OpenRaiser/Intern-Atlas huggingface dataset:
FPGA-Based Multiplier with a New Approximate Full Adder for Error-Resilient Applications
Venue: arXiv cs.AR 2025
Year: 2025

Electronic devices primarily aim to offer low power consumption, high speed, and a compact area. The performance of very large-scale integration (VLSI) devices is influenced by arithmetic operations, where multiplication is a crucial operation. Therefore, a high-speed multiplier is essential for developing any signal-processing module. Numerous multipliers have been reviewed in existing literature, and their speed is largely determined by how partial products (PPs) are accumulated. To enhance the speed of multiplication beyond current methods, an approximate adder-based multiplier is introduced. This approach allows for the simultaneous addition of PPs from two consecutive bits using a novel approximate adder. The proposed multiplier is utilized in a mean filter structure and implemented in ISE Design Suite 14.7 using VHDL and synthesized on the Xilinx Spartan3-XC3S400 FPGA board. Compared to the literature, the proposed multiplier achieves power and power-delay product (PDP) improvements of 56.09% and 73.02%, respectively. The validity of the expressed multiplier is demonstrated through the mean filter system. Results show that it achieves power savings of 33.33%. Additionally, the proposed multiplier provides more accurate results than other approximate multipliers by expressing higher values of peak signal-to-noise ratio (PSNR), (30.58%), and structural similarity index metric (SSIM), (22.22%), while power consumption is in a low range.
Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
Venue: arXiv cs.NE 2025
Year: 2025

Spiking Neural Networks (SNNs) and neuromorphic computing present a promising alternative to traditional Artificial Neural Networks (ANNs) by significantly improving energy efficiency, particularly in edge and implantable devices. However, assessing the energy performance of SNN models remains a challenge due to the lack of standardized and actionable metrics and the difficulty of measuring energy consumption in experimental neuromorphic hardware. In this paper, we conduct a preliminary exploratory study of energy efficiency metrics proposed in the SNN benchmarking literature. We classify 13 commonly used metrics based on four key properties: Accessibility, Fidelity, Actionability, and Trend-Based analysis. Our findings indicate that while many existing metrics provide useful comparisons between architectures, they often lack practical insights for SNN developers. Notably, we identify a gap between accessible and high-fidelity metrics, limiting early-stage energy assessment. Additionally, we emphasize the lack of metrics that provide practitioners with actionable insights, making it difficult to guide energy-efficient SNN development. To address these challenges, we outline research directions for bridging accessibility and fidelity and finding new Actionable metrics for implantable neuromorphic devices, introducing more Trend-Based metrics, metrics that reflect changes in power requirements, battery-aware metrics, and improving energy-performance tradeoff assessments. The results from this paper pave the way for future research on enhancing energy metrics and their Actionability for SNNs.
Team Anotheroption at SemEval-2025 Task 8: Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents a system developed for SemEval 2025 Task 8: Question Answering (QA) over tabular data. Our approach integrates several key components: text-to-SQL and text-to-code generation modules, a self-correction mechanism, and a retrieval-augmented generation (RAG). Additionally, it includes an end-to-end (E2E) module, all orchestrated by a large language model (LLM). Through ablation studies, we analyzed the effects of different parts of our pipeline and identified the challenges that are still present in this field. During the evaluation phase of the competition, our solution achieved an accuracy of 80%, resulting in a top-13 ranking among the 38 participating teams. Our pipeline demonstrates a significant improvement in accuracy for open-source models and achieves a performance comparable to proprietary LLMs in QA tasks over tables. The code is available at GitHub repository.
Is Fine-Tuning an Effective Solution? Reassessing Knowledge Editing for Unstructured Data
Venue: arXiv cs.CL 2025
Year: 2025

Unstructured Knowledge Editing (UKE) is crucial for updating the relevant knowledge of large language models (LLMs). It focuses on unstructured inputs, such as long or free-form texts, which are common forms of real-world knowledge. Although previous studies have proposed effective methods and tested them, some issues exist: (1) Lack of Locality evaluation for UKE, and (2) Abnormal failure of fine-tuning (FT) based methods for UKE. To address these issues, we first construct two datasets, UnKEBench-Loc and AKEW-Loc (CF), by extending two existing UKE datasets with locality test data from the unstructured and structured views. This enables a systematic evaluation of the Locality of post-edited models. Furthermore, we identify four factors that may affect the performance of FT-based methods. Based on these factors, we conduct experiments to determine how the well-performing FT-based methods should be trained for the UKE task, providing a training recipe for future research. Our experimental results indicate that the FT-based method with the optimal setting (FT-UKE) is surprisingly strong, outperforming the existing state-of-the-art (SOTA). In batch editing scenarios, FT-UKE shows strong performance as well, with its advantage over SOTA methods increasing as the batch size grows, expanding the average metric lead from +6.78% to +10.80%
Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead
Venue: arXiv cs.SE 2025
Year: 2025

The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint. As concerns regarding environmental sustainability come to the forefront, understanding and optimizing how software impacts the environment becomes paramount. In this paper, we present a state-of-the-art review of methods and tools that enable the measurement of software and AI-related energy and/or carbon emissions. We introduce a taxonomy to categorize the existing work as Monitoring, Estimation, or Black-Box approaches. We delve deeper into the tools and compare them across different dimensions and granularity - for example, whether their measurement encompasses energy and carbon emissions and the components considered (like CPU, GPU, RAM, etc.). We present our observations on the practical use (component wise consolidation of approaches) as well as the challenges that we have identified across the current state-of-the-art. As we start an initiative to address these challenges, we emphasize active collaboration across the community in this important field.
Non-Contact Health Monitoring During Daily Personal Care Routines
Venue: arXiv cs.CV 2025
Year: 2025

Remote photoplethysmography (rPPG) enables non-contact, continuous monitoring of physiological signals and offers a practical alternative to traditional health sensing methods. Although rPPG is promising for daily health monitoring, its application in long-term personal care scenarios, such as mirror-facing routines in high-altitude environments, remains challenging due to ambient lighting variations, frequent occlusions from hand movements, and dynamic facial postures. To address these challenges, we present LADH (Long-term Altitude Daily Health), the first long-term rPPG dataset containing 240 synchronized RGB and infrared (IR) facial videos from 21 participants across five common personal care scenarios, along with ground-truth PPG, respiration, and blood oxygen signals. Our experiments demonstrate that combining RGB and IR video inputs improves the accuracy and robustness of non-contact physiological monitoring, achieving a mean absolute error (MAE) of 4.99 BPM in heart rate estimation. Furthermore, we find that multi-task learning enhances performance across multiple physiological indicators simultaneously. Dataset and code are open at https://github.com/McJackTang/FusionVitals.
Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets
Venue: arXiv cs.CV 2025
Year: 2025

Existing multimodal methods typically assume that different modalities share the same category set. However, in real-world applications, the category distributions in multimodal data exhibit inconsistencies, which can hinder the model's ability to effectively utilize cross-modal information for recognizing all categories. In this work, we propose the practical setting termed Multi-Modal Heterogeneous Category-set Learning (MMHCL), where models are trained in heterogeneous category sets of multi-modal data and aim to recognize complete classes set of all modalities during test. To effectively address this task, we propose a Class Similarity-based Cross-modal Fusion model (CSCF). Specifically, CSCF aligns modality-specific features to a shared semantic space to enable knowledge transfer between seen and unseen classes. It then selects the most discriminative modality for decision fusion through uncertainty estimation. Finally, it integrates cross-modal information based on class similarity, where the auxiliary modality refines the prediction of the dominant one. Experimental results show that our method significantly outperforms existing state-of-the-art (SOTA) approaches on multiple benchmark datasets, effectively addressing the MMHCL task.
TikTok's Research API: Problems Without Explanations
Venue: arXiv cs.CY 2025
Year: 2025

Following the Digital Services Act of 2023, which requires Very Large Online Platforms (VLOPs) and Very Large Online Search Engines (VLOSEs) to facilitate data accessibility for independent research, TikTok augmented its Research API access within Europe in July 2023. This action was intended to ensure compliance with the DSA, bolster transparency, and address systemic risks. Nonetheless, research findings reveal that despite this expansion, notable limitations and inconsistencies persist within the data provided. Our experiment reveals that the API fails to provide metadata for one in eight videos provided through data donations, including official TikTok videos, advertisements, and content from specific accounts, without an apparent reason. The API data is incomplete, making it unreliable when working with data donations, a prominent methodology for algorithm audits and research on platform accountability. To monitor the functionality of the API and eventual fixes implemented by TikTok, we publish a dashboard with a daily check of the availability of 10 videos that were not retrievable in the last month. The video list includes very well-known accounts, notably that of Taylor Swift. The current API lacks the necessary capabilities for thorough independent research and scrutiny. It is crucial to support and safeguard researchers who utilize data scraping to independently validate the platform's data quality.
Mainframe-Style Channel Controllers for Modern Disaggregated Memory Systems
Venue: arXiv cs.OS 2025
Year: 2025

Despite the promise of alleviating the main memory bottleneck, and the existence of commercial hardware implementations, techniques for Near-Data Processing have seen relatively little real-world deployment. The idea has received renewed interest with the appearance of disaggregated or "far" memory, for example in the use of CXL memory pools. However, we argue that the lack of a clear OS-centric abstraction of Near-Data Processing is a major barrier to adoption of the technology. Inspired by the channel controllers which interface the CPU to disk drives in mainframe systems, we propose memory channel controllers as a convenient, portable, and virtualizable abstraction of Near-Data Processing for modern disaggregated memory systems. In addition to providing a clean abstraction that enables OS integration while requiring no changes to CPU architecture, memory channel controllers incorporate another key innovation: they exploit the cache coherence provided by emerging interconnects to provide a much richer programming model, with more fine-grained interaction, than has been possible with existing designs.
Delegations as Adaptive Representation Patterns: Rethinking Influence in Liquid Democracy
Venue: arXiv cs.CY 2025
Year: 2025

Liquid democracy is a mechanism for the division of labor in decision-making through the transitive delegation of influence. In essence, all individuals possess the autonomy to determine the issues with which they will engage directly, while for other matters, they may appoint a representative of their choosing. So far, the literature has studied the delegation structures emerging in liquid democracy as static. As a result, transitivity defined as the capacity to transfer acquired authority to another entity, has been identified as a concern as it would be conducive to unrestrained accumulation of power. Focusing on the implementation of liquid democracy supported by the LiquidFeedback software, we propose a novel approach to assessing the influence of voting nodes in a transitive delegation graph, taking into account the process nature of real-world liquid democracy in which delegation and voting are distinct and increasingly independent activities. By introducing a novel model of delegations in liquid democracy, we show how transitivity may in fact contribute to an effective regulation of deliberation influence and decision-making power. While maintaining the one-person, one-vote paradigm for all votes cast, the anticipated influence of an agent, to the extent it is stemming from transitivity, experiences a precipitous decline following an exponential trajectory. In general, it is our objective to move the first steps towards a rigorous analysis of liquid democracy as an adaptive democratic representation process. The adaptivity aspect of liquid democracy has not yet been explored within the existing academic literature despite it being, we believe, one of its most important features. We therefore also outline a research agenda focusing on this aspect of liquid democracy.
Do LLMs Give Psychometrically Plausible Responses in Educational Assessments?
Venue: arXiv cs.CL 2025
Year: 2025

Knowing how test takers answer items in educational assessments is essential for test development, to evaluate item quality, and to improve test validity. However, this process usually requires extensive pilot studies with human participants. If large language models (LLMs) exhibit human-like response behavior to test items, this could open up the possibility of using them as pilot participants to accelerate test development. In this paper, we evaluate the human-likeness or psychometric plausibility of responses from 18 instruction-tuned LLMs with two publicly available datasets of multiple-choice test items across three subjects: reading, U.S. history, and economics. Our methodology builds on two theoretical frameworks from psychometrics which are commonly used in educational assessment, classical test theory and item response theory. The results show that while larger models are excessively confident, their response distributions can be more human-like when calibrated with temperature scaling. In addition, we find that LLMs tend to correlate better with humans in reading comprehension items compared to other subjects. However, the correlations are not very strong overall, indicating that LLMs should not be used for piloting educational assessments in a zero-shot setting.
Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
Venue: arXiv cs.RO 2025
Year: 2025

End-to-end autonomous driving has emerged as a promising paradigm for directly mapping sensor inputs to planning maneuvers using learning-based modular integrations. However, existing imitation learning (IL)-based models suffer from generalization to hard cases, and a lack of corrective feedback loop under post-deployment. While reinforcement learning (RL) offers a potential solution to tackle hard cases with optimality, it is often hindered by overfitting to specific driving cases, resulting in catastrophic forgetting of generalizable knowledge and sample inefficiency. To overcome these challenges, we propose Reinforced Refinement with Self-aware Expansion (R2SE), a novel learning pipeline that constantly refines hard domain while keeping generalizable driving policy for model-agnostic end-to-end driving systems. Through reinforcement fine-tuning and policy expansion that facilitates continuous improvement, R2SE features three key components: 1) Generalist Pretraining with hard-case allocation trains a generalist imitation learning (IL) driving system while dynamically identifying failure-prone cases for targeted refinement; 2) Residual Reinforced Specialist Fine-tuning optimizes residual corrections using reinforcement learning (RL) to improve performance in hard case domain while preserving global driving knowledge; 3) Self-aware Adapter Expansion dynamically integrates specialist policies back into the generalist model, enhancing continuous performance improvement. Experimental results in closed-loop simulation and real-world datasets demonstrate improvements in generalization, safety, and long-horizon policy robustness over state-of-the-art E2E systems, highlighting the effectiveness of reinforce refinement for scalable autonomous driving.
OctoNav: Towards Generalist Embodied Navigation
Venue: arXiv cs.CV 2025
Year: 2025

Embodied navigation stands as a foundation pillar within the broader pursuit of embodied AI. However, previous navigation research is divided into different tasks/capabilities, e.g., ObjNav, ImgNav and VLN, where they differ in task objectives and modalities, making datasets and methods are designed individually. In this work, we take steps toward generalist navigation agents, which can follow free-form instructions that include arbitrary compounds of multi-modal and multi-capability. To achieve this, we propose a large-scale benchmark and corresponding method, termed OctoNav-Bench and OctoNav-R1. Specifically, OctoNav-Bench features continuous environments and is constructed via a designed annotation pipeline. We thoroughly craft instruction-trajectory pairs, where instructions are diverse in free-form with arbitrary modality and capability. Also, we construct a Think-Before-Action (TBA-CoT) dataset within OctoNav-Bench to provide the thinking process behind actions. For OctoNav-R1, we build it upon MLLMs and adapt it to a VLA-type model, which can produce low-level actions solely based on 2D visual observations. Moreover, we design a Hybrid Training Paradigm (HTP) that consists of three stages, i.e., Action-/TBA-SFT, Nav-GPRO, and Online RL stages. Each stage contains specifically designed learning policies and rewards. Importantly, for TBA-SFT and Nav-GRPO designs, we are inspired by the OpenAI-o1 and DeepSeek-R1, which show impressive reasoning ability via thinking-before-answer. Thus, we aim to investigate how to achieve thinking-before-action in the embodied navigation field, to improve model's reasoning ability toward generalists. Specifically, we propose TBA-SFT to utilize the TBA-CoT dataset to fine-tune the model as a cold-start phrase and then leverage Nav-GPRO to improve its thinking ability. Finally, OctoNav-R1 shows superior performance compared with previous methods.
variability.dev: Towards an Online Toolbox for Feature Modeling
Venue: arXiv cs.SE 2025
Year: 2025

The emergence of feature models as the default to model the variability in configurable systems fosters a rich diversity in applications, application domains, and perspectives. Independent of their domain, modelers require to open, view, edit, transform, save, and configure models as well as to collaborate with others. However, at the time of writing, the top five results when googling ``Online Editor Feature Model'' point to editors that either have minimal functionality, are unmaintained or defunct, or require an offline installation, such as FeatureIDE. In this work we present a preview of our in-development online toolbox for feature modeling, variability.dev. In particular, we showcase our collaborative feature-model editor and our online configurator both of which are built on top of the FeatureIDE library.
Dataset of News Articles with Provenance Metadata for Media Relevance Assessment
Venue: arXiv cs.CL 2025
Year: 2025

Out-of-context and misattributed imagery is the leading form of media manipulation in today's misinformation and disinformation landscape. The existing methods attempting to detect this practice often only consider whether the semantics of the imagery corresponds to the text narrative, missing manipulation so long as the depicted objects or scenes somewhat correspond to the narrative at hand. To tackle this, we introduce News Media Provenance Dataset, a dataset of news articles with provenance-tagged images. We formulate two tasks on this dataset, location of origin relevance (LOR) and date and time of origin relevance (DTOR), and present baseline results on six large language models (LLMs). We identify that, while the zero-shot performance on LOR is promising, the performance on DTOR hinders, leaving room for specialized architectures and future work.
Hierarchical Learning-Enhanced MPC for Safe Crowd Navigation with Heterogeneous Constraints
Venue: arXiv cs.RO 2025
Year: 2025

In this paper, we propose a novel hierarchical framework for robot navigation in dynamic environments with heterogeneous constraints. Our approach leverages a graph neural network trained via reinforcement learning (RL) to efficiently estimate the robot's cost-to-go, formulated as local goal recommendations. A spatio-temporal path-searching module, which accounts for kinematic constraints, is then employed to generate a reference trajectory to facilitate solving the non-convex optimization problem used for explicit constraint enforcement. More importantly, we introduce an incremental action-masking mechanism and a privileged learning strategy, enabling end-to-end training of the proposed planner. Both simulation and real-world experiments demonstrate that the proposed method effectively addresses local planning in complex dynamic environments, achieving state-of-the-art (SOTA) performance. Compared with existing learning-optimization hybrid methods, our approach eliminates the dependency on high-fidelity simulation environments, offering significant advantages in computational efficiency and training scalability. The code will be released as open-source upon acceptance of the paper.