OpenRaiser/Intern-Atlas huggingface dataset:
Mechanism Design under Unawareness -- Extended Abstract
Venue: arXiv cs.GT 2025
Year: 2025

We study the design of mechanisms under asymmetric awareness and information. While the mechanism designer cannot necessarily commit to a particular social choice function in the face of unawareness, she can at least commit to properties of social choice functions such as efficiency given ex post awareness. Assuming quasi-linear utilities and private values, we show that we can implement in conditional dominant strategies a social choice function that is utilitarian ex post efficient under pooled awareness without the need of the social planner being fully aware ex ante. To this end, we develop novel dynamic versions of Vickrey-Clarke-Groves mechanisms in which true types are revealed and subsequently elaborated at endogenous higher awareness levels. We explore how asymmetric awareness affects budget balance and participation constraints. We show that ex ante unforeseen contingencies are no excuse for deficits. Finally, we propose a dynamic elaboration reverse second price auction for efficient procurement of complex incompletely specified projects with budget balance and participation constraints.
Distributed Knowing How
Venue: arXiv cs.LO 2025
Year: 2025

Distributed knowledge is a key concept in the standard epistemic logic of knowledge-that. In this paper, we propose a corresponding notion of distributed knowledge-how and study its logic. Our framework generalizes two existing traditions in the logic of know-how: the individual-based multi-step framework and the coalition-based single-step framework. In particular, we assume a group can accomplish more than what its individuals can jointly do. The distributed knowledge-how is based on the distributed knowledge-that of a group whose multi-step strategies derive from distributed actions that subgroups can collectively perform. As the main result, we obtain a sound and strongly complete proof system for our logic of distributed knowledge-how, which closely resembles the logic of distributed knowledge-that in both the axioms and the proof method of completeness.
Conditionals Based on Selection Functions, Modal Operators and Probabilities
Venue: arXiv cs.LO 2025
Year: 2025

Methods for probability updating, of which Bayesian conditionalization is the most well-known and widely used, are modeling tools that aim to represent the process of modifying an initial epistemic state, typically represented by a prior probability function P, which is adjusted in light of new information. Notably, updating methods and conditional sentences seem to intuitively share a deep connection, as is evident in the case of conditionalization. The present work contributes to this line of research and aims at shedding new light on the relationship between updating methods and conditional connectives. Departing from previous literature that often focused on a specific type of conditional or a particular updating method, our goal is to prove general results concerning the connection between conditionals and their probabilities. This will allow us to characterize the probabilities of certain conditional connectives and to understand what class of updating procedures can be represented using specific conditional connectives. Broadly, we adopt a general perspective that encompasses a large class of conditionals and a wide range of updating methods, enabling us to prove some general results concerning their interrelation.
Are Large Random Graphs Always Safe to Hide?
Venue: arXiv cs.LO 2025
Year: 2025

We discuss winning possibilities of players in various variants of cops and robber game played on large random graphs, a testbed for various kinds of network queries, search problems in particular. We explore the use of logic frameworks to investigate such results; in particular, we show that whenever a winning condition for either player can be expressed as a certain kind of formula in first-order logic, that player almost always wins. In the process, we obtain more insight into the logic-game connection from the zero-one law perspective.
Prudent Rationalizability and the Best Rationalization Principle
Venue: arXiv cs.GT 2025
Year: 2025

We study cautious reasoning in finite sequential games played by agents with perfect recall. Our contribution lies in formulating a definition of prudent rationalizability (Heifetz et al. 2021, BEJTE) as an iterative reduction procedure of beliefs. To this end, we represent the players' beliefs by systems of conditional non-standard probability measures. The key novelty is the notion of c-strong belief, a non-standard, "cautious" version of strong belief (Battigalli and Siniscalchi 2002, JET). Our formulation of prudent rationalizability embodies a "best rationalization principle" similar to the one that underlies the solution concept of strong rationalizability. The main results show the equivalence between the proposed definition with the one originally put forth by Heifetz et al. (2021) in terms of conditional beliefs represented by standard probabilities. In particular, it is shown that prudent rationalizability can be algorithmically characterized by iterated admissibility. Finally, our formulation can be extended to sequential games with unawareness.
UAV-MM3D: A Large-Scale Synthetic Benchmark for 3D Perception of Unmanned Aerial Vehicles with Multi-Modal Data
Venue: arXiv cs.CV 2025
Year: 2025

Accurate perception of UAVs in complex low-altitude environments is critical for airspace security and related intelligent systems. Developing reliable solutions requires large-scale, accurately annotated, and multimodal data. However, real-world UAV data collection faces inherent constraints due to airspace regulations, privacy concerns, and environmental variability, while manual annotation of 3D poses and cross-modal correspondences is time-consuming and costly. To overcome these challenges, we introduce UAV-MM3D, a high-fidelity multimodal synthetic dataset for low-altitude UAV perception and motion understanding. It comprises 400K synchronized frames across diverse scenes (urban areas, suburbs, forests, coastal regions) and weather conditions (clear, cloudy, rainy, foggy), featuring multiple UAV models (micro, small, medium-sized) and five modalities - RGB, IR, LiDAR, Radar, and DVS (Dynamic Vision Sensor). Each frame provides 2D/3D bounding boxes, 6-DoF poses, and instance-level annotations, enabling core tasks related to UAVs such as 3D detection, pose estimation, target tracking, and short-term trajectory forecasting. We further propose LGFusionNet, a LiDAR-guided multimodal fusion baseline, and a dedicated UAV trajectory prediction baseline to facilitate benchmarking. With its controllable simulation environment, comprehensive scenario coverage, and rich annotations, UAV3D offers a public benchmark for advancing 3D perception of UAVs.
Quantifying Geometry Effects on Low-Cost Intelligent Reflecting Surfaces
Venue: arXiv cs.ET 2025
Year: 2025

Intelligent Reflecting Surfaces (IRS) promise low-power coverage extension, yet practical deployments must curb hardware complexity and control overhead. This paper quantifies the performance impact of two cost-saving measures, column-wise element grouping and 1-bit (binary) phase quantization, relative to the ideal fully-controlled, continuous-phase baseline. A single-input single-output link is simulated at 26 GHz (mmWave) across three deployment geometries that vary the relative heights of access point, IRS and user equipment. Results show that switching from continuous to binary phase control reduces median SNR gain by approximately 4 dB, while adopting column-wise grouping introduces a similar penalty; combining both constraints incurs approximately 8 dB loss under height-offset deployments. When all nodes share the same height, the degradation from column-wise control becomes negligible, indicating deployment geometry can offset control-granularity limits. Despite the losses, a 32 x 32 column-wise binary IRS still delivers double-digit SNR gains over the no-IRS baseline in most positions, confirming its viability for cost-constrained scenarios. The study provides quantitative guidelines on when simplified IRS architectures can meet link-budget targets and where full element-wise control remains justified.
NOMAD: A Multi-Agent LLM System for UML Class Diagram Generation from Natural Language Requirements
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are increasingly utilised in software engineering, yet their ability to generate structured artefacts such as UML diagrams remains underexplored. In this work we present NOMAD, a cognitively inspired, modular multi-agent framework that decomposes UML generation into a series of role-specialised subtasks. Each agent handles a distinct modelling activity, such as entity extraction, relationship classification, and diagram synthesis, mirroring the goal-directed reasoning processes of an engineer. This decomposition improves interpretability and allows for targeted verification strategies. We evaluate NOMAD through a mixed design: a large case study (Northwind) for in-depth probing and error analysis, and human-authored UML exercises for breadth and realism. NOMAD outperforms all selected baselines, while revealing persistent challenges in fine-grained attribute extraction. Building on these observations, we introduce the first systematic taxonomy of errors in LLM-generated UML diagrams, categorising structural, relationship, and semantic/logical. Finally, we examine verification as a design probe, showing its mixed effects and outlining adaptive strategies as promising directions. Together, these contributions position NOMAD as both an effective framework for UML class diagram generation and a lens onto the broader research challenges of reliable language-to-model workflows.
On the SER Performance of ZF and MMSE Receivers in Pilot-Aided Simultaneous Communication and Localization
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, a symbol error rate (SER) analysis is provided to evaluate the impact of localization inaccuracy on the communication performance under Zero-Forcing (ZF) and Minimum Mean-Square Error (MMSE) equalizers. Specifically, we adopt a pilot-aided simultaneous communication and localization (PASCAL) system, in which multiple drones actively transmit signals towards the base station (BS). Upon receiving the signal, the BS estimates the drones' location parameters to reconstruct the channel matrix, which is then utilized for ZF and MMSE equalization. As the channel matrix is characterized by the estimated parameters associated with the target's location and the matrix inversion involved in ZF and MMSE further complicates the analysis, obtaining a closed-form SER expression becomes intractable. Thus, a tightly approximated SER expression is respectively derived for ZF and MMSE by using a hybrid approximation method incorporating Neumann approximation and Taylor approximation. Our analysis reveals several important design insights: first, the average SER of drone $k$ for both ZF and MMSE can be affected by the localization errors from all drones including drone $k$; second, the average SER of ZF is unaffected by the estimation inaccuracy of range, whereas the average SER of MMSE is influenced by it; third, ZF and MMSE is the most susceptible to the influence of angle estimation errors compared to the other localization errors; fourth, ZF is highly sensitive to localization errors and may be even worse than maximal ratio combining (MRC) under some conditions of significant estimation errors. Numerical simulation results verify our findings and also validate the accuracy of the analysis across a wide range of system parameters.
Do You See What I Say? Generalizable Deepfake Detection based on Visual Speech Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Deepfake generation has witnessed remarkable progress, contributing to highly realistic generated images, videos, and audio. While technically intriguing, such progress has raised serious concerns related to the misuse of manipulated media. To mitigate such misuse, robust and reliable deepfake detection is urgently needed. Towards this, we propose a novel network FauxNet, which is based on pre-trained Visual Speech Recognition (VSR) features. By extracting temporal VSR features from videos, we identify and segregate real videos from manipulated ones. The holy grail in this context has to do with zero-shot detection, i.e., generalizable detection, which we focus on in this work. FauxNet consistently outperforms the state-of-the-art in this setting. In addition, FauxNet is able to attribute - distinguish between generation techniques from which the videos stem. Finally, we propose new datasets, referred to as Authentica-Vox and Authentica-HDTF, comprising about 38,000 real and fake videos in total, the latter created with six recent deepfake generation techniques. We provide extensive analysis and results on the Authentica datasets and FaceForensics++, demonstrating the superiority of FauxNet. The Authentica datasets will be made publicly available.
Visual-Geometry Diffusion Policy: Robust Generalization via Complementarity-Aware Multimodal Fusion
Venue: arXiv cs.RO 2025
Year: 2025

Imitation learning has emerged as a crucial ap proach for acquiring visuomotor skills from demonstrations, where designing effective observation encoders is essential for policy generalization. However, existing methods often struggle to generalize under spatial and visual randomizations, instead tending to overfit. To address this challenge, we propose Visual Geometry Diffusion Policy (VGDP), a multimodal imitation learning framework built around a Complementarity-Aware Fusion Module where modality-wise dropout enforces balanced use of RGB and point-cloud cues, with cross-attention serving only as a lightweight interaction layer. Our experiments show that the expressiveness of the fused latent space is largely induced by the enforced complementarity from modality-wise dropout, with cross-attention serving primarily as a lightweight interaction mechanism rather than the main source of robustness. Across a benchmark of 18 simulated tasks and 4 real-world tasks, VGDP outperforms seven baseline policies with an average performance improvement of 39.1%. More importantly, VGDP demonstrates strong robustness under visual and spatial per turbations, surpassing baselines with an average improvement of 41.5% in different visual conditions and 15.2% in different spatial settings.
Angle-Optimized Partial Disentanglement for Multimodal Emotion Recognition in Conversation
Venue: arXiv cs.MM 2025
Year: 2025

Multimodal Emotion Recognition in Conversation (MERC) aims to enhance emotion understanding by integrating complementary cues from text, audio, and visual modalities. Existing MERC approaches predominantly focus on cross-modal shared features, often overlooking modality-specific features that capture subtle yet critical emotional cues such as micro-expressions, prosodic variations, and sarcasm. Although related work in multimodal emotion recognition (MER) has explored disentangling shared and modality-specific features, these methods typically employ rigid orthogonal constraints to achieve full disentanglement, which neglects the inherent complementarity between feature types and may limit recognition performance. To address these challenges, we propose Angle-Optimized Feature Learning (AO-FL), a framework tailored for MERC that achieves partial disentanglement of shared and specific features within each modality through adaptive angular optimization. Specifically, AO-FL aligns shared features across modalities to ensure semantic consistency, and within each modality it adaptively models the angular relationship between its shared and modality-specific features to preserve both distinctiveness and complementarity. An orthogonal projection refinement further removes redundancy in specific features and enriches shared features with contextual information, yielding more discriminative multimodal representations. Extensive experiments confirm the effectiveness of AO-FL for MERC, demonstrating superior performance over state-of-the-art approaches. Moreover, AO-FL can be seamlessly integrated with various unimodal feature extractors and extended to other multimodal fusion tasks, such as MER, thereby highlighting its strong generalization beyond MERC.
Hypergraphs as Metro Maps: Drawing Paths with Few Bends in Trees, Cacti, and Plane 4-Graphs
Venue: arXiv cs.CG 2025
Year: 2025

A hypergraph consists of a set of vertices and a set of subsets of vertices, called hyperedges. In the metro map metaphor, each hyperedge is represented by a path (the metro line) and the union of all these paths is the support graph (metro network) of the hypergraph. Formally speaking, a path-based support is a graph together with a set of paths. We consider the problem of constructing drawings of path-based supports that (i) minimize the sum of the number of bends on all paths, (ii) minimize the maximum number of bends on any path, or (iii) maximize the number of 0-bend paths, then the number of 1-bend paths, etc. We concentrate on straight-line drawings of path-based tree and cactus supports as well as orthogonal drawings of path-based plane supports with maximum degree 4.
Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems
Venue: arXiv cs.LG 2025
Year: 2025

Recommender systems (RSs) output ranked lists of items, such as movies or restaurants, that users may find interesting, based on the user's past ratings and ratings from other users. RSs increasingly incorporate differential privacy (DP) to protect user data, raising questions about how privacy mechanisms affect both recommendation accuracy and fairness. We conduct a comprehensive, cross-model evaluation of two DP mechanisms, differentially private stochastic gradient descent (DPSGD) and local differential privacy (LDP), applied to four recommender systems (Neural Collaborative Filtering (NCF), Bayesian Personalized Ranking (BPR), Singular Value Decomposition (SVD), and Variational Autoencoder (VAE)) on the MovieLens-1M and Yelp datasets. We find that stronger privacy consistently reduces utility, but not uniformly. NCF under DPSGD shows the smallest accuracy loss (under 10 percent at epsilon approximately 1), whereas SVD and BPR experience larger drops, especially for users with niche preferences. VAE is the most sensitive to privacy, with sharp declines for sparsely represented groups. The impact on bias metrics is similarly heterogeneous. DPSGD generally reduces the gap between recommendations of popular and less popular items, whereas LDP preserves existing patterns more closely. These results highlight that no single DP mechanism is uniformly superior; instead, each provides trade-offs under different privacy regimes and data conditions.
CoT4AD: A Vision-Language-Action Model with Explicit Chain-of-Thought Reasoning for Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language-Action (VLA) models have recently attracted growing attention in end-to-end autonomous driving for their strong reasoning capabilities and rich world knowledge. However, existing VLAs often suffer from limited numerical reasoning ability and overly simplified input-output mappings, which hinder their performance in complex driving scenarios requiring step-by-step causal reasoning. To address these challenges, we propose CoT4AD, a novel VLA framework that introduces Chain-of-Thought (CoT) reasoning for autonomous driving to enhance both numerical and causal reasoning in Vision-Language Models (VLMs). CoT4AD integrates visual observations and language instructions to perform semantic reasoning, scene understanding, and trajectory planning. During training, it explicitly models a perception-question-prediction-action CoT to align the reasoning space with the action space across multiple driving tasks. During inference, it performs implicit CoT reasoning to enable consistent numerical reasoning and robust decision-making in dynamic environments. Extensive experiments on both real-world and simulated benchmarks, including nuScenes and Bench2Drive, demonstrate that CoT4AD achieves state-of-the-art performance in both open-loop and closed-loop evaluations. Code will be released upon paper acceptance.
A Computable Game-Theoretic Framework for Multi-Agent Theory of Mind
Venue: arXiv cs.AI 2025
Year: 2025

Originating in psychology, $\textit{Theory of Mind}$ (ToM) has attracted significant attention across multiple research communities, especially logic, economics, and robotics. Most psychological work does not aim at formalizing those central concepts, namely $\textit{goals}$, $\textit{intentions}$, and $\textit{beliefs}$, to automate a ToM-based computational process, which, by contrast, has been extensively studied by logicians. In this paper, we offer a different perspective by proposing a computational framework viewed through the lens of game theory. On the one hand, the framework prescribes how to make boudedly rational decisions while maintaining a theory of mind about others (and recursively, each of the others holding a theory of mind about the rest); on the other hand, it employs statistical techniques and approximate solutions to retain computability of the inherent computational problem.
A programming language combining quantum and classical control
Venue: arXiv cs.LO 2025
Year: 2025

The two main notions of control in quantum programming languages are often referred to as "quantum" control and "classical" control. With the latter, the control flow is based on classical information, potentially resulting from a quantum measurement, and this paradigm is well-suited to mixed state quantum computation. Whereas with quantum control, we are primarily focused on pure quantum computation and there the "control" is based on superposition. The two paradigms have not mixed well traditionally and they are almost always treated separately. In this work, we show that the paradigms may be combined within the same system. The key ingredients for achieving this are: (1) syntactically: a modality for incorporating pure quantum types into a mixed state quantum type system; (2) operationally: an adaptation of the notion of "quantum configuration" from quantum lambda-calculi, where the quantum data is replaced with pure quantum primitives; (3) denotationally: suitable (sub)categories of Hilbert spaces, for pure computation and von Neumann algebras, for mixed state computation in the Heisenberg picture of quantum mechanics.
Beyond Success: Refining Elegant Robot Manipulation from Mixed-Quality Data via Just-in-Time Intervention
Venue: arXiv cs.RO 2025
Year: 2025

Vision-Language-Action (VLA) models have enabled notable progress in general-purpose robotic manipulation, yet their learned policies often exhibit variable execution quality. We attribute this variability to the mixed-quality nature of human demonstrations, where the implicit principles that govern how actions should be carried out are only partially satisfied. To address this challenge, we introduce the LIBERO-Elegant benchmark with explicit criteria for evaluating execution quality. Using these criteria, we develop a decoupled refinement framework that improves execution quality without modifying or retraining the base VLA policy. We formalize Elegant Execution as the satisfaction of Implicit Task Constraints (ITCs) and train an Elegance Critic via offline Calibrated Q-Learning to estimate the expected quality of candidate actions. At inference time, a Just-in-Time Intervention (JITI) mechanism monitors critic confidence and intervenes only at decision-critical moments, providing selective, on-demand refinement. Experiments on LIBERO-Elegant and real-world manipulation tasks show that the learned Elegance Critic substantially improves execution quality, even on unseen tasks. The proposed model enables robotic control that values not only whether tasks succeed, but also how they are performed.