OpenRaiser/Intern-Atlas huggingface dataset:
A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning
Venue: arXiv cs.LG 2025
Year: 2025

Self-supervised learning has become a key method for training deep learning models when labeled data is scarce or unavailable. While graph machine learning holds great promise across various domains, the design of effective pretext tasks for self-supervised graph representation learning remains challenging. Contrastive learning, a popular approach in graph self-supervised learning, leverages positive and negative pairs to compute a contrastive loss function. However, current graph contrastive learning methods often struggle to fully use structural patterns and node similarities. To address these issues, we present a new method called Fused Gromov Wasserstein Subgraph Contrastive Learning (FOSSIL). Our model integrates node-level and subgraph-level contrastive learning, seamlessly combining a standard node-level contrastive loss with the Fused Gromov-Wasserstein distance. This combination helps our method capture both node features and graph structure together. Importantly, our approach works well with both homophilic and heterophilic graphs and can dynamically create views for generating positive and negative pairs. Through extensive experiments on benchmark graph datasets, we show that FOSSIL outperforms or achieves competitive performance compared to current state-of-the-art methods.
Variations in Relevance Judgments and the Shelf Life of Test Collections
Venue: arXiv cs.IR 2025
Year: 2025

The fundamental property of Cranfield-style evaluations, that system rankings are stable even when assessors disagree on individual relevance decisions, was validated on traditional test collections. However, the paradigm shift towards neural retrieval models affected the characteristics of modern test collections, e.g., documents are short, judged with four grades of relevance, and information needs have no descriptions or narratives. Under these changes, it is unclear whether assessor disagreement remains negligible for system comparisons. We investigate this aspect under the additional condition that the few modern test collections are heavily re-used. Given more possible query interpretations due to less formalized information needs, an ``expiration date'' for test collections might be needed if top-effectiveness requires overfitting to a single interpretation of relevance. We run a reproducibility study and re-annotate the relevance judgments of the 2019~TREC Deep Learning track. We can reproduce prior work in the neural retrieval setting, showing that assessor disagreement does not affect system rankings. However, we observe that some models substantially degrade with our new relevance judgments, and some have already reached the effectiveness of humans as rankers, providing evidence that test collections can expire.
Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition
Venue: arXiv cs.LG 2025
Year: 2025

Online handwriting recognition (HWR) using data from inertial measurement units (IMUs) remains challenging due to variations in writing styles and the limited availability of annotated datasets. Previous approaches often struggle with handwriting from unseen writers, making writer-independent (WI) recognition a crucial yet difficult problem. This paper presents an HWR model designed to improve WI HWR on IMU data, using a CNN encoder and a BiLSTM-based decoder. Our approach demonstrates strong robustness to unseen handwriting styles, outperforming existing methods on the WI splits of both the public OnHW dataset and our word-based dataset, achieving character error rates (CERs) of 7.37\% and 9.44\%, and word error rates (WERs) of 15.12\% and 32.17\%, respectively. Robustness evaluation shows that our model maintains superior accuracy across different age groups, and knowledge learned from one group generalizes better to another. Evaluation on our sentence-based dataset further demonstrates its potential in recognizing full sentences. Through comprehensive ablation studies, we show that our design choices lead to a strong balance between performance and efficiency. These findings support the development of more adaptable and scalable HWR systems for real-world applications.
Cicada: A Pipeline-Efficient Approach to Serverless Inference with Decoupled Management
Venue: arXiv cs.DC 2025
Year: 2025

Serverless computing has emerged as a pivotal paradigm for deploying Deep Learning (DL) models, offering automatic scaling and cost efficiency. However, the inherent cold start problem in serverless ML inference systems, particularly the time-consuming model loading process, remains a significant bottleneck. Utilizing pipelined model loading improves efficiency but still suffer from pipeline stalls due to sequential layer construction and monolithic weight loading. In this paper, we propose \textit{Cicada}, a novel pipeline optimization framework that coordinates computational, storage, and scheduling resources through three key mechanisms: (1) \textit{MiniLoader}: which reduces layer construction overhead by opportunistically optimizing parameter initialization; (2) \textit{WeightDecoupler}: decoupling weight file processing from layer construction, enabling asynchronous weight retrieval and out-of-order weight application; (3) \textit{Priority-Aware Scheduler}: dynamically allocating resources to ensure high-priority inference tasks are executed promptly. Our experimental results demonstrate that Cicada achieves significant performance improvements over the state-of-the-art PISeL framework. Specifically, Cicada reduces end-to-end inference latency by an average of 61.59\%, with the MiniLoader component contributing the majority of this optimization (53.41\%), and the WeightDecoupler achieves up to 26.17\% improvement. Additionally, Cicada achieves up to 2.52x speedup in the inference pipeline utlization compared to PISeL.
Explainable Biomedical Claim Verification with Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

Verification of biomedical claims is critical for healthcare decision-making, public health policy and scientific research. We present an interactive biomedical claim verification system by integrating LLMs, transparent model explanations, and user-guided justification. In the system, users first retrieve relevant scientific studies from a persistent medical literature corpus and explore how different LLMs perform natural language inference (NLI) within task-adaptive reasoning framework to classify each study as "Support," "Contradict," or "Not Enough Information" regarding the claim. Users can examine the model's reasoning process with additional insights provided by SHAP values that highlight word-level contributions to the final result. This combination enables a more transparent and interpretable evaluation of the model's decision-making process. A summary stage allows users to consolidate the results by selecting a result with narrative justification generated by LLMs. As a result, a consensus-based final decision is summarized for each retrieved study, aiming safe and accountable AI-assisted decision-making in biomedical contexts. We aim to integrate this explainable verification system as a component within a broader evidence synthesis framework to support human-AI collaboration.
TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions
Venue: arXiv cs.IR 2025
Year: 2025

Temporal awareness is crucial in many information retrieval tasks, particularly in scenarios where the relevance of documents depends on their alignment with the query's temporal context. Traditional approaches such as BM25 and Dense Passage Retrieval (DPR) focus on lexical or semantic similarity but tend to neglect the temporal alignment between queries and documents, which is essential for time-sensitive tasks like temporal question answering (TQA). We propose TempRetriever, a novel extension of DPR that explicitly incorporates temporal information by embedding both the query date and document timestamp into the retrieval process. This allows retrieving passages that are not only contextually relevant but also aligned with the temporal intent of queries. We evaluate TempRetriever on two large-scale datasets ArchivalQA and ChroniclingAmericaQA demonstrating its superiority over baseline retrieval models across multiple metrics. TempRetriever achieves a 6.63\% improvement in Top-1 retrieval accuracy and a 3.79\% improvement in NDCG@10 compared to the standard DPR on ArchivalQA. Similarly, for ChroniclingAmericaQA, TempRetriever exhibits a 9.56\% improvement in Top-1 retrieval accuracy and a 4.68\% improvement in NDCG@10. We also propose a novel, time-based negative sampling strategy which further enhances retrieval performance by addressing temporal misalignment during training. Our results underline the importance of temporal aspects in dense retrieval systems and establish a new benchmark for time-aware passage retrieval.
Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar
Venue: arXiv cs.RO 2025
Year: 2025

Localizing humans is a key prerequisite for any service robot operating in proximity to people. In these scenarios, robots rely on a multitude of state-of-the-art detectors usually designed to operate with RGB-D cameras or expensive 3D LiDARs. However, most commercially available service robots are equipped with cameras with a narrow field of view, making them blind when a user is approaching from other directions, or inexpensive 1D LiDARs whose readings are difficult to interpret. To address these limitations, we propose a self-supervised approach to detect humans and estimate their 2D pose from 1D LiDAR data, using detections from an RGB-D camera as a supervision source. Our approach aims to provide service robots with spatial awareness of nearby humans. After training on 70 minutes of data autonomously collected in two environments, our model is capable of detecting humans omnidirectionally from 1D LiDAR data in a novel environment, with 71% precision and 80% recall, while retaining an average absolute error of 13 cm in distance and 44° in orientation.
Beyond Words: A Latent Memory Approach to Internal Reasoning in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in large language models (LLMs) have popularized the chain-of-thought (CoT) paradigm, in which models produce explicit reasoning steps in natural language. Although this approach improves interpretability and facilitates external auditing, it may not represent the most computationally efficient method for internal reasoning. In contrast, human cognition relies on implicit mental representations that recall past sensory and episodic information without requiring complete verbalization. In this paper, we propose a framework that integrates implicit mental representations into the internal reasoning processes of LLMs. Preliminary experiments indicate that incorporating an Implicit Memory Module (IMM) into a simple GPT model yields a reduction of between 35% and 57% in final training loss compared to a regular GPT baseline. The addition of an explicit interpretability channel (e.g., a chain-of-thought decoder) is straightforward to implement within this approach. We outline theoretical foundations, propose technical mechanisms to scale the memory module, and discuss how these ideas may lead to more efficient and robust reasoning, with optional future extensions for explicit auditability.
When MIS and Maximal Matching are Easy in the Congested Clique
Venue: arXiv cs.DC 2025
Year: 2025

Two of the most fundamental distributed symmetry-breaking problems are that of finding a maximal independent set (MIS) and a maximal matching (MM) in a graph. It is a major open question whether these problems can be solved in constant rounds of the all-to-all communication model of \textsf{Congested\ Clique}, with $O(\log\log Δ)$ being the best upper bound known (where $Δ$ is the maximum degree). We explore in this paper the boundary of the feasible, asking for \emph{which graphs} we can solve the problems in constant rounds. We find that for several graph parameters, ranging from sparse to highly dense graphs, the problems do have a constant-round solution. In particular, we give algorithms that run in constant rounds when: (1) the average degree is at most $d(G) \le 2^{O(\sqrt{\log n})}$, (2) the neighborhood independence number is at most $β(G) \le 2^{O(\sqrt{\log n})}$, or (3) the independence number is at most $α(G) \le |V(G)|/d(G)^μ$, for any constant $μ> 0$. Further, we establish that these are tight bounds for the known methods, for all three parameters, suggesting that new ideas are needed for further progress.
S4ConvD: Adaptive Scaling and Frequency Adjustment for Energy-Efficient Sensor Networks in Smart Buildings
Venue: arXiv cs.LG 2025
Year: 2025

Predicting energy consumption in smart buildings is challenging due to dependencies in sensor data and the variability of environmental conditions. We introduce S4ConvD, a novel convolutional variant of Deep State Space Models (Deep-SSMs), that minimizes reliance on extensive preprocessing steps. S4ConvD is designed to optimize runtime in resource-constrained environments. By implementing adaptive scaling and frequency adjustments, this model shows to capture complex temporal patterns in building energy dynamics. Experiments on the ASHRAE Great Energy Predictor III dataset reveal that S4ConvD outperforms current benchmarks. Additionally, S4ConvD benefits from significant improvements in GPU runtime through the use of Block Tiling optimization techniques. Thus, S4ConvD has the potential for practical deployment in real-time energy modeling. Furthermore, the complete codebase and dataset are accessible on GitHub, fostering open-source contributions and facilitating further research. Our method also promotes resource-efficient model execution, enhancing both energy forecasting and the potential integration of renewable energy sources into smart grid systems.
Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing
Venue: arXiv cs.LG 2025
Year: 2025

This paper studies fast adversarial training against sparse adversarial perturbations bounded by $l_0$ norm. We demonstrate the challenges of employing $1$-step attacks on $l_0$ bounded perturbations for fast adversarial training, including degraded performance and the occurrence of catastrophic overfitting (CO). We highlight that CO in $l_0$ adversarial training is caused by sub-optimal perturbation locations of $1$-step attack. Theoretical and empirical analyses reveal that the loss landscape of $l_0$ adversarial training is more craggy compared to its $l_\infty$, $l_2$ and $l_1$ counterparts. Moreover, we corroborate that the craggy loss landscape can aggravate CO. To address these issues, we propose Fast-LS-$l_0$ that incorporates soft labels and the trade-off loss function to smooth the adversarial loss landscape. Extensive experiments demonstrate our method can overcome the challenge of catastrophic overfitting, achieve state-of-the-art performance, and narrow down the performance gap between $1$-step and multi-step adversarial training against sparse attacks.
Proof systems for partial incorrectness logic (partial reverse Hoare logic)
Venue: arXiv cs.LO 2025
Year: 2025

Partial incorrectness logic (partial reverse Hoare logic) has recently been introduced as a new Hoare-style logic that over-approximates the weakest pre-conditions of a program and a post-condition. It is expected to verify systems where the final state must guarantee its initial state, such as authentication, secure communication tools and digital signatures. However, the logic has only been given semantics. This paper defines two proof systems for partial incorrectness logic (partial reverse Hoare logic): ordinary and cyclic proof systems. They are sound and relatively complete. The relative completeness of our ordinary proof system is proved by showing that the weakest pre-condition of a while loop and a post-condition is its loop invariant. The relative completeness of our cyclic proof system is also proved by providing a way to transform any cyclic proof into an ordinary proof.
Fast 3D point clouds retrieval for Large-scale 3D Place Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Retrieval in 3D point clouds is a challenging task that consists in retrieving the most similar point clouds to a given query within a reference of 3D points. Current methods focus on comparing descriptors of point clouds in order to identify similar ones. Due to the complexity of this latter step, here we focus on the acceleration of the retrieval by adapting the Differentiable Search Index (DSI), a transformer-based approach initially designed for text information retrieval, for 3D point clouds retrieval. Our approach generates 1D identifiers based on the point descriptors, enabling direct retrieval in constant time. To adapt DSI to 3D data, we integrate Vision Transformers to map descriptors to these identifiers while incorporating positional and semantic encoding. The approach is evaluated for place recognition on a public benchmark comparing its retrieval capabilities against state-of-the-art methods, in terms of quality and speed of returned point clouds.
An LLM-based Delphi Study to Predict GenAI Evolution
Venue: arXiv cs.AI 2025
Year: 2025

Predicting the future trajectory of complex and rapidly evolving systems remains a significant challenge, particularly in domains where data is scarce or unreliable. This study introduces a novel approach to qualitative forecasting by leveraging Large Language Models to conduct Delphi studies. The methodology was applied to explore the future evolution of Generative Artificial Intelligence, revealing insights into key factors such as geopolitical tensions, economic disparities, regulatory frameworks, and ethical considerations. The results highlight how LLM-based Delphi studies can facilitate structured scenario analysis, capturing diverse perspectives while mitigating issues such as respondent fatigue. However, limitations emerge in terms of knowledge cutoffs, inherent biases, and sensitivity to initial conditions. While the approach provides an innovative means for structured foresight, this method could be also considered as a novel form of reasoning. further research is needed to refine its ability to manage heterogeneity, improve reliability, and integrate external data sources.
AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests
Venue: arXiv cs.RO 2025
Year: 2025

Generating adversarial safety-critical scenarios is a pivotal method for testing autonomous driving systems, as it identifies potential weaknesses and enhances system robustness and reliability. However, existing approaches predominantly emphasize unrestricted collision scenarios, prompting non-player character (NPC) vehicles to attack the ego vehicle indiscriminately. These works overlook these scenarios' authenticity, rationality, and relevance, resulting in numerous extreme, contrived, and largely unrealistic collision events involving aggressive NPC vehicles. To rectify this issue, we propose a three-layer relative safety region model, which partitions the area based on danger levels and increases the likelihood of NPC vehicles entering relative boundary regions. This model directs NPC vehicles to engage in adversarial actions within relatively safe boundary regions, thereby augmenting the scenarios' authenticity. We introduce AuthSim, a comprehensive platform for generating authentic and effective safety-critical scenarios by integrating the three-layer relative safety region model with reinforcement learning. To our knowledge, this is the first attempt to address the authenticity and effectiveness of autonomous driving system test scenarios comprehensively. Extensive experiments demonstrate that AuthSim outperforms existing methods in generating effective safety-critical scenarios. Notably, AuthSim achieves a 5.25% improvement in average cut-in distance and a 27.12% enhancement in average collision interval time, while maintaining higher efficiency in generating effective safety-critical scenarios compared to existing methods. This underscores its significant advantage in producing authentic scenarios over current methodologies.
Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation
Venue: arXiv cs.CL 2025
Year: 2025

Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queries remains challenging and manual. We present an automated system utilizing large language models that combines criteria parsing, two-level retrieval augmented generation with specialized knowledge bases, medical concept standardization, and SQL generation to retrieve patient cohorts with patient funnels. The system achieves 0.75 F1-score in cohort identification on EHR data, effectively capturing complex temporal and logical relationships. These results demonstrate the feasibility of automated cohort generation for epidemiological research.
Optimizing Large Language Models for ESG Activity Detection in Financial Texts
Venue: arXiv cs.AI 2025
Year: 2025

The integration of Environmental, Social, and Governance (ESG) factors into corporate decision-making is a fundamental aspect of sustainable finance. However, ensuring that business practices align with evolving regulatory frameworks remains a persistent challenge. AI-driven solutions for automatically assessing the alignment of sustainability reports and non-financial disclosures with specific ESG activities could greatly support this process. Yet, this task remains complex due to the limitations of general-purpose Large Language Models (LLMs) in domain-specific contexts and the scarcity of structured, high-quality datasets. In this paper, we investigate the ability of current-generation LLMs to identify text related to environmental activities. Furthermore, we demonstrate that their performance can be significantly enhanced through fine-tuning on a combination of original and synthetically generated data. To this end, we introduce ESG-Activities, a benchmark dataset containing 1,325 labelled text segments classified according to the EU ESG taxonomy. Our experimental results show that fine-tuning on ESG-Activities significantly enhances classification accuracy, with open models such as Llama 7B and Gemma 7B outperforming large proprietary solutions in specific configurations. These findings have important implications for financial analysts, policymakers, and AI researchers seeking to enhance ESG transparency and compliance through advanced natural language processing techniques.
HQColon: A Hybrid Interactive Machine Learning Pipeline for High Quality Colon Labeling and Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

High-resolution colon segmentation is crucial for clinical and research applications, such as digital twins and personalized medicine. However, the leading open-source abdominal segmentation tool, TotalSegmentator, struggles with accuracy for the colon, which has a complex and variable shape, requiring time-intensive labeling. Here, we present the first fully automatic high-resolution colon segmentation method. To develop it, we first created a high resolution colon dataset using a pipeline that combines region growing with interactive machine learning to efficiently and accurately label the colon on CT colonography (CTC) images. Based on the generated dataset consisting of 435 labeled CTC images we trained an nnU-Net model for fully automatic colon segmentation. Our fully automatic model achieved an average symmetric surface distance of 0.2 mm (vs. 4.0 mm from TotalSegmentator) and a 95th percentile Hausdorff distance of 1.0 mm (vs. 18 mm from TotalSegmentator). Our segmentation accuracy substantially surpasses TotalSegmentator. We share our trained model and pipeline code, providing the first and only open-source tool for high-resolution colon segmentation. Additionally, we created a large-scale dataset of publicly available high-resolution colon labels.