OpenRaiser/Intern-Atlas huggingface dataset:
Investigating the originality of scientific papers across time and domain: A quantitative analysis
Venue: arXiv cs.DL 2025
Year: 2025

The study of creativity in science has long sought quantitative metrics capable of capturing the originality of the scientific insights contained within articles and other scientific works. In recent years, the field has witnessed a substantial expansion of research activity, enabled by advances in natural language processing and network analysis, and has utilised both macro- and micro-scale approaches with success. However, they often do not examine the text itself for evidence of originality. In this paper, we apply a computational measure correlating with originality from creativity science, Divergent Semantic Integration (DSI), to a set of 51,200 scientific abstracts and titles sourced from the Web of Science. To adapt DSI for application to scientific texts, we advance the original BERT method by incorporating SciBERT (a model trained on scientific corpora) into the computation of DSI. In our study, we observe that DSI plays a more pronounced role in the accrual of early citations for papers with fewer authors, varies substantially across subjects and research fields, and exhibits a declining correlation with citation counts over time. Furthermore, by modelling SciBERT- and BERT-DSI as predictors of the logarithm of 5-year citation counts alongside field, publication year, and the logarithm of author count, we find statistically significant relationships, with adjusted R-squared of 0.103 and 0.101 for BERT-DSI and SciBERT-DSI. Because existing scientometric measures rarely assess the originality expressed in textual content, DSI provides a valuable means of directly quantifying the conceptual originality embedded in scientific writing.
Finding core subgraphs of directed graphs via discrete Ricci curvature flow
Venue: arXiv cs.SI 2025
Year: 2025

Ricci curvature and its associated flow offer powerful geometric methods for analyzing complex networks. While existing research heavily focuses on applications for undirected graphs such as community detection and core extraction, there have been relatively less attention on directed graphs. In this paper, we introduce a definition of Ricci curvature and an accompanying curvature flow for directed graphs. Crucially, for strongly connected directed graphs, this flow admits a unique global solution. We then apply this flow to detect strongly connected subgraphs from weakly connected directed graphs. (A weakly connected graph is connected overall but not necessarily strongly connected). Unlike prior work requiring graphs to be strongly connected, our method loosens this requirement. We transform a weakly connected graph into a strongly connected one by adding edges with very large artificial weights. This modification does not compromise our core subgraph detection. Due to their extreme weight, these added edges are automatically discarded during the final iteration of the Ricci curvature flow. For core evaluation, our approach consistently surpasses traditional methods, achieving better results on at least two out of three key metrics. The implementation code is publicly available at https://github.com/12tangze12/Finding-core-subgraphs-on-directed-graphs.
Towards symbolic regression for interpretable clinical decision scores
Venue: arXiv cs.LG 2025
Year: 2025

Medical decision-making makes frequent use of algorithms that combine risk equations with rules, providing clear and standardized treatment pathways. Symbolic regression (SR) traditionally limits its search space to continuous function forms and their parameters, making it difficult to model this decision-making. However, due to its ability to derive data-driven, interpretable models, SR holds promise for developing data-driven clinical risk scores. To that end we introduce Brush, an SR algorithm that combines decision-tree-like splitting algorithms with non-linear constant optimization, allowing for seamless integration of rule-based logic into symbolic regression and classification models. Brush achieves Pareto-optimal performance on SRBench, and was applied to recapitulate two widely used clinical scoring systems, achieving high accuracy and interpretable models. Compared to decision trees, random forests, and other SR methods, Brush achieves comparable or superior predictive performance while producing simpler models.
SkipKV: Selective Skipping of KV Generation and Storage for Efficient Inference with Large Reasoning Models
Venue: arXiv cs.AI 2025
Year: 2025

Large reasoning models (LRMs) often cost significant key-value (KV) cache overhead, due to their linear growth with the verbose chain-of-thought (CoT) reasoning process. This costs both memory and throughput bottleneck limiting their efficient deployment. Towards reducing KV cache size during inference, we first investigate the effectiveness of existing KV cache eviction methods for CoT reasoning. Interestingly, we find that due to unstable token-wise scoring and the reduced effective KV budget caused by padding tokens, state-of-the-art (SoTA) eviction methods fail to maintain accuracy in the multi-batch setting. Additionally, these methods often generate longer sequences than the original model, as semantic-unaware token-wise eviction leads to repeated revalidation during reasoning. To address these issues, we present \textbf{SkipKV}, a \textbf{\textit{training-free}} KV compression method for selective \textit{eviction} and \textit{generation} operating at a coarse-grained sentence-level sequence removal for efficient CoT reasoning. In specific, it introduces a \textit{sentence-scoring metric} to identify and remove highly similar sentences while maintaining semantic coherence. To suppress redundant generation, SkipKV dynamically adjusts a steering vector to update the hidden activation states during inference enforcing the LRM to generate concise response. Extensive evaluations on multiple reasoning benchmarks demonstrate the effectiveness of SkipKV in maintaining up to $\mathbf{26.7}\%$ improved accuracy compared to the alternatives, at a similar compression budget. Additionally, compared to SoTA, SkipKV yields up to $\mathbf{1.6}\times$ fewer generation length while improving throughput up to $\mathbf{1.7}\times$.
Benchmarking Offline Multi-Objective Reinforcement Learning in Critical Care
Venue: arXiv cs.LG 2025
Year: 2025

In critical care settings such as the Intensive Care Unit, clinicians face the complex challenge of balancing conflicting objectives, primarily maximizing patient survival while minimizing resource utilization (e.g., length of stay). Single-objective Reinforcement Learning approaches typically address this by optimizing a fixed scalarized reward function, resulting in rigid policies that fail to adapt to varying clinical priorities. Multi-objective Reinforcement Learning (MORL) offers a solution by learning a set of optimal policies along the Pareto Frontier, allowing for dynamic preference selection at test time. However, applying MORL in healthcare necessitates strict offline learning from historical data. In this paper, we benchmark three offline MORL algorithms, Conditioned Conservative Pareto Q-Learning (CPQL), Adaptive CPQL, and a modified Pareto Efficient Decision Agent (PEDA) Decision Transformer (PEDA DT), against three scalarized single-objective baselines (BC, CQL, and DDQN) on the MIMIC-IV dataset. Using Off-Policy Evaluation (OPE) metrics, we demonstrate that PEDA DT algorithm offers superior flexibility compared to static scalarized baselines. Notably, our results extend previous findings on single-objective Decision Transformers in healthcare, confirming that sequence modeling architectures remain robust and effective when scaled to multi-objective conditioned generation. These findings suggest that offline MORL is a promising framework for enabling personalized, adjustable decision-making in critical care without the need for retraining.
"Your Privacy is Your Responsibility": Understanding How Users Collectively Navigate the Complexity of Privacy on Quora
Venue: arXiv cs.HC 2025
Year: 2025

In the current technology environment, users are often in a vulnerable position when it comes to protecting their privacy. Previous efforts to promote privacy protection have largely focused on top-down approaches such as regulation and technology design, missing opportunities to understand how to empower users through bottom-up, collective approaches. Our paper addresses this by analyzing what and how privacy-related topics are discussed on Quora. We identified a wide range of interconnected privacy topics brought up by the users, including privacy risks and dangers, protection strategies, organizational practices, and existing laws and regulations. Our results highlight the interplay among the individual, technological, organizational, and societal factors affecting users' privacy attitudes. Moreover, we provide implications for designing community-based tools to better support users' collective efforts in navigating privacy, tools that incorporate users' diverse privacy-related behaviors and preferences, simplify information access and sharing, and connect designers and developers with the user community.
CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space
Venue: arXiv cs.LG 2025
Year: 2025

Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform. While world models (WMs) offer a paradigm for generative prediction, existing medical applications remain limited. Existing methods often rely on stochastic diffusion models, focusing on visual reconstruction rather than causal, physiological transitions. Furthermore, in medical domain, models like MeWM typically ignore patient-specific temporal and clinical contexts and lack a feedback mechanism to link predictions to treatment decisions. To address these gaps, we introduce CLARITY, a medical world model that forecasts disease evolution directly within a structured latent space. It explicitly integrates time intervals (temporal context) and patient-specific data (clinical context) to model treatment-conditioned progression as a smooth, interpretable trajectory, and thus generate physiologically faithful, individualized treatment plans. Finally, CLARITY introduces a novel prediction-to-decision framework, translating latent rollouts into transparent, actionable recommendations. CLARITY demonstrates state-of-the-art performance in treatment planning. On the MU-Glioma-Post dataset, our approach outperforms recent MeWM by 12\%, and significantly surpasses all other medical-specific large language models.
What Pulls the Strings? Understanding the Characteristics and Role of Argumentation in Open-Source Software Usability Discussions
Venue: arXiv cs.SE 2025
Year: 2025

The usability of open-source software (OSS) is important but frequently overlooked in favor of technical and functional complexity. Argumentation can be a pivotal device for diverse stakeholders in OSS usability discussions to express opinions and persuade others. However, the characteristics of argument discourse in those discussions remain unknown, resulting in difficulties in providing effective support for discussion participants. We address this through a comprehensive analysis of argument discourse and quality in five OSS projects. Our results indicated that usability discussions are predominantly argument-driven, although their qualities vary. Issue comments exhibit lower-quality arguments than the issue posts, suggesting a shortage of collective intelligence about usability in OSS communities. Moreover, argument discourse and quality have various impacts on the subsequent behavior of participants. Overall, this research offers insights to help OSS stakeholders build more effective arguments and eventually improve OSS usability. These insights can also inform studies about other distributed collaborative communities.
Scaffolding Reshapes Dialogic Engagement in Collaborative Problem Solving: Comparative Analysis of Two Approaches
Venue: arXiv cs.SI 2025
Year: 2025

Supporting learners during Collaborative Problem Solving (CPS) is a necessity. Existing studies have compared scaffolds with maximal and minimal instructional support by studying their effects on learning and behaviour. However, our understanding of how such scaffolds could differently shape the distribution of individual dialogic engagement and behaviours across different CPS phases remains limited. This study applied Heterogeneous Interaction Network Analysis (HINA) and Sequential Pattern Mining (SPM) to uncover the structural effects of scaffolding on different phases of the CPS process among K-12 students in authentic educational settings. Students with a maximal scaffold demonstrated higher dialogic engagement across more phases than those with a minimal scaffold. However, they were extensively demonstrating scripting behaviours across the phases, evidencing the presence of overscripting. Although students with the minimal scaffold demonstrated more problem solving behaviours and fewer scripting behaviours across the phases, they repeated particular behaviours in multiple phases and progressed more to socialising behaviours. In both scaffold conditions, problem solving behaviours rarely progressed to other problem solving behaviours. The paper discusses the implications of these findings for scaffold design and teaching practice of CPS, and highlights the distinct yet complementary value of HINA and SPM approaches to investigate students' learning processes during CPS.
Fairness-aware PageRank via Edge Reweighting
Venue: arXiv cs.SI 2025
Year: 2025

Link-analysis algorithms, such as PageRank, are instrumental in understanding the structural dynamics of networks by evaluating the importance of individual vertices based on their connectivity. Recently, with the rising importance of responsible AI, the question of fairness in link-analysis algorithms has gained traction. In this paper, we present a new approach for incorporating group fairness into the PageRank algorithm by reweighting the transition probabilities in the underlying transition matrix. We formulate the problem of achieving fair PageRank by seeking to minimize the fairness loss, which is the difference between the original group-wise PageRank distribution and a target PageRank distribution. We further define a group-adapted fairness notion, which accounts for group homophily by considering random walks with group-biased restart for each group. Since the fairness loss is non-convex, we propose an efficient projected gradient-descent method for computing locally-optimal edge weights. Unlike earlier approaches, we do not recommend adding new edges to the network, nor do we adjust the restart vector. Instead, we keep the topology of the underlying network unchanged and only modify the relative importance of existing edges. We empirically compare our approach with state-of-the-art baselines and demonstrate the efficacy of our method, where very small changes in the transition matrix lead to significant improvement in the fairness of the PageRank algorithm.
Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks
Venue: arXiv cs.LG 2025
Year: 2025

We propose an interpretable deep competing risks model called the Deep Kernel Aalen-Johansen (DKAJ) estimator, which generalizes the classical Aalen-Johansen nonparametric estimate of cumulative incidence functions (CIFs). Each data point (e.g., patient) is represented as a weighted combination of clusters. If a data point has nonzero weight only for one cluster, then its predicted CIFs correspond to those of the classical Aalen-Johansen estimator restricted to data points from that cluster. These weights come from an automatically learned kernel function that measures how similar any two data points are. On four standard competing risks datasets, we show that DKAJ is competitive with state-of-the-art baselines while being able to provide visualizations to assist model interpretation.
Universal Adversarial Suffixes Using Calibrated Gumbel-Softmax Relaxation
Venue: arXiv cs.CL 2025
Year: 2025

Language models (LMs) are often used as zero-shot or few-shot classifiers by scoring label words, but they remain fragile to adversarial prompts. Prior work typically optimizes task- or model-specific triggers, making results difficult to compare and limiting transferability. We study universal adversarial suffixes: short token sequences (4-10 tokens) that, when appended to any input, broadly reduce accuracy across tasks and models. Our approach learns the suffix in a differentiable "soft" form using Gumbel-Softmax relaxation and then discretizes it for inference. Training maximizes calibrated cross-entropy on the label region while masking gold tokens to prevent trivial leakage, with entropy regularization to avoid collapse. A single suffix trained on one model transfers effectively to others, consistently lowering both accuracy and calibrated confidence. Experiments on sentiment analysis, natural language inference, paraphrase detection, commonsense QA, and physical reasoning with Qwen2-1.5B, Phi-1.5, and TinyLlama-1.1B demonstrate consistent attack effectiveness and transfer across tasks and model families.
Improving the Sensitivity of Backdoor Detectors via Class Subspace Orthogonalization
Venue: arXiv cs.LG 2025
Year: 2025

Most post-training backdoor detection methods rely on attacked models exhibiting extreme outlier detection statistics for the target class of an attack, compared to non-target classes. However, these approaches may fail: (1) when some (non-target) classes are easily discriminable from all others, in which case they may naturally achieve extreme detection statistics (e.g., decision confidence); and (2) when the backdoor is subtle, i.e., with its features weak relative to intrinsic class-discriminative features. A key observation is that the backdoor target class has contributions to its detection statistic from both the backdoor trigger and from its intrinsic features, whereas non-target classes only have contributions from their intrinsic features. To achieve more sensitive detectors, we thus propose to suppress intrinsic features while optimizing the detection statistic for a given class. For non-target classes, such suppression will drastically reduce the achievable statistic, whereas for the target class the (significant) contribution from the backdoor trigger remains. In practice, we formulate a constrained optimization problem, leveraging a small set of clean examples from a given class, and optimizing the detection statistic while orthogonalizing with respect to the class's intrinsic features. We dub this plug-and-play approach Class Subspace Orthogonalization (CSO) and assess it against challenging mixed-label and adaptive attacks.
Chat with UAV -- Human-UAV Interaction Based on Large Language Models
Venue: arXiv cs.RO 2025
Year: 2025

The future of UAV interaction systems is evolving from engineer-driven to user-driven, aiming to replace traditional predefined Human-UAV Interaction designs. This shift focuses on enabling more personalized task planning and design, thereby achieving a higher quality of interaction experience and greater flexibility, which can be used in many fileds, such as agriculture, aerial photography, logistics, and environmental monitoring. However, due to the lack of a common language between users and the UAVs, such interactions are often difficult to be achieved. The developments of Large Language Models possess the ability to understand nature languages and Robots' (UAVs') behaviors, marking the possibility of personalized Human-UAV Interaction. Recently, some HUI frameworks based on LLMs have been proposed, but they commonly suffer from difficulties in mixed task planning and execution, leading to low adaptability in complex scenarios. In this paper, we propose a novel dual-agent HUI framework. This framework constructs two independent LLM agents (a task planning agent, and an execution agent) and applies different Prompt Engineering to separately handle the understanding, planning, and execution of tasks. To verify the effectiveness and performance of the framework, we have built a task database covering four typical application scenarios of UAVs and quantified the performance of the HUI framework using three independent metrics. Meanwhile different LLM models are selected to control the UAVs with compared performance. Our user study experimental results demonstrate that the framework improves the smoothness of HUI and the flexibility of task execution in the tasks scenario we set up, effectively meeting users' personalized needs.
MobileFineTuner: A Unified End-to-End Framework for Fine-Tuning LLMs on Mobile Phones
Venue: arXiv cs.LG 2025
Year: 2025

Mobile phones are the most ubiquitous end devices, generating vast amounts of human-authored data and serving as the primary platform for end-side applications. As high-quality public data for large language models (LLMs) approaches exhaustion, on-device fine-tuning provides an opportunity to leverage private user data while preserving privacy. However, existing approaches are predominantly simulation-based or rely on IoT devices and PCs, leaving commodity mobile phones largely unexplored. A key gap is the absence of an open-source framework that enables practical LLM fine-tuning on mobile phones. We present MobileFineTuner, a unified open-source framework that enables end-to-end LLM fine-tuning directly on commodity mobile phones. MobileFineTuner is designed for efficiency, scalability, and usability, supporting full-parameters fine-tuning (Full-FT) and parameter-efficient fine-tuning (PEFT). To address the memory and energy limitations inherent to mobile phones, we introduce system-level optimizations including parameter sharding, gradient accumulation, and energy-aware computation scheduling. We demonstrate the practicality of MobileFineTuner by fine-tuning GPT-2, Gemma 3, and Qwen 2.5 on real mobile phones. Extensive experiments and ablation studies validate the effectiveness of the proposed optimizations and establish MobileFineTuner as a viable foundation for future research on on-device LLM training.