OpenRaiser/Intern-Atlas huggingface dataset:
FuXi-$γ$: Efficient Sequential Recommendation with Exponential-Power Temporal Encoder and Diagonal-Sparse Positional Mechanism
Venue: arXiv cs.IR 2025
Year: 2025

Sequential recommendation aims to model users' evolving preferences based on their historical interactions. Recent advances leverage Transformer-based architectures to capture global dependencies, but existing methods often suffer from high computational overhead, primarily due to discontinuous memory access in temporal encoding and dense attention over long sequences. To address these limitations, we propose FuXi-$γ$, a novel sequential recommendation framework that improves both effectiveness and efficiency through principled architectural design. FuXi-$γ$ adopts a decoder-only Transformer structure and introduces two key innovations: (1) An exponential-power temporal encoder that encodes relative temporal intervals using a tunable exponential decay function inspired by the Ebbinghaus forgetting curve. This encoder enables flexible modeling of both short-term and long-term preferences while maintaining high efficiency through continuous memory access and pure matrix operations. (2) A diagonal-sparse positional mechanism that prunes low-contribution attention blocks using a diagonal-sliding strategy guided by the persymmetry of Toeplitz matrix. Extensive experiments on four real-world datasets demonstrate that FuXi-$γ$ achieves state-of-the-art performance in recommendation quality, while accelerating training by up to 4.74$\times$ and inference by up to 6.18$\times$, making it a practical and scalable solution for long-sequence recommendation. Our code is available at https://github.com/Yeedzhi/FuXi-gamma.
From Information Freshness to Semantics of Information and Goal-oriented Communications
Venue: arXiv cs.IT 2025
Year: 2025

Future wireless networks must support real-time, data-driven cyber-physical systems in which communication is tightly coupled with sensing, inference, control, and decision-making. Traditional communication paradigms centered on accuracy, throughput, and latency are increasingly inadequate for these systems, where the value of information depends on its semantic relevance to a specific task. This paper provides a unified exposition of the progression from classical distortion-based frameworks, through information freshness metrics such as the Age of Information (AoI) and its variants, to the emerging paradigm of goal-oriented semantics-aware communication. We organize and systematize existing semantics-aware metrics, including content- and version-aware measures, context-dependent distortion formulations, and history-dependent error persistence metrics that capture lasting impact and urgency. Within this framework, we highlight how these metrics address the limitations of purely accuracy- or freshness-centric designs, and how they collectively enable the selective generation and transmission of only task-relevant information. We further review analytical tools based on Markov decision process (MDP) and Lyapunov optimization methods that have been employed to characterize optimal or near-optimal timing and scheduling policies under semantic performance criteria and communication constraints. By synthesizing these developments into a coherent framework, the paper clarifies the design principles underlying goal-oriented, semantics-aware communication systems. It illustrates how they can significantly improve efficiency, reliability, and task performance. The presented perspective aims to serve as a bridge between information-theoretic, control-theoretic, and networking viewpoints, and to guide the design of semantic communication architectures for 6G and beyond.
Federated Learning with Feedback Alignment
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) enables collaborative training across multiple clients while preserving data privacy, yet it struggles with data heterogeneity, where clients' data are not distributed independently and identically (non-IID). This causes local drift, hindering global model convergence. To address this, we introduce Federated Learning with Feedback Alignment (FLFA), a novel framework that integrates feedback alignment into FL. FLFA uses the global model's weights as a shared feedback matrix during local training's backward pass, aligning local updates with the global model efficiently. This approach mitigates local drift with minimal additional computational cost and no extra communication overhead. Our theoretical analysis supports FLFA's design by showing how it alleviates local drift and demonstrates robust convergence for both local and global models. Empirical evaluations, including accuracy comparisons and measurements of local drift, further illustrate that FLFA can enhance other FL methods demonstrating its effectiveness.
Unveiling Statistical Significance of Online Regression over Multiple Datasets
Venue: arXiv cs.LG 2025
Year: 2025

Despite extensive focus on techniques for evaluating the performance of two learning algorithms on a single dataset, the critical challenge of developing statistical tests to compare multiple algorithms across various datasets has been largely overlooked in most machine learning research. Additionally, in the realm of Online Learning, ensuring statistical significance is essential to validate continuous learning processes, particularly for achieving rapid convergence and effectively managing concept drifts in a timely manner. Robust statistical methods are needed to assess the significance of performance differences as data evolves over time. This article examines the state-of-the-art online regression models and empirically evaluates several suitable tests. To compare multiple online regression models across various datasets, we employed the Friedman test along with corresponding post-hoc tests. For thorough evaluations, utilizing both real and synthetic datasets with 5-fold cross-validation and seed averaging ensures comprehensive assessment across various data subsets. Our tests generally confirmed the performance of competitive baselines as consistent with their individual reports. However, some statistical test results also indicate that there is still room for improvement in certain aspects of state-of-the-art methods.
Temporal HAL-API Dependencies as a Gateway to Formal Embedded Software Development
Venue: arXiv cs.SE 2025
Year: 2025

Temporal HAL-API Dependencies (THADs) can be useful to capture an interesting class of correctness properties in embedded software development. They demand a moderate effort for specification (which can be done via program annotations) and verification (which can be done automatically via software model checking). In this sense, they have the potential to form an interesting sweet spot between generic properties (that demand virtually no specification effort, and that are typically addressed by static analysis) and application-specific properties as addressed by full-fledged formal methods. Thus, they may form a gateway to wider and more economic use of formal methods in industrial embedded software development.
Liquid Reasoning Transformers: A Sudoku-Based Prototype for Chess-Scale Algorithmic Tasks
Venue: arXiv cs.LG 2025
Year: 2025

The Liquid Reasoning Transformer (LRT) is a transformer architecture designed for inference with adaptive depths using iterative changes, discard-based correction, and a learned stopping mechanism. Instead of relying on a single feedforward pass, the model updates a recurrent reasoning token across multiple internal steps, allowing it to correct early errors and allocate computation based on input difficulty. We evaluate the LRT on Sudoku as a controlled testbed for structured reasoning and show that it achieves strong performance, reaching 98.68% digit accuracy and 36.30% full-puzzle accuracy without using symbolic rules or search. Analyzing internal patterns shows that the discard and stop gates play different, important roles in stabilizing inferences and adjusting computational depth. We discuss how these mechanisms extend naturally to chess-scale reasoning tasks and outline extensions for multi-token reasoning and larger domains.
DrivePI: Spatial-aware 4D MLLM for Unified Autonomous Driving Understanding, Perception, Prediction and Planning
Venue: arXiv cs.CV 2025
Year: 2025

Although multi-modal large language models (MLLMs) have shown strong capabilities across diverse domains, their application in generating fine-grained 3D perception and prediction outputs in autonomous driving remains underexplored. In this paper, we propose DrivePI, a novel spatial-aware 4D MLLM that serves as a unified Vision-Language-Action (VLA) framework that is also compatible with vision-action (VA) models. Our method jointly performs spatial understanding, 3D perception (i.e., 3D occupancy), prediction (i.e., occupancy flow), and planning (i.e., action outputs) in parallel through end-to-end optimization. To obtain both precise geometric information and rich visual appearance, our approach integrates point clouds, multi-view images, and language instructions within a unified MLLM architecture. We further develop a data engine to generate text-occupancy and text-flow QA pairs for 4D spatial understanding. Remarkably, with only a 0.5B Qwen2.5 model as MLLM backbone, DrivePI as a single unified model matches or exceeds both existing VLA models and specialized VA models. Specifically, compared to VLA models, DrivePI outperforms OpenDriveVLA-7B by 2.5% mean accuracy on nuScenes-QA and reduces collision rate by 70% over ORION (from 0.37% to 0.11%) on nuScenes. Against specialized VA models, DrivePI surpasses FB-OCC by 10.3 RayIoU for 3D occupancy on OpenOcc, reduces the mAVE from 0.591 to 0.509 for occupancy flow on OpenOcc, and achieves 32% lower L2 error than VAD (from 0.72m to 0.49m) for planning on nuScenes. Code will be available at https://github.com/happinesslz/DrivePI
Fine-Grained Energy Prediction For Parallellized LLM Inference With PIE-P
Venue: arXiv cs.DC 2025
Year: 2025

With the widespread adoption of Large Language Models (LLMs), energy costs of running LLMs is quickly becoming a critical concern. However, precisely measuring the energy consumption of LLMs is often infeasible because hardware-based power monitors are not always accessible and software-based energy measurement tools are not accurate. While various prediction techniques have been developed to estimate LLM energy consumption, these approaches are limited to single-GPU environments and thus are not applicable to modern LLM inference which is typically parallelized across multiple GPUs. In this work, we remedy this gap and introduce PIE-P, a fine-grained energy prediction framework for multi-GPU inference, including tensor, pipeline, and data parallelism. Predicting the energy under parallelized inference is complicated by the non-determinism in inter-GPU communication, additional communication overheads, and difficulties in isolating energy during the communication/synchronization phase. We develop a scalable prediction framework that addresses these issues via precise sampling, fine-grained modeling of inter-GPU communication, and careful accounting of parallelization overhead. Our evaluation results show that PIE-P yields accurate and fine-grained energy predictions across parallelism strategies, significantly outperforming baselines.
Fault-Tolerant Sandboxing for AI Coding Agents: A Transactional Approach to Safe Autonomous Execution
Venue: arXiv cs.AI 2025
Year: 2025

The transition of Large Language Models (LLMs) from passive code generators to autonomous agents introduces significant safety risks, specifically regarding destructive commands and inconsistent system states. Existing commercial solutions often prioritize interactive user safety, enforcing authentication barriers that break the headless loops required for true autonomy. This paper presents a Fault-Tolerant Sandboxing framework designed to mitigate these risks through a policy-based interception layer and a transactional filesystem snapshot mechanism. We hypothesize that wrapping agent actions in atomic transactions can guarantee safety with acceptable latency, outperforming the heavy initialization overhead of containers or the interactive friction of commercial CLIs. We validated this approach by deploying the Minimind-MoE LLM served via nano-vllm on a custom Proxmox-based testbed utilizing EVPN/VXLAN isolation. Experimental results demonstrate a 100\% interception rate for high-risk commands and a 100\% success rate in rolling back failed states. Crucially, our prototype incurs only a 14.5\% performance overhead (approx. 1.8s) per transaction. In contrast, benchmarking against the Gemini CLI sandbox revealed that it requires interactive authentication ("Sign in"), rendering it unusable for headless, autonomous agent workflows.
OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization
Venue: arXiv cs.NE 2025
Year: 2025

Black-box optimization often relies on evolutionary and swarm algorithms whose performance is highly problem dependent. We view an optimizer as a short program over a small vocabulary of search operators and learn this operator program separately for each problem instance. We instantiate this idea in Operator-Programmed Algorithms (OPAL), a landscape-aware framework for continuous black-box optimization that uses a small design budget with a standard differential evolution baseline to probe the landscape, builds a $k$-nearest neighbor graph over sampled points, and encodes this trajectory with a graph neural network. A meta-learner then maps the resulting representation to a phase-wise schedule of exploration, restart, and local search operators. On the CEC~2017 test suite, a single meta-trained OPAL policy is statistically competitive with state-of-the-art adaptive differential evolution variants and achieves significant improvements over simpler baselines under nonparametric tests. Ablation studies on CEC~2017 justify the choices for the design phase, the trajectory graph, and the operator-program representation, while the meta-components add only modest wall-clock overhead. Overall, the results indicate that operator-programmed, landscape-aware per-instance design is a practical way forward beyond ad hoc metaphor-based algorithms in black-box optimization.
Adapting Multimodal Foundation Models for Few-Shot Learning: A Comprehensive Study on Contrastive Captioners
Venue: arXiv cs.CV 2025
Year: 2025

Large-scale multimodal foundation models, particularly Contrastive Captioners (CoCa), have achieved state-of-the-art results by unifying contrastive alignment with generative captioning. While zero-shot transfer capabilities are well-documented, the adaptation of these generative-contrastive hybrids to downstream tasks with extreme data scarcity (few-shot learning) remains under-explored. Existing literature predominantly focuses on dual-encoder architectures like CLIP, leaving a gap in understanding how CoCa's distinct latent space responds to parameter-efficient fine-tuning (PEFT). This paper presents a comprehensive empirical study on adapting the CoCa visual backbone for few-shot image classification. We systematically evaluate a hierarchy of strategies, ranging from training-free hybrid prototyping to deep parameter adaptation via Low-Rank Adaptation (LoRA). First, we identify an "augmentation divergence": while strong data augmentation degrades the performance of linear probing in low-shot settings, it is essential for stabilizing LoRA fine-tuning. We also demonstrate that hybrid objectives incorporating Supervised Contrastive (SupCon) loss yield consistent performance improvements over standard Cross-Entropy across varying shot counts. Crucially, we characterize the sensitivity of training configurations to data scarcity, providing empirical reference settings for scaling regularization, rank, and sampling strategies to facilitate the efficient adaptation of generative-contrastive foundation models.
Optimal Labeler Assignment and Sampling for Active Learning in the Presence of Imperfect Labels
Venue: arXiv cs.LG 2025
Year: 2025

Active Learning (AL) has garnered significant interest across various application domains where labeling training data is costly. AL provides a framework that helps practitioners query informative samples for annotation by oracles (labelers). However, these labels often contain noise due to varying levels of labeler accuracy. Additionally, uncertain samples are more prone to receiving incorrect labels because of their complexity. Learning from imperfectly labeled data leads to an inaccurate classifier. We propose a novel AL framework to construct a robust classification model by minimizing noise levels. Our approach includes an assignment model that optimally assigns query points to labelers, aiming to minimize the maximum possible noise within each cycle. Additionally, we introduce a new sampling method to identify the best query points, reducing the impact of label noise on classifier performance. Our experiments demonstrate that our approach significantly improves classification performance compared to several benchmark methods.
Improving Recursive Transformers with Mixture of LoRAs
Venue: arXiv cs.LG 2025
Year: 2025

Parameter sharing in recursive transformers reduces model size but collapses layer-wise expressivity. We propose Mixture of LoRAs (MoL), a lightweight conditional-computation mechanism that inserts Low-Rank Adaptation (LoRA) experts inside a shared feed-forward network (FFN). MoL enables token-conditional weight-space modulation of the shared FFN without untying backbone parameters, unlike prior approaches that add fixed or externally attached adapters. We pretrain a modernised recursive architecture, ModernALBERT, integrating rotary embeddings, GeGLU, FlashAttention, and a distillation-based initialisation. Across GLUE, SQuAD-v2, and BEIR, ModernALBERT (50M--120M) achieves state-of-the-art performance among compact models and surpasses larger fully parameterised baselines. We also propose an expert-merging procedure that compresses MoL into a single adapter at inference while preserving accuracy, enabling efficient deployment. Our results show that conditional weight-space modulation effectively restores the expressivity lost under aggressive parameter sharing in recursive transformers.
Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for scaling to new tasks, yet current research suffers from three critical pitfalls: data-regime bias, suboptimal adaptation, and insufficient task coverage. In this paper, we address these pitfalls and introduce AnyMC3D, a scalable 3D classifier adapted from 2D FMs. Our method scales efficiently to new tasks by adding only lightweight plugins (about 1M parameters per task) on top of a single frozen backbone. This versatile framework also supports multi-view inputs, auxiliary pixel-level supervision, and interpretable heatmap generation. We establish a comprehensive benchmark of 12 tasks covering diverse pathologies, anatomies, and modalities, and systematically analyze state-of-the-art 3D classification techniques. Our analysis reveals key insights: (1) effective adaptation is essential to unlock FM potential, (2) general-purpose FMs can match medical-specific FMs if properly adapted, and (3) 2D-based methods surpass 3D architectures for 3D classification. For the first time, we demonstrate the feasibility of achieving state-of-the-art performance across diverse applications using a single scalable framework (including 1st place in the VLM3D challenge), eliminating the need for separate task-specific models.
Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents a method to predict the evolution of a complex traffic scenario with multiple objects. The current state of the scenario is assumed to be known from sensors and the prediction is taking into account various hypotheses about the behavior of traffic participants. This way, the uncertainties regarding the behavior of traffic participants can be modelled in detail. In the first part of this paper a model-based approach is presented to compute Predicted-Occupancy Grids (POG), which are introduced as a grid-based probabilistic representation of the future scenario hypotheses. However, due to the large number of possible trajectories for each traffic participant, the model-based approach comes with a very high computational load. Thus, a machine-learning approach is adopted for the computation of POGs. This work uses a novel grid-based representation of the current state of the traffic scenario and performs the mapping to POGs. This representation consists of augmented cells in an occupancy grid. The adopted machine-learning approach is based on the Random Forest algorithm. Simulations of traffic scenarios are performed to compare the machine-learning with the model-based approach. The results are promising and could enable the real-time computation of POGs for vehicle safety applications. With this detailed modelling of uncertainties, crucial components in vehicle safety systems like criticality estimation and trajectory planning can be improved.
Content Adaptive based Motion Alignment Framework for Learned Video Compression
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in end-to-end video compression have shown promising results owing to their unified end-to-end learning optimization. However, such generalized frameworks often lack content-specific adaptation, leading to suboptimal compression performance. To address this, this paper proposes a content adaptive based motion alignment framework that improves performance by adapting encoding strategies to diverse content characteristics. Specifically, we first introduce a two-stage flow-guided deformable warping mechanism that refines motion compensation with coarse-to-fine offset prediction and mask modulation, enabling precise feature alignment. Second, we propose a multi-reference quality aware strategy that adjusts distortion weights based on reference quality, and applies it to hierarchical training to reduce error propagation. Third, we integrate a training-free module that downsamples frames by motion magnitude and resolution to obtain smooth motion estimation. Experimental results on standard test datasets demonstrate that our framework CAMA achieves significant improvements over state-of-the-art Neural Video Compression models, achieving a 24.95% BD-rate (PSNR) savings over our baseline model DCVC-TCM, while also outperforming reproduced DCVC-DC and traditional codec HM-16.25.
SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces SLIM-VDB, a new lightweight semantic mapping system with probabilistic semantic fusion for closed-set or open-set dictionaries. Advances in data structures from the computer graphics community, such as OpenVDB, have demonstrated significantly improved computational and memory efficiency in volumetric scene representation. Although OpenVDB has been used for geometric mapping in robotics applications, semantic mapping for scene understanding with OpenVDB remains unexplored. In addition, existing semantic mapping systems lack support for integrating both fixed-category and open-language label predictions within a single framework. In this paper, we propose a novel 3D semantic mapping system that leverages the OpenVDB data structure and integrates a unified Bayesian update framework for both closed- and open-set semantic fusion. Our proposed framework, SLIM-VDB, achieves significant reduction in both memory and integration times compared to current state-of-the-art semantic mapping approaches, while maintaining comparable mapping accuracy. An open-source C++ codebase with a Python interface is available at https://github.com/umfieldrobotics/slim-vdb.