. We demonstrate the suitability of our framework by applying it to an example from the sUAS domain. Our preliminary results confirm the applicability of our framework. We conclude with a research roadmap to outline our next research goals along with our current proposal.
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks
Venue: arXiv cs.NE 2025
Year: 2025

Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional roles of their modelling components remain unclear. By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive experiments, we demonstrate how these components influence the accuracy, generalization, and robustness of SNNs. Specifically, we find that the leakage plays a crucial role in balancing memory retention and robustness, the reset mechanism is essential for uninterrupted temporal processing and computational efficiency, and the recurrence enriches the capability to model complex dynamics at a cost of robustness degradation. With these interesting observations, we provide optimization suggestions for enhancing the performance of SNNs in different scenarios. This work deepens the understanding of how SNNs work, which offers valuable guidance for the development of more effective and robust neuromorphic models.
Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making
Venue: arXiv cs.HC 2025
Year: 2025

In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. In such a paradigm, humans are found to rarely trigger analytical thinking and face difficulties in communicating the nuances of conflicting opinions to the AI when disagreements occur. To tackle this challenge, we propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making. Based on theories in human deliberation, this framework engages humans and AI in dimension-level opinion elicitation, deliberative discussion, and decision updates. To empower AI with deliberative capabilities, we designed Deliberative AI, which leverages large language models (LLMs) as a bridge between humans and domain-specific models to enable flexible conversational interactions and faithful information provision. An exploratory evaluation on a graduate admissions task shows that Deliberative AI outperforms conventional explainable AI (XAI) assistants in improving humans' appropriate reliance and task performance. Based on a mixed-methods analysis of participant behavior, perception, user experience, and open-ended feedback, we draw implications for future AI-assisted decision tool design.
Discovering Graph Generating Dependencies for Property Graph Profiling
Venue: arXiv cs.DB 2025
Year: 2025

With the increasing use of graph-structured data, there is also increasing interest in investigating graph data dependencies and their applications, e.g., in graph data profiling. Graph Generating Dependencies (GGDs) are a class of dependencies for property graphs that can express the relation between different graph patterns and constraints based on their attribute similarities. Rich syntax and semantics of GGDs make them a good candidate for graph data profiling. Nonetheless, GGDs are difficult to define manually, especially when there are no data experts available. In this paper, we propose GGDMiner, a framework for discovering approximate GGDs from graph data automatically, with the intention of profiling graph data through GGDs for the user. GGDMiner has three main steps: (1) pre-processing, (2) candidate generation, and, (3) GGD extraction. To optimize memory consumption and execution time, GGDMiner uses a factorized representation of each discovered graph pattern, called Answer Graph. Our results show that the discovered set of GGDs can give an overview about the input graph, both schema level information and also correlations between the graph patterns and attributes.
Free Sets in Planar Graphs: History and Applications
Venue: arXiv cs.CG 2025
Year: 2025

A subset $S$ of vertices in a planar graph $G$ is a free set if, for every set $P$ of $|S|$ points in the plane, there exists a straight-line crossing-free drawing of $G$ in which vertices of $S$ are mapped to distinct points in $P$. In this survey, we review - several equivalent definitions of free sets, - results on the existence of large free sets in planar graphs and subclasses of planar graphs, - and applications of free sets in graph drawing. The survey concludes with a list of open problems in this still very active research area.
Uncertainty Quantification for Gradient-based Explanations in Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Explanation methods help understand the reasons for a model's prediction. These methods are increasingly involved in model debugging, performance optimization, and gaining insights into the workings of a model. With such critical applications of these methods, it is imperative to measure the uncertainty associated with the explanations generated by these methods. In this paper, we propose a pipeline to ascertain the explanation uncertainty of neural networks by combining uncertainty estimation methods and explanation methods. We use this pipeline to produce explanation distributions for the CIFAR-10, FER+, and California Housing datasets. By computing the coefficient of variation of these distributions, we evaluate the confidence in the explanation and determine that the explanations generated using Guided Backpropagation have low uncertainty associated with them. Additionally, we compute modified pixel insertion/deletion metrics to evaluate the quality of the generated explanations.
TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are possible. However, reasoning problems defined in PDDL do not capture temporal aspects of action taking, such as concurrent actions between two agents when there are no conflicting conditions, without significant modification and definition to existing PDDL domains. A human expert aware of such constraints can decompose a goal into subgoals, each reachable through single agent planning, to take advantage of simultaneous actions. In contrast to classical planning, large language models (LLMs) directly used for inferring plan steps rarely guarantee execution success, but are capable of leveraging commonsense reasoning to assemble action sequences. We combine the strengths of both classical planning and LLMs by approximating human intuitions for multi-agent planning goal decomposition. We demonstrate that LLM-based goal decomposition leads to faster planning times than solving multi-agent PDDL problems directly while simultaneously achieving fewer plan execution steps than a single agent plan alone, as well as most multiagent plans, while guaranteeing execution success. Additionally, we find that LLM-based approximations of subgoals result in similar multi-agent execution lengths to those specified by human experts. Website and resources at https://glamor-usc.github.io/twostep