OpenRaiser/Intern-Atlas huggingface dataset:
ADPro: a Test-time Adaptive Diffusion Policy via Manifold-constrained Denoising and Task-aware Initialization for Robotic Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Diffusion policies have recently emerged as a powerful class of visuomotor controllers for robot manipulation, offering stable training and expressive multi-modal action modeling. However, existing approaches typically treat action generation as an unconstrained denoising process, ignoring valuable a priori knowledge about geometry and control structure. In this work, we propose the Adaptive Diffusion Policy (ADP), a test-time adaptation method that introduces two key inductive biases into the diffusion. First, we embed a geometric manifold constraint that aligns denoising updates with task-relevant subspaces, leveraging the fact that the relative pose between the end-effector and target scene provides a natural gradient direction, and guiding denoising along the geodesic path of the manipulation manifold. Then, to reduce unnecessary exploration and accelerate convergence, we propose an analytically guided initialization: rather than sampling from an uninformative prior, we compute a rough registration between the gripper and target scenes to propose a structured initial noisy action. ADP is compatible with pre-trained diffusion policies and requires no retraining, enabling test-time adaptation that tailors the policy to specific tasks, thereby enhancing generalization across novel tasks and environments. Experiments on RLBench, CALVIN, and real-world datasets show that ADPro, an implementation of ADP, improves success rates, generalization, and sampling efficiency, achieving up to 25% faster execution and 9% points over strong diffusion baselines.
Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs
Venue: arXiv cs.RO 2025
Year: 2025

Contemporary industrial cyber-physical production systems (CPPS) composed of robotic workcells face significant challenges in the analysis of undesired conditions due to the flexibility of Industry 4.0 that disrupts traditional quality assurance mechanisms. This paper presents a novel industry-oriented semantic model called Product-Process-Resource Asset Knowledge Graph (PPR-AKG), which is designed to analyze and mitigate undesired conditions in flexible CPPS. Built on top of the well-proven Product-Process-Resource (PPR) model originating from ISA-95 and VDI-3682, a comprehensive OWL ontology addresses shortcomings of conventional model-driven engineering for CPPS, particularly inadequate undesired condition and error handling representation. The integration of semantic technologies with large language models (LLMs) provides intuitive interfaces for factory operators, production planners, and engineers to interact with the entire model using natural language. Evaluation with the use case addressing electric vehicle battery remanufacturing demonstrates that the PPR-AKG approach efficiently supports resource allocation based on explicitly represented capabilities as well as identification and mitigation of undesired conditions in production. The key contributions include (1) a holistic PPR-AKG model capturing multi-dimensional production knowledge, and (2) the useful combination of the PPR-AKG with LLM-based chatbots for human interaction.
Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback
Venue: arXiv cs.LG 2025
Year: 2025

Neuromorphic computing is an emerging technology enabling low-latency and energy-efficient signal processing. A key algorithmic tool in neuromorphic computing is spiking neural networks (SNNs). SNNs are biologically inspired neural networks which utilize stateful neurons, and provide low-bit data processing by encoding and decoding information using spikes. Similar to SNNs, deep state-space models (SSMs) utilize stateful building blocks. However, deep SSMs, which recently achieved competitive performance in various temporal modeling tasks, are typically designed with high-precision activation functions and no reset mechanisms. To bridge the gains offered by SNNs and the recent deep SSM models, we propose a novel multiple-output spiking neuron model that combines a linear, general SSM state transition with a non-linear feedback mechanism through reset. Compared to the existing neuron models for SNNs, our proposed model clearly conceptualizes the differences between the spiking function, the reset condition and the reset action. The experimental results on various tasks, i.e., a keyword spotting task, an event-based vision task and a sequential pattern recognition task, show that our proposed model achieves performance comparable to existing benchmarks in the SNN literature. Our results illustrate how the proposed reset mechanism can overcome instability and enable learning even when the linear part of neuron dynamics is unstable, allowing us to go beyond the strictly enforced stability of linear dynamics in recent deep SSM models.
Unsupervised Partner Design Enables Robust Ad-hoc Teamwork
Venue: arXiv cs.LG 2025
Year: 2025

We introduce Unsupervised Partner Design (UPD) - a population-free, multi-agent reinforcement learning framework for robust ad-hoc teamwork that adaptively generates training partners without requiring pretrained partners or manual parameter tuning. UPD constructs diverse partners by stochastically mixing an ego agent's policy with biased random behaviours and scores them using a variance-based learnability metric that prioritises partners near the ego agent's current learning frontier. We show that UPD can be integrated with unsupervised environment design, resulting in the first method enabling fully unsupervised curricula over both level and partner distributions in a cooperative setting. Through extensive evaluations on Overcooked-AI and the Overcooked Generalisation Challenge, we demonstrate that this dynamic partner curriculum is highly effective: UPD consistently outperforms both population-based and population-free baselines as well as ablations. In a user study, we further show that UPD achieves higher returns than all baselines and was perceived as significantly more adaptive, more human-like, a better collaborator, and less frustrating.
Nail: Not Another Fault-Injection Framework for Chisel-generated RTL
Venue: arXiv cs.AR 2025
Year: 2025

Fault simulation and emulation are essential techniques for evaluating the dependability of integrated circuits, enabling early-stage vulnerability analysis and supporting the implementation of effective mitigation strategies. High-level hardware description languages such as Chisel facilitate the rapid development of complex fault scenarios with minimal modification to the design. However, existing Chisel-based fault injection (FI) frameworks are limited by coarse-grained, instruction-level controllability, restricting the precision of fault modeling. This work introduces Nail, a Chisel-based open-source FI framework that overcomes these limitations by introducing state-based faults. This approach enables fault scenarios that depend on specific system states, rather than solely on instruction-level triggers, thereby removing the need for precise timing of fault activation. For greater controllability, Nail allows users to arbitrarily modify internal trigger states via software at runtime. To support this, Nail automatically generates a software interface, offering straightforward access to the instrumented design. This enables fine-tuning of fault parameters during active FI campaigns - a feature particularly beneficial for FPGA emulation, where synthesis is time-consuming. Utilizing these features, Nail narrows the gap between the high speed of emulation-based FI frameworks, the usability of software-based approaches, and the controllability achieved in simulation. We demonstrate Nail's state-based FI and software framework by modeling a faulty general-purpose register in a RISC-V processor. Although this might appear straightforward, it requires state-dependent FI and was previously impossible without fundamental changes to the design. The approach was validated in both simulation and FPGA emulation, where the addition of Nail introduced less than 1% resource overhead.
AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games
Venue: arXiv cs.AI 2025
Year: 2025

Cheating in online video games compromises the integrity of gaming experiences. Anti-cheat systems, such as VAC (Valve Anti-Cheat), face significant challenges in keeping pace with evolving cheating methods without imposing invasive measures on users' systems. This paper presents AntiCheatPT\_256, a transformer-based machine learning model designed to detect cheating behaviour in Counter-Strike 2 using gameplay data. To support this, we introduce and publicly release CS2CD: A labelled dataset of 795 matches. Using this dataset, 90,707 context windows were created and subsequently augmented to address class imbalance. The transformer model, trained on these windows, achieved an accuracy of 89.17\% and an AUC of 93.36\% on an unaugmented test set. This approach emphasizes reproducibility and real-world applicability, offering a robust baseline for future research in data-driven cheat detection.
Emoji Reactions on Telegram: Unreliable Indicators of Emotional Resonance
Venue: arXiv cs.HC 2025
Year: 2025

Emoji reactions are a frequently used feature of messaging platforms, yet their communicative role remains understudied. Prior work on emojis has focused predominantly on in-text usage, showing that emojis embedded in messages tend to amplify and mirror the author's affective tone. This evidence has often been extended to emoji reactions, treating them as indicators of emotional resonance or user sentiment. However, they may reflect broader social dynamics. Here, we investigate the communicative function of emoji reactions on Telegram. We analyze over 650k crypto-related messages that received at least one reaction, annotating each with sentiment, emotion, persuasion strategy, and speech act labels, and inferring the sentiment and emotion of emoji reactions using both lexicons and LLMs. We uncover a systematic mismatch between message and reaction sentiment, with positive reactions dominating even for neutral or negative content. This pattern persists across rhetorical strategies and emotional tones, indicating that emojis used as reactions do not reliably function as indicators of emotional mirroring or resonance of the content, in contrast to findings reported for in-text emojis. Finally, we identify the features that most predict emoji engagement. Overall, our findings caution against treating emoji reactions as sentiment labels, highlighting the need for more nuanced approaches in sentiment and engagement analysis.
Are you In or Out (of gallery)? Wisdom from the Same-Identity Crowd
Venue: arXiv cs.CV 2025
Year: 2025

A central problem in one-to-many facial identification is that the person in the probe image may or may not have enrolled image(s) in the gallery; that is, may be In-gallery or Out-of-gallery. Past approaches to detect when a rank-one result is Out-of-gallery have mostly focused on finding a suitable threshold on the similarity score. We take a new approach, using the additional enrolled images of the identity with the rank-one result to predict if the rank-one result is In-gallery / Out-of-gallery. Given a gallery of identities and images, we generate In-gallery and Out-of-gallery training data by extracting the ranks of additional enrolled images corresponding to the rank-one identity. We then train a classifier to utilize this feature vector to predict whether a rank-one result is In-gallery or Out-of-gallery. Using two different datasets and four different matchers, we present experimental results showing that our approach is viable for mugshot quality probe images, and also, importantly, for probes degraded by blur, reduced resolution, atmospheric turbulence and sunglasses. We also analyze results across demographic groups, and show that In-gallery / Out-of-gallery classification accuracy is similar across demographics. Our approach has the potential to provide an objective estimate of whether a one-to-many facial identification is Out-of-gallery, and thereby to reduce false positive identifications, wrongful arrests, and wasted investigative time. Interestingly, comparing the results of older deep CNN-based face matchers with newer ones suggests that the effectiveness of our Out-of-gallery detection approach emerges only with matchers trained using advanced margin-based loss functions.
Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization
Venue: arXiv cs.CE 2025
Year: 2025

Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically optimal allocation of land to margin and habitat interventions at the individual farm level. These farm-specific intervention levels are then passed to an Ecological Connectivity (EC) model, which spatially arranges them across the landscape to maximize connectivity while preserving farm-level profitability. Finally, we introduce a Bayesian Optimization (BO) approach that translates these spatial outcomes into simple, cost effective, and scalable policy instruments, such as subsidies and eco-premiums, using non-spatial, farm-level policy parameters. Applying the framework to a Canadian agricultural landscape, we demonstrate how it enhances connectivity under real-world economic constraints. Our approach provides a globally relevant tool for aligning farm incentives with biodiversity goals, advancing the development of agri-environmental policies that are economically viable and ecologically effective.
Feature-Space Oversampling for Addressing Class Imbalance in SAR Ship Classification
Venue: arXiv cs.CV 2025
Year: 2025

SAR ship classification faces the challenge of long-tailed datasets, which complicates the classification of underrepresented classes. Oversampling methods have proven effective in addressing class imbalance in optical data. In this paper, we evaluated the effect of oversampling in the feature space for SAR ship classification. We propose two novel algorithms inspired by the Major-to-minor (M2m) method M2m$_f$, M2m$_u$. The algorithms are tested on two public datasets, OpenSARShip (6 classes) and FuSARShip (9 classes), using three state-of-the-art models as feature extractors: ViT, VGG16, and ResNet50. Additionally, we also analyzed the impact of oversampling methods on different class sizes. The results demonstrated the effectiveness of our novel methods over the original M2m and baselines, with an average F1-score increase of 8.82% for FuSARShip and 4.44% for OpenSARShip.
CLIPin: A Non-contrastive Plug-in to CLIP for Multimodal Semantic Alignment
Venue: arXiv cs.CV 2025
Year: 2025

Large-scale natural image-text datasets, especially those automatically collected from the web, often suffer from loose semantic alignment due to weak supervision, while medical datasets tend to have high cross-modal correlation but low content diversity. These properties pose a common challenge for contrastive language-image pretraining (CLIP): they hinder the model's ability to learn robust and generalizable representations. In this work, we propose CLIPin, a unified non-contrastive plug-in that can be seamlessly integrated into CLIP-style architectures to improve multimodal semantic alignment, providing stronger supervision and enhancing alignment robustness. Furthermore, two shared pre-projectors are designed for image and text modalities respectively to facilitate the integration of contrastive and non-contrastive learning in a parameter-compromise manner. Extensive experiments on diverse downstream tasks demonstrate the effectiveness and generality of CLIPin as a plug-and-play component compatible with various contrastive frameworks. Code is available at https://github.com/T6Yang/CLIPin.
What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting
Venue: arXiv cs.AI 2025
Year: 2025

Committee-selection problems arise in many contexts and applications, and there has been increasing interest within the social choice research community on identifying which properties are satisfied by different multi-winner voting rules. In this work, we propose a data-driven framework to evaluate how frequently voting rules violate axioms across diverse preference distributions in practice, shifting away from the binary perspective of axiom satisfaction given by worst-case analysis. Using this framework, we analyze the relationship between multi-winner voting rules and their axiomatic performance under several preference distributions. We then show that neural networks, acting as voting rules, can outperform traditional rules in minimizing axiom violations. Our results suggest that data-driven approaches to social choice can inform the design of new voting systems and support the continuation of data-driven research in social choice.
An Online Multi-dimensional Knapsack Approach for Slice Admission Control
Venue: arXiv cs.NI 2025
Year: 2025

Network Slicing has emerged as a powerful technique to enable cost-effective, multi-tenant communications and services over a shared physical mobile network infrastructure. One major challenge of service provisioning in slice-enabled networks is the uncertainty in the demand for the limited network resources that must be shared among existing slices and potentially new Network Slice Requests. In this paper, we consider admission control of Network Slice Requests in an online setting, with the goal of maximizing the long-term revenue received from admitted requests. We model the Slice Admission Control problem as an Online Multidimensional Knapsack Problem and present two reservation-based policies and their algorithms, which have a competitive performance for Online Multidimensional Knapsack Problems. Through Monte Carlo simulations, we evaluate the performance of our online admission control method in terms of average revenue gained by the Infrastructure Provider, system resource utilization, and the ratio of accepted slice requests. We compare our approach with those of the online First Come First Serve greedy policy. The simulation's results prove that our proposed online policies increase revenues for Infrastructure Providers by up to 12.9 % while reducing the average resource consumption by up to 1.7% In particular, when the tenants' economic inequality increases, an Infrastructure Provider who adopts our proposed online admission policies gains higher revenues compared to an Infrastructure Provider who adopts First Come First Serve.
Does block size matter in randomized block Krylov low-rank approximation?
Venue: arXiv cs.DS 2025
Year: 2025

We study the problem of computing a rank-$k$ approximation of a matrix using randomized block Krylov iteration. Prior work has shown that, for block size $b = 1$ or $b = k$, a $(1 + \varepsilon)$-factor approximation to the best rank-$k$ approximation can be obtained after $\tilde O(k/\sqrt{\varepsilon})$ matrix-vector products with the target matrix. On the other hand, when $b$ is between $1$ and $k$, the best known bound on the number of matrix-vector products scales with $b(k-b)$, which could be as large as $O(k^2)$. Nevertheless, in practice, the performance of block Krylov methods is often optimized by choosing a block size $1 \ll b \ll k$. We resolve this theory-practice gap by proving that randomized block Krylov iteration produces a $(1 + \varepsilon)$-factor approximate rank-$k$ approximation using $\tilde O(k/\sqrt{\varepsilon})$ matrix-vector products for any block size $1\le b\le k$. Our analysis relies on new bounds for the minimum singular value of a random block Krylov matrix, which may be of independent interest. Similar bounds are central to recent breakthroughs on faster algorithms for sparse linear systems [Peng & Vempala, SODA 2021; Nie, STOC 2022].
Semi-automated Fact-checking in Portuguese: Corpora Enrichment using Retrieval with Claim extraction
Venue: arXiv cs.CL 2025
Year: 2025

The accelerated dissemination of disinformation often outpaces the capacity for manual fact-checking, highlighting the urgent need for Semi-Automated Fact-Checking (SAFC) systems. Within the Portuguese language context, there is a noted scarcity of publicly available datasets that integrate external evidence, an essential component for developing robust AFC systems, as many existing resources focus solely on classification based on intrinsic text features. This dissertation addresses this gap by developing, applying, and analyzing a methodology to enrich Portuguese news corpora (Fake.Br, COVID19.BR, MuMiN-PT) with external evidence. The approach simulates a user's verification process, employing Large Language Models (LLMs, specifically Gemini 1.5 Flash) to extract the main claim from texts and search engine APIs (Google Search API, Google FactCheck Claims Search API) to retrieve relevant external documents (evidence). Additionally, a data validation and preprocessing framework, including near-duplicate detection, is introduced to enhance the quality of the base corpora.
Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Automated polyp segmentation is essential for early diagnosis of colorectal cancer, yet developing robust models remains challenging due to limited annotated data and significant performance degradation under domain shift. Although semi-supervised learning (SSL) reduces annotation requirements, existing methods rely on generic augmentations that ignore polyp-specific structural properties, resulting in poor generalization to new imaging centers and devices. To address this, we introduce Frequency Prior Guided Matching (FPGM), a novel augmentation framework built on a key discovery: polyp edges exhibit a remarkably consistent frequency signature across diverse datasets. FPGM leverages this intrinsic regularity in a two-stage process. It first learns a domain-invariant frequency prior from the edge regions of labeled polyps. Then, it performs principled spectral perturbations on unlabeled images, aligning their amplitude spectra with this learned prior while preserving phase information to maintain structural integrity. This targeted alignment normalizes domain-specific textural variations, thereby compelling the model to learn the underlying, generalizable anatomical structure. Validated on six public datasets, FPGM establishes a new state-of-the-art against ten competing methods. It demonstrates exceptional zero-shot generalization capabilities, achieving over 10% absolute gain in Dice score in data-scarce scenarios. By significantly enhancing cross-domain robustness, FPGM presents a powerful solution for clinically deployable polyp segmentation under limited supervision.