OpenRaiser/Intern-Atlas huggingface dataset:
What do you say? A pilot study investigating student responses in Data Driven Classroom Interviews
Venue: arXiv cs.HC 2025
Year: 2025

Data that contextualizes student interactions with online learning systems can be challenging to obtain. This study looks at the rhetorical strategies of a novel method for conducting in-the-moment Data-Driven Classroom Interviews (DDCIs). By using Ordered Network Analysis (ONA) to reanalyze data from Wei et al.'s (2025) Epistemic Network Analysis, we better account for the sequences in which these rhetorical strategies emerge during the interview process. Specifically, we examine how five rhetorical strategies by interviewers relate to five possible rhetorical strategies used in student responses. As with the previous study, results demonstrate minor differences in how students with high and low situational interest respond. Namely, whereas students with high situational interest show moderately higher levels of enthusiasm, students with low situational interest are more likely to respond to interviewers with an explanation. However, overall this study confirms that there are few interviewer-driven differences in these interviews, and it documents that interviewers are following guidelines to rely upon open-ended questions
TrimTokenator-LC: Towards Adaptive Visual Token Pruning for Large Multimodal Models with Long Contexts
Venue: arXiv cs.CV 2025
Year: 2025

Large Multimodal Models (LMMs) have proven effective on various tasks. They typically encode visual inputs into Original Model sequences of tokens, which are then concatenated with textual tokens and jointly processed by the language model. However, the growing number of visual tokens greatly increases inference cost. Visual token pruning has emerged as a promising solution. However, existing methods often overlook scenarios involving long context inputs with multiple images. In this paper, we analyze the challenges of visual token pruning in long context, multi-image settings and introduce an adaptive pruning method tailored for such scenarios. We decompose redundancy into intra-image and inter-image components and quantify them through intra-image diversity and inter-image variation, which jointly guide dynamic budget allocation. Our approach consists of two stages. The intra-image stage allocates each image a content-aware token budget and greedily selects its most representative tokens. The inter-image stage performs global diversity filtering to form a candidate pool and then applies a Pareto selection procedure that balances diversity with text alignment. Extensive experiments show that our approach can reduce up to 80% of visual tokens while maintaining performance in long context settings.
Identifying social bots via heterogeneous motifs based on Naïve Bayes model
Venue: arXiv cs.CR 2025
Year: 2025

Identifying social bots has become a critical challenge due to their significant influence on social media ecosystems. Despite advancements in detection methods, most topology-based approaches insufficiently account for the heterogeneity of neighborhood preferences and lack a systematic theoretical foundation, relying instead on intuition and experience. Here, we propose a theoretical framework for detecting social bots utilizing heterogeneous motifs based on the Naïve Bayes model. Specifically, we refine homogeneous motifs into heterogeneous ones by incorporating node-label information, effectively capturing the heterogeneity of neighborhood preferences. Additionally, we systematically evaluate the contribution of different node pairs within heterogeneous motifs to the likelihood of a node being identified as a social bot. Furthermore, we mathematically quantify the maximum capability of each heterogeneous motif, enabling the estimation of its potential benefits. Comprehensive evaluations on four large, publicly available benchmarks confirm that our method surpasses state-of-the-art techniques, achieving superior performance across five evaluation metrics. Moreover, our results reveal that selecting motifs with the highest capability achieves detection performance comparable to using all heterogeneous motifs. Overall, our framework offers an effective and theoretically grounded solution for social bot detection, significantly enhancing cybersecurity measures in social networks.
Neighbor-Aware Token Reduction via Hilbert Curve for Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Vision Transformers (ViTs) have achieved remarkable success in visual recognition tasks, but redundant token representations limit their computational efficiency. Existing token merging and pruning strategies often overlook spatial continuity and neighbor relationships, resulting in the loss of local context. This paper proposes novel neighbor-aware token reduction methods based on Hilbert curve reordering, which explicitly preserves the neighbor structure in a 2D space using 1D sequential representations. Our method introduces two key strategies: Neighbor-Aware Pruning (NAP) for selective token retention and Merging by Adjacent Token similarity (MAT) for local token aggregation. Experiments demonstrate that our approach achieves state-of-the-art accuracy-efficiency trade-offs compared to existing methods. This work highlights the importance of spatial continuity and neighbor structure, offering new insights for the architectural optimization of ViTs.
Two-Robot Computational Landscape: A Complete Characterization of Model Power in Minimal Mobile Robot Systems
Venue: arXiv cs.RO 2025
Year: 2025

The computational power of autonomous mobile robots under the Look-Compute-Move (LCM) model has been widely studied through an extensive hierarchy of robot models defined by the presence of memory, communication, and synchrony assumptions. While the general n-robot landscape has been largely established, the exact structure for two robots has remained unresolved. This paper presents the first complete characterization of the computational power of two autonomous robots across all major models, namely OBLOT, FSTA, FCOM, and LUMI, under the full spectrum of schedulers (FSYNCH, SSYNCH, ASYNCH, and their atomic variants). Our results reveal a landscape that fundamentally differs from the general case. Most notably, we prove that FSTA^F and LUMI^F coincide under full synchrony, a surprising collapse indicating that perfect synchrony can substitute both memory and communication when only two robots exist. We also show that FSTA and FCOM are orthogonal: there exists a problem solvable in the weakest communication model but impossible even in the strongest finite-state model, completing the bidirectional incomparability. All equivalence and separation results are derived through a novel simulation-free method, providing a unified and constructive view of the two-robot hierarchy. This yields the first complete and exact computational landscape for two robots, highlighting the intrinsic challenges of coordination at the minimal scale.
Plug In, Grade Right: Psychology-Inspired AGIQA
Venue: arXiv cs.CV 2025
Year: 2025

Existing AGIQA models typically estimate image quality by measuring and aggregating the similarities between image embeddings and text embeddings derived from multi-grade quality descriptions. Although effective, we observe that such similarity distributions across grades usually exhibit multimodal patterns. For instance, an image embedding may show high similarity to both "excellent" and "poor" grade descriptions while deviating from the "good" one. We refer to this phenomenon as "semantic drift", where semantic inconsistencies between text embeddings and their intended descriptions undermine the reliability of text-image shared-space learning. To mitigate this issue, we draw inspiration from psychometrics and propose an improved Graded Response Model (GRM) for AGIQA. The GRM is a classical assessment model that categorizes a subject's ability across grades using test items with various difficulty levels. This paradigm aligns remarkably well with human quality rating, where image quality can be interpreted as an image's ability to meet various quality grades. Building on this philosophy, we design a two-branch quality grading module: one branch estimates image ability while the other constructs multiple difficulty levels. To ensure monotonicity in difficulty levels, we further model difficulty generation in an arithmetic manner, which inherently enforces a unimodal and interpretable quality distribution. Our Arithmetic GRM based Quality Grading (AGQG) module enjoys a plug-and-play advantage, consistently improving performance when integrated into various state-of-the-art AGIQA frameworks. Moreover, it also generalizes effectively to both natural and screen content image quality assessment, revealing its potential as a key component in future IQA models.
CNSight: Evaluation of Clinical Note Segmentation Tools
Venue: arXiv cs.CL 2025
Year: 2025

Clinical notes are often stored in unstructured or semi-structured formats after extraction from electronic medical record (EMR) systems, which complicates their use for secondary analysis and downstream clinical applications. Reliable identification of section boundaries is a key step toward structuring these notes, as sections such as history of present illness, medications, and discharge instructions each provide distinct clinical contexts. In this work, we evaluate rule-based baselines, domain-specific transformer models, and large language models for clinical note segmentation using a curated dataset of 1,000 notes from MIMIC-IV. Our experiments show that large API-based models achieve the best overall performance, with GPT-5-mini reaching a best average F1 of 72.4 across sentence-level and freetext segmentation. Lightweight baselines remain competitive on structured sentence-level tasks but falter on unstructured freetext. Our results provide guidance for method selection and lay the groundwork for downstream tasks such as information extraction, cohort identification, and automated summarization.
ReDiF: Reinforced Distillation for Few Step Diffusion
Venue: arXiv cs.LG 2025
Year: 2025

Distillation addresses the slow sampling problem in diffusion models by creating models with smaller size or fewer steps that approximate the behavior of high-step teachers. In this work, we propose a reinforcement learning based distillation framework for diffusion models. Instead of relying on fixed reconstruction or consistency losses, we treat the distillation process as a policy optimization problem, where the student is trained using a reward signal derived from alignment with the teacher's outputs. This RL driven approach dynamically guides the student to explore multiple denoising paths, allowing it to take longer, optimized steps toward high-probability regions of the data distribution, rather than relying on incremental refinements. Our framework utilizes the inherent ability of diffusion models to handle larger steps and effectively manage the generative process. Experimental results show that our method achieves superior performance with significantly fewer inference steps and computational resources compared to existing distillation techniques. Additionally, the framework is model agnostic, applicable to any type of diffusion models with suitable reward functions, providing a general optimization paradigm for efficient diffusion learning.
Beyond Beam Sweeping: One-Shot Satellite Acquisition with Doppler-Aware Rainbow Beamforming
Venue: arXiv cs.IT 2025
Year: 2025

High-gain beamforming (BF) is essential for low Earth orbit (LEO) satellite communications to overcome severe path loss, but this requires acquiring precise satellite positions. Conventional satellite acquisition typically relies on time-domain beam sweeping, which incurs substantial overhead and latency. In this correspondence, we propose an efficient one-shot satellite acquisition framework that capitalizes on two phenomena traditionally regarded as impairments: i) Doppler effects and ii) beam-squint effects. Specifically, we derive a closed-form \emph{rainbow beamformer} that leverages beam-squint effects to align frequency-dependent beam directions with satellite positions inferred from their Doppler shifts. This approach enables reception from multiple satellites at once without requiring beam sweeping. To extract satellite position information, we develop three Doppler-aware angle estimation algorithms based on received signals. Simulation results demonstrate that the proposed method significantly outperforms conventional beam sweeping approaches in both acquisition accuracy and required time slots. These gains stem from the ability of the proposed rainbow BF to exploit the \emph{angle-dependent nature of Doppler shifts}, enabling full angular-domain coverage with a single pilot transmission and reception.
OrchANN: A Unified I/O Orchestration Framework for Skewed Out-of-Core Vector Search
Venue: arXiv cs.DB 2025
Year: 2025

Approximate nearest neighbor search (ANNS) at billion scale is fundamentally an out-of-core problem: vectors and indexes live on SSD, so performance is dominated by I/O rather than compute. Under skewed semantic embeddings, existing out-of-core systems break down: a uniform local index mismatches cluster scales, static routing misguides queries and inflates the number of probed partitions, and pruning is incomplete at the cluster level and lossy at the vector level, triggering "fetch-to-discard" reranking on raw vectors. We present OrchANN, an out-of-core ANNS engine that uses an I/O orchestration model for unified I/O governance along the route-access-verify pipeline. OrchANN selects a heterogeneous local index per cluster via offline auto-profiling, maintains a query-aware in-memory navigation graph that adapts to skewed workloads, and applies multi-level pruning with geometric bounds to filter both clusters and vectors before issuing SSD reads. Across five standard datasets under strict out-of-core constraints, OrchANN outperforms four baselines including DiskANN, Starling, SPANN, and PipeANN in both QPS and latency while reducing SSD accesses. Furthermore, OrchANN delivers up to 17.2x higher QPS and 25.0x lower latency than competing systems without sacrificing accuracy.
Many-valued coalgebraic dynamic logics: Safety and strong completeness via reducibility
Venue: arXiv cs.LO 2025
Year: 2025

We present a coalgebraic framework for studying generalisations of dynamic modal logics such as PDL and game logic in which both the propositions and the semantic structures can take values in an algebra $\mathbf{A}$ of truth-degrees. More precisely, we work with coalgebraic modal logic via $\mathbf{A}$-valued predicate liftings where $\mathbf{A}$ is a $\mathsf{FL}_{\mathrm{ew}}$-algebra, and interpret actions (abstracting programs and games) as $\mathsf{F}$-coalgebras where the functor $\mathsf{F}$ represents some type of $\mathbf{A}$-weighted system. We also allow combinations of crisp propositions with $\mathbf{A}$-weighted systems and vice versa. We introduce coalgebra operations and tests, with a focus on operations that are reducible in the sense that modalities for composed actions can be reduced to compositions of modalities for the constituent actions. We prove that reducible operations are safe for bisimulation and behavioural equivalence, and prove a general strong completeness result, from which we obtain new strong completeness results for $2$-valued iteration-free PDL with $\mathbf{A}$-valued accessibility relations when $\mathbf{A}$ is a finite chain, and for many-valued iteration-free game logic with many-valued strategies based on finite Lukasiewicz logic.
AutoForge: Automated Environment Synthesis for Agentic Reinforcement Learning
Venue: arXiv cs.CL 2025
Year: 2025

Conducting reinforcement learning (RL) in simulated environments offers a cost-effective and highly scalable way to enhance language-based agents. However, previous work has been limited to semi-automated environment synthesis or tasks lacking sufficient difficulty, offering little breadth or depth. In addition, the instability of simulated users integrated into these environments, along with the heterogeneity across simulated environments, poses further challenges for agentic RL. In this work, we propose: (1) a unified pipeline for automated and scalable synthesis of simulated environments associated with high-difficulty but easily verifiable tasks; and (2) an environment level RL algorithm that not only effectively mitigates user instability but also performs advantage estimation at the environment level, thereby improving training efficiency and stability. Comprehensive evaluations on agentic benchmarks, including tau-bench, tau2-Bench, and VitaBench, validate the effectiveness of our proposed method. Further in-depth analyses underscore its out-of-domain generalization.
Hash Grid Feature Pruning
Venue: arXiv cs.CV 2025
Year: 2025

Hash grids are widely used to learn an implicit neural field for Gaussian splatting, serving either as part of the entropy model or for inter-frame prediction. However, due to the irregular and non-uniform distribution of Gaussian splats in 3D space, numerous sparse regions exist, rendering many features in the hash grid invalid. This leads to redundant storage and transmission overhead. In this work, we propose a hash grid feature pruning method that identifies and prunes invalid features based on the coordinates of the input Gaussian splats, so that only the valid features are encoded. This approach reduces the storage size of the hash grid without compromising model performance, leading to improved rate-distortion performance. Following the Common Test Conditions (CTC) defined by the standardization committee, our method achieves an average bitrate reduction of 8% compared to the baseline approach.
Spatio-Temporal view on the Topological Functioning Model
Venue: arXiv cs.FL 2025
Year: 2025

The spatial and temporal aspects of system properties are crucial for many types of systems. In this short paper, we present a TopFunST framework to analyse topological dependencies among features of the system, covering also spatial and temporal aspects. TopFunM is based on an extended version of the language of Topological Functioning Models (TFM). The TFM language describes the topological relationships among functional features of the system, which allows not only to analyse system functionality, as well as feature interactions and functioning cycle structures, but spatial and temporal aspects haven't been covered yet. This paper presents a solution to this problem.
MetaCD: A Meta Learning Framework for Cognitive Diagnosis based on Continual Learning
Venue: arXiv cs.LG 2025
Year: 2025

Cognitive diagnosis is an essential research topic in intelligent education, aimed at assessing the level of mastery of different skills by students. So far, many research works have used deep learning models to explore the complex interactions between students, questions, and skills. However, the performance of existing method is frequently limited by the long-tailed distribution and dynamic changes in the data. To address these challenges, we propose a meta-learning framework for cognitive diagnosis based on continual learning (MetaCD). This framework can alleviate the long-tailed problem by utilizing meta-learning to learn the optimal initialization state, enabling the model to achieve good accuracy on new tasks with only a small amount of data. In addition, we utilize a continual learning method named parameter protection mechanism to give MetaCD the ability to adapt to new skills or new tasks, in order to adapt to dynamic changes in data. MetaCD can not only improve the plasticity of our model on a single task, but also ensure the stability and generalization of the model on sequential tasks. Comprehensive experiments on five real-world datasets show that MetaCD outperforms other baselines in both accuracy and generalization.
Geometric Structural Knowledge Graph Foundation Model
Venue: arXiv cs.AI 2025
Year: 2025

Structural knowledge graph foundation models aim to generalize reasoning to completely new graphs with unseen entities and relations. A key limitation of existing approaches like Ultra is their reliance on a single relational transformation (e.g., element-wise multiplication) in message passing, which can constrain expressiveness and fail to capture diverse relational and structural patterns exhibited on diverse graphs. In this paper, we propose Gamma, a novel foundation model that introduces multi-head geometric attention to knowledge graph reasoning. Gamma replaces the single relational transformation with multiple parallel ones, including real, complex, split-complex, and dual number based transformations, each designed to model different relational structures. A relational conditioned attention fusion mechanism then adaptively fuses them at link level via a lightweight gating with entropy regularization, allowing the model to robustly emphasize the most appropriate relational bias for each triple pattern. We present a full formalization of these algebraic message functions and discuss how their combination increases expressiveness beyond any single space. Comprehensive experiments on 56 diverse knowledge graphs demonstrate that Gamma consistently outperforms Ultra in zero-shot inductive link prediction, with a 5.5% improvement in mean reciprocal rank on the inductive benchmarks and a 4.4% improvement across all benchmarks, highlighting benefits from complementary geometric representations.
Generalized Hyperderivative Reed-Solomon Codes
Venue: arXiv cs.IT 2025
Year: 2025

This article introduces Generalized Hyperderivative Reed-Solomon codes (GHRS codes), which generalize NRT Reed-Solomon codes. Its main results are as follows: 1) every GHRS code is MDS, 2) the dual of a GHRS code is also an GHRS code, 3) determine subfamilies of GHRS codes whose members are low-density parity-check codes (LDPCs), and 4) determine a family of GHRS codes whose members are quasi-cyclic. We point out that there are GHRS codes having all of these properties.
Spatial-aware Symmetric Alignment for Text-guided Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Text-guided Medical Image Segmentation has shown considerable promise for medical image segmentation, with rich clinical text serving as an effective supplement for scarce data. However, current methods have two key bottlenecks. On one hand, they struggle to process diagnostic and descriptive texts simultaneously, making it difficult to identify lesions and establish associations with image regions. On the other hand, existing approaches focus on lesions description and fail to capture positional constraints, leading to critical deviations. Specifically, with the text "in the left lower lung", the segmentation results may incorrectly cover both sides of the lung. To address the limitations, we propose the Spatial-aware Symmetric Alignment (SSA) framework to enhance the capacity of referring hybrid medical texts consisting of locational, descriptive, and diagnostic information. Specifically, we propose symmetric optimal transport alignment mechanism to strengthen the associations between image regions and multiple relevant expressions, which establishes bi-directional fine-grained multimodal correspondences. In addition, we devise a composite directional guidance strategy that explicitly introduces spatial constraints in the text by constructing region-level guidance masks. Extensive experiments on public benchmarks demonstrate that SSA achieves state-of-the-art (SOTA) performance, particularly in accurately segmenting lesions characterized by spatial relational constraints.