OpenRaiser/Intern-Atlas huggingface dataset:
From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes
Venue: arXiv cs.CV 2025
Year: 2025

3D visual grounding has made notable progress in localizing objects within complex 3D scenes. However, grounding referring expressions beyond objects in 3D scenes remains unexplored. In this paper, we introduce Anywhere3D-Bench, a holistic 3D visual grounding benchmark consisting of 2,886 referring expression-3D bounding box pairs spanning four different grounding levels: human-activity areas, unoccupied space beyond objects, individual objects in the scene, and fine-grained object parts. We assess a range of state-of-the-art 3D visual grounding methods alongside large language models (LLMs) and multimodal LLMs (MLLMs) on Anywhere3D-Bench. Experimental results reveal that space-level and part-level visual grounding pose the greatest challenges: space-level tasks require a more comprehensive spatial reasoning ability, for example, modeling distances and spatial relations within 3D space, while part-level tasks demand fine-grained perception of object composition. Even the best-performing models, Google Gemini-2.5-Pro and OpenAI o3, achieve just around 30% accuracy on space-level tasks and around 40% on part-level tasks, significantly lower than its performance on area-level and object-level tasks. These findings underscore a critical gap in current models' capacity to understand and reason about 3D scenes beyond object-level semantics.
Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion
Venue: arXiv cs.LG 2025
Year: 2025

Early identification of high-risk ICU patients is crucial for directing limited medical resources. We introduce ALFIA (Adaptive Layer Fusion with Intelligent Attention), a modular, attention-based architecture that jointly trains LoRA (Low-Rank Adaptation) adapters and an adaptive layer-weighting mechanism to fuse multi-layer semantic features from a BERT backbone. Trained on our rigorous cw-24 (CriticalWindow-24) benchmark, ALFIA surpasses state-of-the-art tabular classifiers in AUPRC while preserving a balanced precision-recall profile. The embeddings produced by ALFIA's fusion module, capturing both fine-grained clinical cues and high-level concepts, enable seamless pairing with GBDTs (CatBoost/LightGBM) as ALFIA-boost, and deep neuro networks as ALFIA-nn, yielding additional performance gains. Our experiments confirm ALFIA's superior early-warning performance, by operating directly on routine clinical text, it furnishes clinicians with a convenient yet robust tool for risk stratification and timely intervention in critical-care settings.
Light and 3D: a methodological exploration of digitisation techniques adapted to a selection of objects from the Mus{é}e d'Arch{é}ologie Nationale
Venue: arXiv cs.CV 2025
Year: 2025

The need to digitize heritage objects is now widely accepted. This article presents the very fashionable context of the creation of ''digital twins''. It illustrates the diversity of photographic 3D digitization methods, but this is not its only objective. Using a selection of objects from the collections of the mus{é}e d'Arch{é}ologie nationale, it shows that no single method is suitable for all cases. Rather, the method to be recommended for a given object should be the result of a concerted choice between those involved in heritage and those involved in the digital domain, as each new object may require the adaptation of existing tools. It would therefore be pointless to attempt an absolute classification of 3D digitization methods. On the contrary, we need to find the digital tool best suited to each object, taking into account not only its characteristics, but also the future use of its digital twin.
Nonlinear elastodynamic material identification of heterogeneous isogeometric Bernoulli-Euler beams
Venue: arXiv cs.CE 2025
Year: 2025

This paper presents a Finite Element Model Updating framework for identifying heterogeneous material distributions in planar Bernoulli-Euler beams based on a rotation-free isogeometric formulation. The procedure follows two steps: First, the elastic properties are identified from quasi-static displacements; then, the density is determined from modal data (low frequencies and mode shapes), given the previously obtained elastic properties. The identification relies on three independent discretizations: the isogeometric finite element mesh, a high-resolution grid of experimental measurements, and a material mesh composed of low-order Lagrange elements. The material mesh approximates the unknown material distributions, with its nodal values serving as design variables. The error between experiments and numerical model is expressed in a least squares manner. The objective is minimized using local optimization with the trust-region method, providing analytical derivatives to accelerate computations. Several numerical examples exhibiting large displacements are provided to test the proposed approach. To alleviate membrane locking, the B2M1 discretization is employed when necessary. Quasi-experimental data is generated using refined finite element models with random noise applied up to 4%. The method yields satisfactory results as long as a sufficient amount of experimental data is available, even for high measurement noise. Regularization is used to ensure a stable solution for dense material meshes. The density can be accurately reconstructed based on the previously identified elastic properties. The proposed framework can be straightforwardly extended to shells and 3D continua.
PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages
Venue: arXiv cs.CR 2025
Year: 2025

Security vulnerabilities in software packages are a significant concern for developers and users alike. Patching these vulnerabilities in a timely manner is crucial to restoring the integrity and security of software systems. However, previous work has shown that vulnerability reports often lack proof-of-concept (PoC) exploits, which are essential for fixing the vulnerability, testing patches, and avoiding regressions. Creating a PoC exploit is challenging because vulnerability reports are informal and often incomplete, and because it requires a detailed understanding of how inputs passed to potentially vulnerable APIs may reach security-relevant sinks. In this paper, we present PoCGen, a novel approach to autonomously generate and validate PoC exploits for vulnerabilities in npm packages. The approach is the first to address this task by combining the complementary strengths of large language models (LLMs), e.g., to understand informal vulnerability reports, with static analysis, e.g., to identify taint paths, and dynamic analysis, e.g., to validate generated exploits. PoCGen successfully generates exploits for 77% of the vulnerabilities in the SecBench$.$js dataset. This success rate significantly outperforms a recent baseline (by 45 absolute percentage points), while imposing an average cost of only $0.02 per generated exploit. Moreover, PoCGen generates six successful exploits for recent real-world vulnerabilities, five of which are now included in their respective vulnerability reports.
From Struggle (06-2024) to Mastery (02-2025) LLMs Conquer Advanced Algorithm Exams and Pave the Way for Editorial Generation
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents a comprehensive evaluation of the performance of state-of-the-art Large Language Models (LLMs) on challenging university-level algorithms exams. By testing multiple models on both a Romanian exam and its high-quality English translation, we analyze LLMs' problem-solving capabilities, consistency, and multilingual performance. Our empirical study reveals that the most recent models not only achieve scores comparable to top-performing students but also demonstrate robust reasoning skills on complex, multi-step algorithmic challenges, even though difficulties remain with graph-based tasks. Building on these findings, we explore the potential of LLMs to support educational environments through the generation of high-quality editorial content, offering instructors a powerful tool to enhance student feedback. The insights and best practices discussed herein pave the way for further integration of generative AI in advanced algorithm education.
Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings
Venue: arXiv cs.IR 2025
Year: 2025

Despite the strong performance of ColPali/ColQwen2 in Visualized Document Retrieval (VDR), it encodes each page into multiple patch-level embeddings and leads to excessive memory usage. This empirical study investigates methods to reduce patch embeddings per page at minimum performance degradation. We evaluate two token-reduction strategies: token pruning and token merging. Regarding token pruning, we surprisingly observe that a simple random strategy outperforms other sophisticated pruning methods, though still far from satisfactory. Further analysis reveals that pruning is inherently unsuitable for VDR as it requires removing certain page embeddings without query-specific information. Turning to token merging (more suitable for VDR), we search for the optimal combinations of merging strategy across three dimensions and develop Light-ColPali/ColQwen2. It maintains 98.2% of retrieval performance with only 11.8% of original memory usage, and preserves 94.6% effectiveness at 2.8% memory footprint. We expect our empirical findings and resulting Light-ColPali/ColQwen2 offer valuable insights and establish a competitive baseline for future research towards efficient VDR.
A Unified Framework for Simulating Strongly-Coupled Fluid-Robot Multiphysics
Venue: arXiv cs.RO 2025
Year: 2025

We present a framework for simulating fluid-robot multiphysics as a single, unified optimization problem. The coupled manipulator and incompressible Navier-Stokes equations governing the robot and fluid dynamics are derived together from a single Lagrangian using the principal of least action. We then employ discrete variational mechanics to derive a stable, implicit time-integration scheme for jointly simulating both the fluid and robot dynamics, which are tightly coupled by a constraint that enforces the no-slip boundary condition at the fluid-robot interface. Extending the classical immersed boundary method, we derive a new formulation of the no-slip constraint that is numerically well-conditioned and physically accurate for multibody systems commonly found in robotics. We demonstrate our approach's physical accuracy on benchmark computational fluid-dynamics problems, including Poiseuille flow and a disc in free stream. We then design a locomotion policy for a novel swimming robot in simulation and validate results on real-world hardware, showcasing our framework's sim-to-real capability for robotics tasks.
Towards Reasonable Concept Bottleneck Models
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we propose $\textbf{C}$oncept $\textbf{REA}$soning $\textbf{M}$odels (CREAM), a novel family of Concept Bottleneck Models (CBMs) that: (i) explicitly encodes concept-concept (${\texttt{C-C}}$) and concept-task (${\texttt{C$\rightarrow$Y}}$) relationships to enforce a desired model reasoning; and (ii) use a regularized side-channel to achieve competitive task performance, while keeping high concept importance. Specifically, CREAM architecturally embeds (bi)directed concept-concept, and concept to task relationships specified by a human expert, while severing undesired information flows (e.g., to handle mutually exclusive concepts). Moreover, CREAM integrates a black-box side-channel that is regularized to encourage task predictions to be grounded in the relevant concepts, thereby utilizing the side-channel only when necessary to enhance performance. Our experiments show that: (i) CREAM mainly relies on concepts while achieving task performance on par with black-box models; and (ii) the embedded ${\texttt{C-C}}$ and ${\texttt{C$\rightarrow$Y}}$ relationships ease model interventions and mitigate concept leakage.
Rethinking Contrastive Learning in Session-based Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that existing contrastive learning based methods still suffer from three obstacles: (1) they overlook item-level sparsity and primarily focus on session-level sparsity; (2) they typically augment sessions using item IDs like crop, mask and reorder, failing to ensure the semantic consistency of augmented views; (3) they treat all positive-negative signals equally, without considering their varying utility. To this end, we propose a novel multi-modal adaptive contrastive learning framework called MACL for session-based recommendation. In MACL, a multi-modal augmentation is devised to generate semantically consistent views at both item and session levels by leveraging item multi-modal features. Besides, we present an adaptive contrastive loss that distinguishes varying contributions of positive-negative signals to improve self-supervised learning. Extensive experiments on three real-world datasets demonstrate the superiority of MACL over state-of-the-art methods.
FlowDirector: Training-Free Flow Steering for Precise Text-to-Video Editing
Venue: arXiv cs.CV 2025
Year: 2025

Text-driven video editing aims to modify video content based on natural language instructions. While recent training-free methods have leveraged pretrained diffusion models, they often rely on an inversion-editing paradigm. This paradigm maps the video to a latent space before editing. However, the inversion process is not perfectly accurate, often compromising appearance fidelity and motion consistency. To address this, we introduce FlowDirector, a novel training-free and inversion-free video editing framework. Our framework models the editing process as a direct evolution in the data space. It guides the video to transition smoothly along its inherent spatio-temporal manifold using an ordinary differential equation (ODE), thereby avoiding the inaccurate inversion step. From this foundation, we introduce three flow correction strategies for appearance, motion, and stability: 1) Direction-aware flow correction amplifies components that oppose the source direction and removes irrelevant terms, breaking conservative streamlines and enabling stronger structural and textural changes. 2) Motion-appearance decoupling optimizes motion agreement as an energy term at each timestep, significantly improving consistency and motion transfer. 3) Differential averaging guidance strategy leverages differences among multiple candidate flows to approximate a low variance regime at low cost, suppressing artifacts and stabilizing the trajectory. Extensive experiments across various editing tasks and benchmarks demonstrate that FlowDirector achieves state-of-the-art performance in instruction following, temporal consistency, and background preservation, establishing an efficient new paradigm for coherent video editing without inversion.
TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) excel in high-resource languages but struggle with low-resource languages due to limited training data. This paper presents TALL (Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages), which integrates an LLM with two bilingual translation models. TALL transforms low-resource inputs into high-resource representations, leveraging the LLM's capabilities while preserving linguistic features through dimension alignment layers and custom transformers. Our experiments on Hebrew demonstrate significant improvements over several baselines, including direct use, naive translation, and fine-tuning approaches. The architecture employs a parameter-efficient strategy, freezing pre-trained components while training only lightweight adapter modules, balancing computational efficiency with performance gains.
A Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions
Venue: arXiv cs.CV 2025
Year: 2025

Vietnamese document analysis and recognition (DAR) is a crucial field with applications in digitization, information retrieval, and automation. Despite advancements in OCR and NLP, Vietnamese text recognition faces unique challenges due to its complex diacritics, tonal variations, and lack of large-scale annotated datasets. Traditional OCR methods often struggle with real-world document variations, while deep learning approaches have shown promise but remain limited by data scarcity and generalization issues. Recently, large language models (LLMs) and vision-language models have demonstrated remarkable improvements in text recognition and document understanding, offering a new direction for Vietnamese DAR. However, challenges such as domain adaptation, multimodal learning, and computational efficiency persist. This survey provide a comprehensive review of existing techniques in Vietnamese document recognition, highlights key limitations, and explores how LLMs can revolutionize the field. We discuss future research directions, including dataset development, model optimization, and the integration of multimodal approaches for improved document intelligence. By addressing these gaps, we aim to foster advancements in Vietnamese DAR and encourage community-driven solutions.
Does It Make Sense to Speak of Introspection in Large Language Models?
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) exhibit compelling linguistic behaviour, and sometimes offer self-reports, that is to say statements about their own nature, inner workings, or behaviour. In humans, such reports are often attributed to a faculty of introspection and are typically linked to consciousness. This raises the question of how to interpret self-reports produced by LLMs, given their increasing linguistic fluency and cognitive capabilities. To what extent (if any) can the concept of introspection be meaningfully applied to LLMs? Here, we present and critique two examples of apparent introspective self-report from LLMs. In the first example, an LLM attempts to describe the process behind its own "creative" writing, and we argue this is not a valid example of introspection. In the second example, an LLM correctly infers the value of its own temperature parameter, and we argue that this can be legitimately considered a minimal example of introspection, albeit one that is (presumably) not accompanied by conscious experience.
RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) possess strong multilingual capabilities, and combining Reinforcement Learning from Human Feedback (RLHF) with translation tasks has shown great potential. However, we observe that this paradigm performs unexpectedly poorly when applied to colloquial subtitle translation tasks. In this work, we investigate this issue and find that the offline reward model (RM) gradually diverges from the online LLM due to distributional shift, ultimately leading to undesirable training outcomes. To address this, we propose RIVAL, an adversarial training framework that formulates the process as a min-max game between the RM and the LLM. RIVAL iteratively updates the both models, with the RM trained to distinguish strong from weak translations (qualitative preference reward), and the LLM trained to enhance its translation for closing this gap. To stabilize training and improve generalizability, we also incorporate quantitative preference reward (e.g., BLEU) into the RM, enabling reference-free quality modeling aligned with human evaluation. Through extensive experiments, we demonstrate that the proposed adversarial training framework significantly improves upon translation baselines.
Semi-Implicit Variational Inference via Kernelized Path Gradient Descent
Venue: arXiv cs.LG 2025
Year: 2025

Semi-implicit variational inference (SIVI) is a powerful framework for approximating complex posterior distributions, but training with the Kullback-Leibler (KL) divergence can be challenging due to high variance and bias in high-dimensional settings. While current state-of-the-art semi-implicit variational inference methods, particularly Kernel Semi-Implicit Variational Inference (KSIVI), have been shown to work in high dimensions, training remains moderately expensive. In this work, we propose a kernelized KL divergence estimator that stabilizes training through nonparametric smoothing. To further reduce the bias, we introduce an importance sampling correction. We provide a theoretical connection to the amortized version of the Stein variational gradient descent, which estimates the score gradient via Stein's identity, showing that both methods minimize the same objective, but our semi-implicit approach achieves lower gradient variance. In addition, our method's bias in function space is benign, leading to more stable and efficient optimization. Empirical results demonstrate that our method outperforms or matches state-of-the-art SIVI methods in both performance and training efficiency.
OpenCCA: An Open Framework to Enable Arm CCA Research
Venue: arXiv cs.CR 2025
Year: 2025

Confidential computing has gained traction across major architectures with Intel TDX, AMD SEV-SNP, and Arm CCA. Unlike TDX and SEV-SNP, a key challenge in researching Arm CCA is the absence of hardware support, forcing researchers to develop ad-hoc performance prototypes on non-CCA Arm boards. This approach leads to duplicated efforts, inconsistent performance comparisons, and high barriers to entry. To address this, we present OpenCCA, an open research platform that enables the execution of CCA-bound code on commodity Armv8.2 hardware. By systematically adapting the software stack -- including bootloader, firmware, hypervisor, and kernel -- OpenCCA emulates CCA operations for performance evaluation while preserving functional correctness. We demonstrate its effectiveness with typical life-cycle measurements and case-studies inspired by prior CCA-based papers on a easily available Armv8.2 Rockchip board that costs $250.
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems
Venue: arXiv cs.LG 2025
Year: 2025

Recently, federated learning frameworks such as Python TestBed for Federated Learning Algorithms and MicroPython TestBed for Federated Learning Algorithms have emerged to tackle user privacy concerns and efficiency in embedded systems. Even more recently, an efficient federated anomaly detection algorithm, FLiForest, based on Isolation Forests has been developed, offering a low-resource, unsupervised method well-suited for edge deployment and continuous learning. In this paper, we present an application of Isolation Forest-based temperature anomaly detection, developed using the previously mentioned federated learning frameworks, aimed at small edge devices and IoT systems running MicroPython. The system has been experimentally evaluated, achieving over 96% accuracy in distinguishing normal from abnormal readings and above 78% precision in detecting anomalies across all tested configurations, while maintaining a memory usage below 160 KB during model training. These results highlight its suitability for resource-constrained environments and edge systems, while upholding federated learning principles of data privacy and collaborative learning.