OpenRaiser/Intern-Atlas huggingface dataset:
GRMM: Real-Time High-Fidelity Gaussian Morphable Head Model with Learned Residuals
Venue: arXiv cs.GR 2025
Year: 2025

3D Morphable Models (3DMMs) enable controllable facial geometry and expression editing for reconstruction, animation, and AR/VR, but traditional PCA-based mesh models are limited in resolution, detail, and photorealism. Neural volumetric methods improve realism but remain too slow for interactive use. Recent Gaussian Splatting (3DGS) based facial models achieve fast, high-quality rendering but still depend solely on a mesh-based 3DMM prior for expression control, limiting their ability to capture fine-grained geometry, expressions, and full-head coverage. We introduce GRMM, the first full-head Gaussian 3D morphable model that augments a base 3DMM with residual geometry and appearance components, additive refinements that recover high-frequency details such as wrinkles, fine skin texture, and hairline variations. GRMM provides disentangled control through low-dimensional, interpretable parameters (e.g., identity shape, facial expressions) while separately modelling residuals that capture subject- and expression-specific detail beyond the base model's capacity. Coarse decoders produce vertex-level mesh deformations, fine decoders represent per-Gaussian appearance, and a lightweight CNN refines rasterised images for enhanced realism, all while maintaining 75 FPS real-time rendering. To learn consistent, high-fidelity residuals, we present EXPRESS-50, the first dataset with 60 aligned expressions across 50 identities, enabling robust disentanglement of identity and expression in Gaussian-based 3DMMs. Across monocular 3D face reconstruction, novel-view synthesis, and expression transfer, GRMM surpasses state-of-the-art methods in fidelity and expression accuracy while delivering interactive real-time performance.
A Theoretical Framework of the Processes of Change in Psychotherapy Delivered by Artificial Agents
Venue: arXiv cs.HC 2025
Year: 2025

The question of whether artificial agents (e.g., chatbots and social robots) can replace human therapists has received notable attention following the recent launch of large language models. However, little is known about the processes of change in psychotherapy delivered by artificial agents. To facilitate hypothesis development and stimulate scientific debate, the present article offers the first theoretical framework of the processes of change in psychotherapy delivered by artificial agents. The theoretical framework rests upon a conceptual analysis of what active ingredients may be inherently linked to the presence of human therapists. We propose that human therapists' ontological status as human beings and sociocultural status as socially sanctioned healthcare professionals play crucial roles in promoting treatment outcomes. In the absence of the ontological and sociocultural status of human therapists, we propose what we coin the genuineness gap and credibility gap can emerge and undermine key processes of change in psychotherapy. Based on these propositions, we propose avenues for scientific investigations and practical applications aimed at leveraging the strengths of artificial agents and human therapists respectively. We also highlight the intricate agentic nature of artificial agents and discuss how this complicates endeavors to establish universally applicable propositions regarding the processes of change in these interventions.
Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety
Venue: arXiv cs.RO 2025
Year: 2025

Integrating large language models (LLMs) into robotic systems has revolutionised embodied artificial intelligence, enabling advanced decision-making and adaptability. However, ensuring reliability, encompassing both security against adversarial attacks and safety in complex environments, remains a critical challenge. To address this, we propose a unified framework that mitigates prompt injection attacks while enforcing operational safety through robust validation mechanisms. Our approach combines prompt assembling, state management, and safety validation, evaluated using both performance and security metrics. Experiments show a 30.8% improvement under injection attacks and up to a 325% improvement in complex environment settings under adversarial conditions compared to baseline scenarios. This work bridges the gap between safety and security in LLM-based robotic systems, offering actionable insights for deploying reliable LLM-integrated mobile robots in real-world settings. The framework is open-sourced with simulation and physical deployment demos at https://llmeyesim.vercel.app/
Understanding Space Is Rocket Science -- Only Top Reasoning Models Can Solve Spatial Understanding Tasks
Venue: arXiv cs.CV 2025
Year: 2025

We propose RocketScience, an open-source contrastive VLM benchmark that tests for spatial relation understanding. It is comprised of entirely new real-world image-text pairs covering mostly relative spatial understanding and the order of objects. The benchmark is designed to be very easy for humans and hard for the current generation of VLMs, and this is empirically verified. Our results show a striking lack of spatial relation understanding in open source and frontier commercial VLMs and a surprisingly high performance of reasoning models. Additionally, we perform a disentanglement analysis to separate the contributions of object localization and spatial reasoning in chain-of-thought-based models and find that the performance on the benchmark is bottlenecked by spatial reasoning and not object localization capabilities. We release the dataset with a CC-BY-4.0 license and make the evaluation code available at: https://github.com/nilshoehing/rocketscience
Fast Computation of $k$-Runs, Parameterized Squares, and Other Generalised Squares
Venue: arXiv cs.DS 2025
Year: 2025

A $k$-mismatch square is a string of the form $XY$ where $X$ and $Y$ are two equal-length strings that have at most $k$ mismatches. Kolpakov and Kucherov [Theor. Comput. Sci., 2003] defined two notions of $k$-mismatch repeats, called $k$-repetitions and $k$-runs, each representing a sequence of consecutive $k$-mismatch squares of equal length. They proposed algorithms for computing $k$-repetitions and $k$-runs working in $O(nk \log k + output)$ time for a string of length $n$ over an integer alphabet, where $output$ is the number of the reported repeats. We show that $output=O(nk \log k)$, both in case of $k$-repetitions and $k$-runs, which implies that the complexity of their algorithms is actually $O(nk \log k)$. We apply this result to computing parameterized squares. A parameterized square is a string of the form $XY$ such that $X$ and $Y$ parameterized-match, i.e., there exists a bijection $f$ on the alphabet such that $f(X) = Y$. Two parameterized squares $XY$ and $X'Y'$ are equivalent if they parameterized match. Recently Hamai et al. [SPIRE 2024] showed that a string of length $n$ over an alphabet of size $σ$ contains less than $nσ$ non-equivalent parameterized squares, improving an earlier bound by Kociumaka et al. [Theor. Comput. Sci., 2016]. We apply our bound for $k$-mismatch repeats to propose an algorithm that reports all non-equivalent parameterized squares in $O(nσ\log σ)$ time. We also show that the number of non-equivalent parameterized squares can be computed in $O(n \log n)$ time. This last algorithm applies to squares under any substring compatible equivalence relation and also to counting squares that are distinct as strings. In particular, this improves upon the $O(nσ)$-time algorithm of Gawrychowski et al. [CPM 2023] for counting order-preserving squares that are distinct as strings if $σ= ω(\log n)$.
ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking
Venue: arXiv cs.CV 2025
Year: 2025

We introduce a novel tracklet-based dataset for benchmarking test-time adaptation (TTA) methods. The aim of this dataset is to mimic the intricate challenges encountered in real-world environments such as images captured by hand-held cameras, self-driving cars, etc. The current benchmarks for TTA focus on how models face distribution shifts, when deployed, and on violations to the customary independent-and-identically-distributed (i.i.d.) assumption in machine learning. Yet, these benchmarks fail to faithfully represent realistic scenarios that naturally display temporal dependencies, such as how consecutive frames from a video stream likely show the same object across time. We address this shortcoming of current datasets by proposing a novel TTA benchmark we call the "Inherent Temporal Dependencies" (ITD) dataset. We ensure the instances in ITD naturally embody temporal dependencies by collecting them from tracklets-sequences of object-centric images we compile from the bounding boxes of an object-tracking dataset. We use ITD to conduct a thorough experimental analysis of current TTA methods, and shed light on the limitations of these methods when faced with the challenges of temporal dependencies. Moreover, we build upon these insights and propose a novel adversarial memory initialization strategy to improve memory-based TTA methods. We find this strategy substantially boosts the performance of various methods on our challenging benchmark.
Simulating classification models to evaluate Predict-Then-Optimize methods
Venue: arXiv cs.LG 2025
Year: 2025

Uncertainty in optimization is often represented as stochastic parameters in the optimization model. In Predict-Then-Optimize approaches, predictions of a machine learning model are used as values for such parameters, effectively transforming the stochastic optimization problem into a deterministic one. This two-stage framework is built on the assumption that more accurate predictions result in solutions that are closer to the actual optimal solution. However, providing evidence for this assumption in the context of complex, constrained optimization problems is challenging and often overlooked in the literature. Simulating predictions of machine learning models offers a way to (experimentally) analyze how prediction error impacts solution quality without the need to train real models. Complementing an algorithm from the literature for simulating binary classification, we introduce a new algorithm for simulating predictions of multiclass classifiers. We conduct a computational study to evaluate the performance of these algorithms, and show that classifier performance can be simulated with reasonable accuracy, although some variability is observed. Additionally, we apply these algorithms to assess the performance of a Predict-Then-Optimize algorithm for a machine scheduling problem. The experiments demonstrate that the relationship between prediction error and how close solutions are to the actual optimum is non-trivial, highlighting important considerations for the design and evaluation of decision-making systems based on machine learning predictions.
LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents
Venue: arXiv cs.AI 2025
Year: 2025

The rise of Large Language Models (LLMs) has had a profoundly transformative effect on a number of fields and domains. However, their uptake in Law has proven more challenging due to the important issues of reliability and transparency. In this study, we present a structured prompting methodology as a viable alternative to the often expensive fine-tuning, with the capability of tacking long legal documents from the CUAD dataset on the task of information retrieval. Each document is first split into chunks via a system of chunking and augmentation, addressing the long document problem. Then, alongside an engineered prompt, the input is fed into QWEN-2 to produce a set of answers for each question. Finally, we tackle the resulting candidate selection problem with the introduction of the Distribution-based Localisation and Inverse Cardinality Weighting heuristics. This approach leverages a general purpose model to promote long term scalability, prompt engineering to increase reliability and the two heuristic strategies to reduce the impact of the black box effect. Whilst our model performs up to 9\% better than the previously presented method, reaching state-of-the-art performance, it also highlights the limiting factor of current automatic evaluation metrics for question answering, serving as a call to action for future research. However, the chief aim of this work is to underscore the potential of structured prompt engineering as a useful, yet under-explored, tool in ensuring accountability and responsibility of AI in the legal domain, and beyond.
Leveraging Media Frames to Improve Normative Diversity in News Recommendations
Venue: arXiv cs.IR 2025
Year: 2025

Click-based news recommender systems suggest users content that aligns with their existing history, limiting the diversity of articles they encounter. Recent advances in aspect-based diversification -- adding features such as sentiments or news categories (e.g. world, politics) -- have made progress toward diversifying recommendations in terms of perspectives. However, these approaches often overlook the role of news framing, which shapes how stories are told by emphasizing specific angles or interpretations. In this paper, we treat media frames as a controllable aspect within the recommendation pipeline. By selecting articles based on a diversity of frames, our approach emphasizes varied narrative angles and broadens the interpretive space recommended to users. In addition to introducing frame-based diversification method, our work is the first to assess the impact of a news recommender system that integrates frame diversity using normative diversity metrics: representation, calibration, and activation. Our experiments based on media frame diversification show an improvement in exposure to previously unclicked frames up to 50%. This is important because repeated exposure to the same frames can reinforce existing biases or narrow interpretations, whereas introducing novel frames broadens users' understanding of issues and perspectives. The method also enhances diversification across categorical and sentiment levels, thereby demonstrating that framing acts as a strong control lever for enhancing normative diversity.
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images
Venue: arXiv cs.CV 2025
Year: 2025

Unstructured urban environments present unique challenges for scene understanding and generalization due to their complex and diverse layouts. We introduce SynthGenNet, a self-supervised student-teacher architecture designed to enable robust test-time domain generalization using synthetic multi-source imagery. Our contributions include the novel ClassMix++ algorithm, which blends labeled data from various synthetic sources while maintaining semantic integrity, enhancing model adaptability. We further employ Grounded Mask Consistency Loss (GMC), which leverages source ground truth to improve cross-domain prediction consistency and feature alignment. The Pseudo-Label Guided Contrastive Learning (PLGCL) mechanism is integrated into the student network to facilitate domain-invariant feature learning through iterative knowledge distillation from the teacher network. This self-supervised strategy improves prediction accuracy, addresses real-world variability, bridges the sim-to-real domain gap, and reliance on labeled target data, even in complex urban areas. Outcomes show our model outperforms the state-of-the-art (relying on single source) by achieving 50% Mean Intersection-Over-Union (mIoU) value on real-world datasets like Indian Driving Dataset (IDD).
Passwords and FIDO2 Are Meant To Be Secret: A Practical Secure Authentication Channel for Web Browsers
Venue: arXiv cs.CR 2025
Year: 2025

Password managers provide significant security benefits to users. However, malicious client-side scripts and browser extensions can steal passwords after the manager has autofilled them into the web page. In this paper, we extend prior work by Stock and Johns, showing how password autofill can be hardened to prevent these local attacks. We implement our design in the Firefox browser and conduct experiments demonstrating that our defense successfully protects passwords from XSS attacks and malicious extensions. We also show that our implementation is compatible with 97% of the Alexa top 1000 websites. Next, we generalize our design, creating a second defense that prevents recently discovered local attacks against the FIDO2 protocols. We implement this second defense into Firefox, demonstrating that it protects the FIDO2 protocol against XSS attacks and malicious extensions. This defense is compatible with all websites, though it does require a small change (2-3 lines) to web servers implementing FIDO2.
Exploring Diffusion Models for Generative Forecasting of Financial Charts
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in generative models have enabled significant progress in tasks such as generating and editing images from text, as well as creating videos from text prompts, and these methods are being applied across various fields. However, in the financial domain, there may still be a reliance on time-series data and a continued focus on transformer models, rather than on diverse applications of generative models. In this paper, we propose a novel approach that leverages text-to-image model by treating time-series data as a single image pattern, thereby enabling the prediction of stock price trends. Unlike prior methods that focus on learning and classifying chart patterns using architectures such as ResNet or ViT, we experiment with generating the next chart image from the current chart image and an instruction prompt using diffusion models. Furthermore, we introduce a simple method for evaluating the generated chart image against ground truth image. We highlight the potential of leveraging text-to-image generative models in the financial domain, and our findings motivate further research to address the current limitations and expand their applicability.
Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers
Venue: arXiv cs.SI 2025
Year: 2025

Most community detection approaches make very strong assumptions about communities in the data, such as every vertex must belong to exactly one community (the communities form a partition). For vector data, Hierarchical Density Based Spatial Clustering for Applications with Noise (HDBSCAN) has emerged as a leading clustering algorithm that allows for outlier points that do not belong to any cluster. The first step in HDBSCAN is to redefine the distance between vectors in such a way that single-linkage clustering is effective and robust to noise. Many community detection algorithms start with a similar step that attempts to increase the weight of edges between similar nodes and decrease weights of noisy edges. In this paper, we apply the hierarchical single-linkage clustering algorithm from HDBSCAN to a variety of node/edge similarity scores to see if there is an algorithm that can effectively detect clusters while allowing for outliers. In experiments on synthetic and real world data sets, we find that no single method is optimal for every type of graph, but the admirable performance indicates that hierarchical single-linkage clustering is a viable paradigm for graph clustering.
Explainability-Driven Dimensionality Reduction for Hyperspectral Imaging
Venue: arXiv cs.AI 2025
Year: 2025

Hyperspectral imaging (HSI) provides rich spectral information for precise material classification and analysis; however, its high dimensionality introduces a computational burden and redundancy, making dimensionality reduction essential. We present an exploratory study into the application of post-hoc explainability methods in a model--driven framework for band selection, which reduces the spectral dimension while preserving predictive performance. A trained classifier is probed with explanations to quantify each band's contribution to its decisions. We then perform deletion--insertion evaluations, recording confidence changes as ranked bands are removed or reintroduced, and aggregate these signals into influence scores. Selecting the highest--influence bands yields compact spectral subsets that maintain accuracy and improve efficiency. Experiments on two public benchmarks (Pavia University and Salinas) demonstrate that classifiers trained on as few as 30 selected bands match or exceed full--spectrum baselines while reducing computational requirements. The resulting subsets align with physically meaningful, highly discriminative wavelength regions, indicating that model--aligned, explanation-guided band selection is a principled route to effective dimensionality reduction for HSI.
Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Venue: arXiv cs.CV 2025
Year: 2025

Spatial understanding is essential for Multimodal Large Language Models (MLLMs) to support perception, reasoning, and planning in embodied environments. Despite recent progress, existing studies reveal that MLLMs still struggle with spatial understanding. However, existing research lacks a comprehensive and systematic evaluation of these limitations, often restricted to isolated scenarios, such as single-view or video. In this work, we present a systematic analysis of spatial understanding from both data and architectural perspectives across three representative scenarios: single-view, multi-view, and video. We propose a benchmark named MulSeT (Multi-view Spatial Understanding Tasks), and design a series of experiments to analyze the spatial reasoning capabilities of MLLMs. From the data perspective, the performance of spatial understanding converges quickly as the training data increases, and the upper bound is relatively low, especially for tasks that require spatial imagination. This indicates that merely expanding training data is insufficient to achieve satisfactory performance. From the architectural perspective, we find that spatial understanding relies more heavily on the positional encoding within the visual encoder than within the language model, in both cascaded and native MLLMs. Moreover, we explore reasoning injection and envision future improvements through architectural design to optimize spatial understanding. These insights shed light on the limitations of current MLLMs and suggest new directions for improving spatial reasoning capabilities through data scaling and architectural tuning.
Evaluating Cumulative Spectral Gradient as a Complexity Measure
Venue: arXiv cs.LG 2025
Year: 2025

Accurate estimation of dataset complexity is crucial for evaluating and comparing link prediction models for knowledge graphs (KGs). The Cumulative Spectral Gradient (CSG) metric derived from probabilistic divergence between classes within a spectral clustering framework was proposed as a dataset complexity measure that (1) naturally scales with the number of classes and (2) correlates strongly with downstream classification performance. In this work, we rigorously assess CSG behavior on standard knowledge graph link prediction benchmarks a multi class tail prediction task, using two key parameters governing its computation, M, the number of Monte Carlo sampled points per class, and K, the number of nearest neighbors in the embedding space. Contrary to the original claims, we find that (1) CSG is highly sensitive to the choice of K and therefore does not inherently scale with the number of target classes, and (2) CSG values exhibit weak or no correlation with established performance metrics such as mean reciprocal rank (MRR). Through experiments on FB15k 237, WN18RR, and other standard datasets, we demonstrate that CSG purported stability and generalization predictive power break down in link prediction settings. Our results highlight the need for more robust, classifier agnostic complexity measures in KG link prediction evaluation.