OpenRaiser/Intern-Atlas huggingface dataset:
Least Volume Analysis
Venue: arXiv cs.LG 2025
Year: 2025

This paper introduces Least Volume (LV)--a simple yet effective regularization method inspired by geometric intuition--that reduces the number of latent dimensions required by an autoencoder without prior knowledge of the dataset's intrinsic dimensionality. We show that its effectiveness depends on the Lipschitz continuity of the decoder, prove that Principal Component Analysis (PCA) is a linear special case, and demonstrate that LV induces a PCA-like importance ordering in nonlinear models. We extend LV to non-Euclidean settings as Generalized Least Volume (GLV), enabling the integration of label information into the latent representation. To support implementation, we also develop an accompanying Dynamic Pruning algorithm. We evaluate LV on several benchmark problems, demonstrating its effectiveness in dimension reduction. Leveraging this, we reveal the role of low-dimensional latent spaces in data sampling and disentangled representation, and use them to probe the varying topological complexity of various datasets. GLV is further applied to labeled datasets, where it induces a contrastive learning effect in representations of discrete labels. On a continuous-label airfoil dataset, it produces representations that lead to smooth changes in aerodynamic performance, thereby stabilizing downstream optimization.
FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data
Venue: arXiv cs.LG 2025
Year: 2025

Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed computing. To deal with it, this paper proposes a novel personalized federated learning algorithm, named Federated Contrastive Shareable Representations (FedCoSR), to facilitate knowledge sharing among clients while maintaining data privacy. Specifically, the parameters of local models' shallow layers and typical local representations are both considered as shareable information for the server and are aggregated globally. To address performance degradation caused by label distribution skew among clients, contrastive learning is adopted between local and global representations to enrich local knowledge. Additionally, to ensure fairness for clients with scarce data, FedCoSR introduces adaptive local aggregation to coordinate the global model involvement in each client. Our simulations demonstrate FedCoSR's effectiveness in mitigating label heterogeneity by achieving accuracy and fairness improvements over existing methods on datasets with varying degrees of label heterogeneity.
Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry
Venue: arXiv cs.CL 2025
Year: 2025

In the rapidly evolving landscape of Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for many tasks, such as extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of travel customer needs from TripAdvisor and Reddit posts. Leveraging a diverse range of models, including both open-source and proprietary ones such as GPT-4 and Gemini, we aim to elucidate their strengths and weaknesses in this specialized domain. Through an evaluation process involving metrics such as BERTScore, ROUGE, and BLEU, we assess the performance of each model in accurately identifying and summarizing customer needs. Our findings highlight the efficacy of opensource LLMs, particularly Mistral 7B, in achieving comparable performance to larger closed models while offering affordability and customization benefits. Additionally, we underscore the importance of considering factors such as model size, resource requirements, and performance metrics when selecting the most suitable LLM for customer needs analysis tasks. Overall, this study contributes valuable insights for businesses seeking to leverage advanced NLP techniques to enhance customer experience and drive operational efficiency in the travel industry.
Utilizing Large Language Models for Information Extraction from Real Estate Transactions
Venue: arXiv cs.CL 2025
Year: 2025

Real estate sales contracts contain crucial information for property transactions, but manual data extraction can be time-consuming and error-prone. This paper explores the application of large language models, specifically transformer-based architectures, for automated information extraction from real estate contracts. We discuss challenges, techniques, and future directions in leveraging these models to improve efficiency and accuracy in real estate contract analysis. We generated synthetic contracts using the real-world transaction dataset, thereby fine-tuning the large-language model and achieving significant metrics improvements and qualitative improvements in information retrieval and reasoning tasks.
MMAC-Copilot: Multi-modal Agent Collaboration Operating Copilot
Venue: arXiv cs.AI 2025
Year: 2025

Large language model agents that interact with PC applications often face limitations due to their singular mode of interaction with real-world environments, leading to restricted versatility and frequent hallucinations. To address this, we propose the Multi-Modal Agent Collaboration framework (MMAC-Copilot), a framework utilizes the collective expertise of diverse agents to enhance interaction ability with application. The framework introduces a team collaboration chain, enabling each participating agent to contribute insights based on their specific domain knowledge, effectively reducing the hallucination associated with knowledge domain gaps. We evaluate MMAC-Copilot using the GAIA benchmark and our newly introduced Visual Interaction Benchmark (VIBench). MMAC-Copilot achieved exceptional performance on GAIA, with an average improvement of 6.8\% over existing leading systems. VIBench focuses on non-API-interactable applications across various domains, including 3D gaming, recreation, and office scenarios. It also demonstrated remarkable capability on VIBench. We hope this work can inspire in this field and provide a more comprehensive assessment of Autonomous agents. The anonymous Github is available at \href{https://anonymous.4open.science/r/ComputerAgentWithVision-3C12}{Anonymous Github}
Asymmetric Information Enhanced Mapping Framework for Multirobot Exploration based on Deep Reinforcement Learning
Venue: arXiv cs.MA 2025
Year: 2025

Despite the great development of multirobot technologies, efficiently and collaboratively exploring an unknown environment is still a big challenge. In this paper, we propose AIM-Mapping, a Asymmetric InforMation Enhanced Mapping framework. The framework fully utilizes the privilege information in the training process to help construct the environment representation as well as the supervised signal in an asymmetric actor-critic training framework. Specifically, privilege information is used to evaluate the exploration performance through an asymmetric feature representation module and a mutual information evaluation module. The decision-making network uses the trained feature encoder to extract structure information from the environment and combines it with a topological map constructed based on geometric distance. Utilizing this kind of topological map representation, we employ topological graph matching to assign corresponding boundary points to each robot as long-term goal points. We conduct experiments in real-world-like scenarios using the Gibson simulation environments. It validates that the proposed method, when compared to existing methods, achieves great performance improvement.
ICMarks: A Robust Watermarking Framework for Integrated Circuit Physical Design IP Protection
Venue: arXiv cs.CR 2025
Year: 2025

Physical design watermarking on contemporary integrated circuit (IC) layout encodes signatures without considering the dense connections and design constraints, which could lead to performance degradation on the watermarked products. This paper presents ICMarks, a quality-preserving and robust watermarking framework for modern IC physical design. ICMarks embeds unique watermark signatures during the physical design's placement stage, thereby authenticating the IC layout ownership. ICMarks's novelty lies in (i) strategically identifying a region of cells to watermark with minimal impact on the layout performance and (ii) a two-level watermarking framework for augmented robustness toward potential removal and forging attacks. Extensive evaluations on benchmarks of different design objectives and sizes validate that ICMarks incurs no wirelength and timing metrics degradation, while successfully proving ownership. Furthermore, we demonstrate ICMarks is robust against two major watermarking attack categories, namely, watermark removal and forging attacks; even if the adversaries have prior knowledge of the watermarking schemes, the signatures cannot be removed without significantly undermining the layout quality.
ir_explain: a Python Library of Explainable IR Methods
Venue: arXiv cs.IR 2025
Year: 2025

While recent advancements in Neural Ranking Models have resulted in significant improvements over traditional statistical retrieval models, it is generally acknowledged that the use of large neural architectures and the application of complex language models in Information Retrieval (IR) have reduced the transparency of retrieval methods. Consequently, Explainability and Interpretability have emerged as important research topics in IR. Several axiomatic and post-hoc explanation methods, as well as approaches that attempt to be interpretable-by-design, have been proposed. This article presents \irexplain, an open-source Python library that implements a variety of well-known techniques for Explainable IR (ExIR) within a common, extensible framework. \irexplain supports the three standard categories of post-hoc explanations, namely pointwise, pairwise, and listwise explanations. The library is designed to make it easy to reproduce state-of-the-art ExIR baselines on standard test collections, as well as to explore new approaches to explaining IR models and methods. To facilitate adoption, \irexplain is well-integrated with widely-used toolkits such as Pyserini and \irdatasets.
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification
Venue: arXiv cs.LG 2025
Year: 2025

The automated real-time recognition of unexpected situations plays a crucial role in the safety of autonomous vehicles, especially in unsupported and unpredictable scenarios. This paper evaluates different Bayesian uncertainty quantification methods from the deep learning domain for the anticipatory testing of safety-critical misbehaviours during system-level simulation-based testing. Specifically, we compute uncertainty scores as the vehicle executes, following the intuition that high uncertainty scores are indicative of unsupported runtime conditions that can be used to distinguish safe from failure-inducing driving behaviors. In our study, we conducted an evaluation of the effectiveness and computational overhead associated with two Bayesian uncertainty quantification methods, namely MC- Dropout and Deep Ensembles, for misbehaviour avoidance. Overall, for three benchmarks from the Udacity simulator comprising both out-of-distribution and unsafe conditions introduced via mutation testing, both methods successfully detected a high number of out-of-bounds episodes providing early warnings several seconds in advance, outperforming two state-of-the-art misbehaviour prediction methods based on autoencoders and attention maps in terms of effectiveness and efficiency. Notably, Deep Ensembles detected most misbehaviours without any false alarms and did so even when employing a relatively small number of models, making them computationally feasible for real-time detection. Our findings suggest that incorporating uncertainty quantification methods is a viable approach for building fail-safe mechanisms in deep neural network-based autonomous vehicles.
Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?
Venue: arXiv cs.CL 2025
Year: 2025

Vision and language model (VLM) decoders are currently the best-performing architectures on multimodal tasks. Next to answers, they are able to produce natural language explanations, either in post-hoc or CoT settings. However, it is not clear to what extent they are using the input vision and text modalities when generating answers or explanations. In this work, we investigate if VLMs rely on their input modalities differently when they produce explanations as opposed to answers. We also evaluate the self-consistency of VLM decoders in both post-hoc and CoT explanation settings, by extending existing unimodal tests and measures to VLM decoders. We find that most tested VLMs are less self-consistent than LLMs. Text contributions in all tested VL decoders are more important than image contributions in all examined tasks. However, when comparing explanation generation to answer generation, the contributions of images are significantly stronger for generating explanations compared to answers. This difference is even larger in CoT compared to post-hoc explanations. Lastly, we provide an up-to-date benchmarking of state-of-the-art VL decoders on the VALSE benchmark, which before was restricted to VL encoders. We find that the tested VL decoders still struggle with most phenomena tested by VALSE.
Private graph colouring with limited defectiveness
Venue: arXiv cs.DS 2025
Year: 2025

Differential privacy is the gold standard in the problem of privacy preserving data analysis, which is crucial in a wide range of disciplines. Vertex colouring is one of the most fundamental questions about a graph. In this paper, we study the vertex colouring problem in the differentially private setting. To be edge-differentially private, a colouring algorithm needs to be defective: a colouring is d-defective if a vertex can share a colour with at most d of its neighbours. Without defectiveness, the only differentially private colouring algorithm needs to assign n different colours to the n different vertices. We show the following lower bound for the defectiveness: a differentially private c-edge colouring algorithm of a graph of maximum degree Δ > 0 has defectiveness at least d = Ω (log n / (log c+log Δ)). We also present an ε-differentially private algorithm to Θ ( Δ / log n + 1 / ε)-colour a graph with defectiveness at most Θ(log n).
Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models
Venue: arXiv cs.LG 2025
Year: 2025

Pretraining and finetuning models has become increasingly popular in decision-making. But there are still serious impediments in Imitation Learning from Observation (ILfO) with pretrained models. This study identifies two primary obstacles: the Embodiment Knowledge Barrier (EKB) and the Demonstration Knowledge Barrier (DKB). The EKB emerges due to the pretrained models' limitations in handling novel observations, which leads to inaccurate action inference. Conversely, the DKB stems from the reliance on limited demonstration datasets, restricting the model's adaptability across diverse scenarios. We propose separate solutions to overcome each barrier and apply them to Action Inference by Maximising Evidence (AIME), a state-of-the-art algorithm. This new algorithm, AIME-NoB, integrates online interactions and a data-driven regulariser to mitigate the EKB. Additionally, it uses a surrogate reward function to broaden the policy's supported states, addressing the DKB. Our experiments on vision-based control tasks from the DeepMind Control Suite and MetaWorld benchmarks show that AIME-NoB significantly improves sample efficiency and converged performance, presenting a robust framework for overcoming the challenges in ILfO with pretrained models. Code available at https://github.com/IcarusWizard/AIME-NoB.
Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions
Venue: arXiv cs.CV 2025
Year: 2025

Neural Radiance Fields (NeRF) show impressive performance in photo-realistic free-view rendering of scenes. Recent improvements on the NeRF such as TensoRF and ZipNeRF employ explicit models for faster optimization and rendering, as compared to the NeRF that employs an implicit representation. However, both implicit and explicit radiance fields require dense sampling of images in the given scene. Their performance degrades significantly when only a sparse set of views is available. Researchers find that supervising the depth estimated by a radiance field helps train it effectively with fewer views. The depth supervision is obtained either using classical approaches or neural networks pre-trained on a large dataset. While the former may provide only sparse supervision, the latter may suffer from generalization issues. As opposed to the earlier approaches, we seek to learn the depth supervision by designing augmented models and training them along with the main radiance field. Further, we aim to design a framework of regularizations that can work across different implicit and explicit radiance fields. We observe that certain features of these radiance field models overfit to the observed images in the sparse-input scenario. Our key finding is that reducing the capability of the radiance fields with respect to positional encoding, the number of decomposed tensor components or the size of the hash table, constrains the model to learn simpler solutions, which estimate better depth in certain regions. By designing augmented models based on such reduced capabilities, we obtain better depth supervision for the main radiance field. We achieve state-of-the-art view-synthesis performance with sparse input views on popular datasets containing forward-facing and 360$^\circ$ scenes by employing the above regularizations.
A Unified Theory of Exact Inference and Learning in Exponential Family Latent Variable Models
Venue: arXiv cs.LG 2025
Year: 2025

Bayes' rule describes how to infer posterior beliefs about latent variables given observations, and inference is a critical step in learning algorithms for latent variable models (LVMs). Although there are exact algorithms for inference and learning for certain LVMs such as linear Gaussian models and mixture models, researchers must typically develop approximate inference and learning algorithms when applying novel LVMs. Here we study the line that separates LVMs that rely on approximation schemes from those that do not, and develop a general theory of exponential family LVMs for which inference and learning may be implemented exactly. Firstly, under mild assumptions about the exponential family form of the LVM, we derive a necessary and sufficient constraint on the parameters of the LVM under which the prior and posterior over the latent variables are in the same exponential family. We then show that a variety of well-known and novel models indeed have this constrained, exponential family form. Finally, we derive generalized inference and learning algorithms for these LVMs, and demonstrate them with a variety of examples. Our unified perspective facilitates both understanding and implementing exact inference and learning algorithms for a wide variety of models, and may guide researchers in the discovery of new models that avoid unnecessary approximations.
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning. To preserve the model fidelity, PAODING adopts an iterative process, which dynamically measures the effect of deleting a neuron to identify candidates that have the least impact to the output layer. Our evaluation shows that PAODING can significantly reduce the model size, generalize on different datasets and models, and meanwhile preserve the model fidelity in terms of test accuracy and adversarial robustness. PAODING is publicly available on PyPI via https://pypi.org/project/paoding-dl.
Aptly: Making Mobile Apps from Natural Language
Venue: arXiv cs.HC 2025
Year: 2025

This paper introduces Aptly, a platform designed to democratize mobile app development, particularly for young learners. Aptly integrates a Large Language Model (LLM) with App Inventor, enabling users to create apps using their natural language. User's description is translated into a programming language that corresponds with App Inventor's visual blocks. A preliminary study with high school students demonstrated the usability and potential of the platform. Prior programming experience influenced how users interact with Aptly. Participants identified areas for improvement and expressed a shift in perspective regarding programming accessibility and AI's role in creative endeavors.
HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs
Venue: arXiv cs.LG 2025
Year: 2025

Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quantization (HGQ), a quantization-aware training framework that optimizes parameter bit-widths through gradient descent. Unlike conventional methods, HGQ determines the optimal bit-width for each parameter independently, making it suitable for hardware platforms supporting heterogeneous arbitrary precision arithmetic. In our experiments, HGQ shows superior performance compared to existing network compression methods, achieving orders of magnitude reduction in resource consumption and latency while maintaining the accuracy on several benchmark tasks. These improvements enable the deployment of complex models previously infeasible due to resource or latency constraints. HGQ is open-source and is used for developing next-generation trigger systems at the CERN ATLAS and CMS experiments for particle physics, enabling the use of advanced machine learning models for real-time data selection with sub-microsecond latency.
Not a Swiss Army Knife: Academics' Perceptions of Trade-Offs Around Generative Artificial Intelligence Use
Venue: arXiv cs.CY 2025
Year: 2025

In the rapidly evolving landscape of computing disciplines, substantial efforts are being dedicated to unraveling the sociotechnical implications of generative AI (Gen AI). While existing research has manifested in various forms, there remains a notable gap concerning the direct engagement of knowledge workers in academia with Gen AI. We interviewed 17 knowledge workers, including faculty and students, to investigate the social and technical dimensions of Gen AI from their perspective. Our participants raised concerns about the opacity of the data used to train Gen AI. This lack of transparency makes it difficult to identify and address inaccurate, biased, and potentially harmful, information generated by these models. Knowledge workers also expressed worries about Gen AI undermining trust in the relationship between instructor and student and discussed potential solutions, such as pedagogy readiness, to mitigate them. Additionally, participants recognized Gen AI's potential to democratize knowledge by accelerating the learning process and act as an accessible research assistant. However, there were also concerns about potential social and power imbalances stemming from unequal access to such technologies. Our study offers insights into the concerns and hopes of knowledge workers about the ethical use of Gen AI in educational settings and beyond, with implications for navigating this new landscape.