OpenRaiser/Intern-Atlas huggingface dataset:
Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-language models (VLM) excel at general understanding yet remain weak at dynamic spatial reasoning (DSR), i.e., reasoning about the evolvement of object geometry and relationship in 3D space over time, largely due to the scarcity of scalable 4D-aware training resources. To bridge this gap across aspects of dataset, benchmark and model, we introduce DSR Suite. First, we propose an automated pipeline that generates multiple-choice question-answer pairs from in-the-wild videos for DSR. By leveraging modern vision foundation models, the pipeline extracts rich geometric and motion information, including camera poses, local point clouds, object masks, orientations, and 3D trajectories. These geometric cues enable the construction of DSR-Train for learning and further human-refined DSR-Bench for evaluation. Compared with previous works, our data emphasize (i) in-the-wild video sources, (ii) object- and scene-level 3D requirements, (iii) viewpoint transformations, (iv) multi-object interactions, and (v) fine-grained, procedural answers. Beyond data, we propose a lightweight Geometry Selection Module (GSM) to seamlessly integrate geometric priors into VLMs, which condenses question semantics and extracts question-relevant knowledge from pretrained 4D reconstruction priors into a compact set of geometry tokens. This targeted extraction avoids overwhelming the model with irrelevant knowledge. Experiments show that integrating DSR-Train and GSM into Qwen2.5-VL-7B significantly enhances its dynamic spatial reasoning capability, while maintaining accuracy on general video understanding benchmarks.
FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models
Venue: arXiv cs.CV 2025
Year: 2025

Large vision-language models (VLMs) typically process hundreds or thousands of visual tokens per image or video frame, incurring quadratic attention cost and substantial redundancy. Existing token reduction methods often ignore the textual query or rely on deep attention maps, whose instability under aggressive pruning leads to degraded semantic alignment. We propose FlashVLM, a text guided visual token selection framework that dynamically adapts visual inputs to the query. Instead of relying on noisy attention weights, FlashVLM computes an explicit cross modal similarity between projected image tokens and normalized text embeddings in the language model space. This extrinsic relevance is fused with intrinsic visual saliency using log domain weighting and temperature controlled sharpening. In addition, a diversity preserving partition retains a minimal yet representative set of background tokens to maintain global context. Under identical token budgets and evaluation protocols, FlashVLM achieves beyond lossless compression, slightly surpassing the unpruned baseline while pruning up to 77.8 percent of visual tokens on LLaVA 1.5, and maintaining 92.8 percent accuracy even under 94.4 percent compression. Extensive experiments on 14 image and video benchmarks demonstrate that FlashVLM delivers state of the art efficiency performance trade offs while maintaining strong robustness and generalization across mainstream VLMs.
Automated stereotactic radiosurgery planning using a human-in-the-loop reasoning large language model agent
Venue: arXiv cs.AI 2025
Year: 2025

Stereotactic radiosurgery (SRS) demands precise dose shaping around critical structures, yet black-box AI systems have limited clinical adoption due to opacity concerns. We tested whether chain-of-thought reasoning improves agentic planning in a retrospective cohort of 41 patients with brain metastases treated with 18 Gy single-fraction SRS. We developed SAGE (Secure Agent for Generative Dose Expertise), an LLM-based planning agent for automated SRS treatment planning. Two variants generated plans for each case: one using a non-reasoning model, one using a reasoning model. The reasoning variant showed comparable plan dosimetry relative to human planners on primary endpoints (PTV coverage, maximum dose, conformity index, gradient index; all p > 0.21) while reducing cochlear dose below human baselines (p = 0.022). When prompted to improve conformity, the reasoning model demonstrated systematic planning behaviors including prospective constraint verification (457 instances) and trade-off deliberation (609 instances), while the standard model exhibited none of these deliberative processes (0 and 7 instances, respectively). Content analysis revealed that constraint verification and causal explanation concentrated in the reasoning agent. The optimization traces serve as auditable logs, offering a path toward transparent automated planning.
Repurposing Video Diffusion Transformers for Robust Point Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Point tracking aims to localize corresponding points across video frames, serving as a fundamental task for 4D reconstruction, robotics, and video editing. Existing methods commonly rely on shallow convolutional backbones such as ResNet that process frames independently, lacking temporal coherence and producing unreliable matching costs under challenging conditions. Through systematic analysis, we find that video Diffusion Transformers (DiTs), pre-trained on large-scale real-world videos with spatio-temporal attention, inherently exhibit strong point tracking capability and robustly handle dynamic motions and frequent occlusions. We propose DiTracker, which adapts video DiTs through: (1) query-key attention matching, (2) lightweight LoRA tuning, and (3) cost fusion with a ResNet backbone. Despite training with 8 times smaller batch size, DiTracker achieves state-of-the-art performance on challenging ITTO benchmark and matches or outperforms state-of-the-art models on TAP-Vid benchmarks. Our work validates video DiT features as an effective and efficient foundation for point tracking.
Making Large Language Models Efficient Dense Retrievers
Venue: arXiv cs.IR 2025
Year: 2025

Recent work has shown that directly fine-tuning large language models (LLMs) for dense retrieval yields strong performance, but their substantial parameter counts make them computationally inefficient. While prior studies have revealed significant layer redundancy in LLMs for generative tasks, it remains unclear whether similar redundancy exists when these models are adapted for retrieval tasks, which require encoding entire sequences into fixed representations rather than generating tokens iteratively. To this end, we conduct a comprehensive analysis of layer redundancy in LLM-based dense retrievers. We find that, in contrast to generative settings, MLP layers are substantially more prunable, while attention layers remain critical for semantic aggregation. Building on this insight, we propose EffiR, a framework for developing efficient retrievers that performs large-scale MLP compression through a coarse-to-fine strategy (coarse-grained depth reduction followed by fine-grained width reduction), combined with retrieval-specific fine-tuning. Across diverse BEIR datasets and LLM backbones, EffiR achieves substantial reductions in model size and inference cost while preserving the performance of full-size models.
Uncovering Patterns of Brain Activity from EEG Data Consistently Associated with Cybersickness Using Neural Network Interpretability Maps
Venue: arXiv cs.HC 2025
Year: 2025

Cybersickness poses a serious challenge for users of virtual reality (VR) technology. Consequently, there has been significant effort to track its occurrence during VR use with brain activity through electroencephalography (EEG). However, a significant confound in current methods for detecting sickness from EEG is they do not account for the simultaneous processing of the sickening visual stimulus that is present in the brain data from VR. Using event-related potentials (ERPs) from an auditory stimulus shown to reflect cybersickness impacts, we can more precisely target EEG cybersickness features and use those to achieve better performance in online cybersickness classification. In this article, we introduce a method utilizing trained convolutional neural networks and transformer models and plot interpretability maps from integrated gradients and class activation to give a visual representation of what the model determined was most useful in sickness classification from an EEG dataset consisting of ERPs recorded during the elicitation of cybersickness. Across 12 runs of our method with three different neural networks, the models consistently pointed to a surprising finding: that amplitudes recorded at an electrode placed on the scalp near the left prefrontal cortex were important in the classification of cybersickness. These results help clarify a hidden pattern in other related research and point to exciting opportunities for future investigation: that this scalp location could be used as a tagged feature for better real-time cybersickness classification with EEG. We provide our code at: [anonymized].
MILP-driven Network Planning Framework for Energy Efficiency and Coverage Maximization in IoT Mesh Networks
Venue: arXiv cs.NI 2025
Year: 2025

In the era of digital transformation, the global deployment of internet of things (IoT) networks and wireless sensor networks (WSNs) is critical for applications ranging from environmental monitoring to smart cities. Large-scale monitoring using WSNs incurs high costs due to the deployment of sensor nodes in the target deployment area. In this paper, we address the challenge of prohibitive deployment costs by proposing an integrated mixed-Integer linear programming (MILP) framework that strategically combines static and mobile Zigbee nodes. Our network planning approach introduces three novel formulations, including boundary-optimized static node placement (MILP-Static), mobile path planning for coverage maximization (MILP-Cov), and movement minimization (MILP-Mov) of the mobile nodes. We validated our framework with extensive simulations and experimental measurements of Zigbee power constraints. Our results show that boundary-optimized static placement (MILP-Static) achieves 53.06% coverage compared with 33.42% of the random approach. In addition, MILP-Cov for path planning reaches 97.95% coverage, while movement minimization (MILP-Mov) reduces traversal cost by 40%. Our proposed framework outperforms the benchmark approaches to provide a foundational solution for cost-effective global IoT deployment in resource constrained environments.
Graph Neural Networks for Source Detection: A Review and Benchmark Study
Venue: arXiv cs.SI 2025
Year: 2025

The source detection problem arises when an epidemic process unfolds over a contact network, and the objective is to identify its point of origin, i.e., the source node. Research on this problem began with the seminal work of Shah and Zaman in 2010, who formally defined it and introduced the notion of rumor centrality. With the emergence of Graph Neural Networks (GNNs), several studies have proposed GNN-based approaches to source detection. However, some of these works lack methodological clarity and/or are hard to reproduce. As a result, it remains unclear (to us, at least) whether GNNs truly outperform more traditional source detection methods across comparable settings. In this paper, we first review existing GNN-based methods for source detection, clearly outlining the specific settings each addresses and the models they employ. Building on this research, we propose a principled GNN architecture tailored to the source detection task. We also systematically investigate key questions surrounding this problem. Most importantly, we aim to provide a definitive assessment of how GNNs perform relative to other source detection methods. Our experiments show that GNNs substantially outperform all other methods we test across a variety of network types. Although we initially set out to challenge the notion of GNNs as a solution to source detection, our results instead demonstrate their remarkable effectiveness for this task. We discuss possible reasons for this strong performance. To ensure full reproducibility, we release all code and data on GitHub. Finally, we argue that epidemic source detection should serve as a benchmark task for evaluating GNN architectures.
Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection
Venue: arXiv cs.LG 2025
Year: 2025

Prevalent multimodal fake news detection relies on consistency-based fusion, yet this paradigm fundamentally misinterprets critical cross-modal discrepancies as noise, leading to over-smoothing, which dilutes critical evidence of fabrication. Mainstream consistency-based fusion inherently minimizes feature discrepancies to align modalities, yet this approach fundamentally fails because it inadvertently smoothes out the subtle cross-modal contradictions that serve as the primary evidence of fabrication. To address this, we propose the Dynamic Conflict-Consensus Framework (DCCF), an inconsistency-seeking paradigm designed to amplify rather than suppress contradictions. First, DCCF decouples inputs into independent Fact and Sentiment spaces to distinguish objective mismatches from emotional dissonance. Second, we employ physics-inspired feature dynamics to iteratively polarize these representations, actively extracting maximally informative conflicts. Finally, a conflict-consensus mechanism standardizes these local discrepancies against the global context for robust deliberative judgment.Extensive experiments conducted on three real world datasets demonstrate that DCCF consistently outperforms state-of-the-art baselines, achieving an average accuracy improvement of 3.52\%.
Bridging the AI Trustworthiness Gap between Functions and Norms
Venue: arXiv cs.AI 2025
Year: 2025

Trustworthy Artificial Intelligence (TAI) is gaining traction due to regulations and functional benefits. While Functional TAI (FTAI) focuses on how to implement trustworthy systems, Normative TAI (NTAI) focuses on regulations that need to be enforced. However, gaps between FTAI and NTAI remain, making it difficult to assess trustworthiness of AI systems. We argue that a bridge is needed, specifically by introducing a conceptual language which can match FTAI and NTAI. Such a semantic language can assist developers as a framework to assess AI systems in terms of trustworthiness. It can also help stakeholders translate norms and regulations into concrete implementation steps for their systems. In this position paper, we describe the current state-of-the-art and identify the gap between FTAI and NTAI. We will discuss starting points for developing a semantic language and the envisioned effects of it. Finally, we provide key considerations and discuss future actions towards assessment of TAI.
Revisiting the Learning Objectives of Vision-Language Reward Models
Venue: arXiv cs.LG 2025
Year: 2025

Learning generalizable reward functions is a core challenge in embodied intelligence. Recent work leverages contrastive vision language models (VLMs) to obtain dense, domain-agnostic rewards without human supervision. These methods adapt VLMs into reward models through increasingly complex learning objectives, yet meaningful comparison remains difficult due to differences in training data, architectures, and evaluation settings. In this work, we isolate the impact of the learning objective by evaluating recent VLM-based reward models under a unified framework with identical backbones, finetuning data, and evaluation environments. Using Meta-World tasks, we assess modeling accuracy by measuring consistency with ground truth reward and correlation with expert progress. Remarkably, we show that a simple triplet loss outperforms state-of-the-art methods, suggesting that much of the improvements in recent approaches could be attributed to differences in data and architectures.
Anytime Metaheuristic Framework for Global Route Optimization in Expected-Time Mobile Search
Venue: arXiv cs.RO 2025
Year: 2025

Expected-time mobile search (ETS) is a fundamental robotics task where a mobile sensor navigates an environment to minimize the expected time required to locate a hidden object. Global route optimization for ETS in static 2D continuous environments remains largely underexplored due to the intractability of objective evaluation, stemming from the continuous nature of the environment and the interplay of motion and visibility constraints. Prior work has addressed this through partial discretization, leading to discrete-sensing formulations tackled via utility-greedy heuristics. Others have taken an indirect approach by heuristically approximating the objective using minimum latency problems on fixed graphs, enabling global route optimization via efficient metaheuristics. This paper builds on and significantly extends the latter by introducing Milaps (Minimum latency problems), a model-based solution framework for ETS. Milaps integrates novel auxiliary objectives and adapts a recent anytime metaheuristic for the traveling deliveryman problem, chosen for its strong performance under tight runtime constraints. Evaluations on a novel large-scale dataset demonstrate superior trade-offs between solution quality and runtime compared to state-of-the-art baselines. The best-performing strategy rapidly generates a preliminary solution, assigns static weights to sensing configurations, and optimizes global costs metaheuristically. Additionally, a qualitative study highlights the framework's flexibility across diverse scenarios.
From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education
Venue: arXiv cs.AI 2025
Year: 2025

Generative AI enables personalized computer science education at scale, yet questions remain about whether such personalization supports or undermines learning. This scoping review synthesizes 32 studies (2023-2025) purposively sampled from 259 records to map personalization mechanisms and effectiveness signals in higher-education computer science contexts. We identify five application domains: intelligent tutoring, personalized materials, formative feedback, AI-augmented assessment, and code review, and analyze how design choices shape learning outcomes. Designs incorporating explanation-first guidance, solution withholding, graduated hint ladders, and artifact grounding (student code, tests, and rubrics) consistently show more positive learning processes than unconstrained chat interfaces. Successful implementations share four patterns: context-aware tutoring anchored in student artifacts, multi-level hint structures requiring reflection, composition with traditional CS infrastructure (autograders and rubrics), and human-in-the-loop quality assurance. We propose an exploration-first adoption framework emphasizing piloting, instrumentation, learning-preserving defaults, and evidence-based scaling. Recurrent risks include academic integrity, privacy, bias and equity, and over-reliance, and we pair these with operational mitigation. The evidence supports generative AI as a mechanism for precision scaffolding when embedded in audit-ready workflows that preserve productive struggle while scaling personalized support.
SA-DiffuSeq: Addressing Computational and Scalability Challenges in Long-Document Generation with Sparse Attention
Venue: arXiv cs.CL 2025
Year: 2025

Diffusion based approaches to long form text generation suffer from prohibitive computational cost and memory overhead as sequence length increases. We introduce SA-DiffuSeq, a diffusion framework that integrates sparse attention to fundamentally improve scalability for long document modeling. By selectively allocating attention within the diffusion process, SA-DiffuSeq significantly reduces computational complexity while maintaining semantic coherence and generation quality. A key component of our method is a soft absorbing state tailored to sparse attention dynamics, which stabilizes diffusion trajectories and accelerates sequence reconstruction. This design improves sampling efficiency and enhances precision in long range dependency modeling. Extensive experiments demonstrate that SA-DiffuSeq consistently surpasses state of the art diffusion baselines in both training efficiency and sampling speed, with especially strong gains on extended sequences. These properties make SA-DiffuSeq well suited for demanding long form applications such as scientific writing, large scale code generation, and multi turn long context dialogue. Overall, our results indicate that incorporating structured sparsity into diffusion models is a promising direction for efficient and expressive long text generation.
FEM-Bench: A Structured Scientific Reasoning Benchmark for Evaluating Code-Generating LLMs
Venue: arXiv cs.LG 2025
Year: 2025

As LLMs advance their reasoning capabilities about the physical world, the absence of rigorous benchmarks for evaluating their ability to generate scientifically valid physical models has become a critical gap. Computational mechanics, which develops and applies mathematical models and numerical methods to predict the behavior of physical systems under forces, deformation, and constraints, provides an ideal foundation for structured scientific reasoning evaluation. Problems follow clear mathematical structure, enforce strict physical and numerical constraints, and support objective verification. The discipline requires constructing explicit models of physical systems and reasoning about geometry, spatial relationships, and material behavior, connecting directly to emerging AI goals in physical reasoning and world modeling. We introduce FEM-Bench, a computational mechanics benchmark designed to evaluate the ability of LLMs to generate correct finite element method (FEM) and related code. FEM-Bench 2025 contains a suite of introductory but nontrivial tasks aligned with material from a first graduate course on computational mechanics. These tasks capture essential numerical and physical modeling challenges while representing only a small fraction of the complexity present in the discipline. Despite their simplicity, state-of-the-art LLMs do not reliably solve all of them. In a five attempt run, the best performing model at function writing, Gemini 3 Pro, completed 30/33 tasks at least once and 26/33 tasks all five times. The best performing model at unit test writing, GPT-5, had an Average Joint Success Rate of 73.8%. Other popular models showed broad performance variation. FEM-Bench establishes a structured foundation for evaluating AI-generated scientific code, and future iterations will incorporate increasingly sophisticated tasks to track progress as models evolve.
TrashDet: Iterative Neural Architecture Search for Efficient Waste Detection
Venue: arXiv cs.CV 2025
Year: 2025

This paper addresses trash detection on the TACO dataset under strict TinyML constraints using an iterative hardware-aware neural architecture search framework targeting edge and IoT devices. The proposed method constructs a Once-for-All-style ResDets supernet and performs iterative evolutionary search that alternates between backbone and neck/head optimization, supported by a population passthrough mechanism and an accuracy predictor to reduce search cost and improve stability. This framework yields a family of deployment-ready detectors, termed TrashDets. On a five-class TACO subset (paper, plastic, bottle, can, cigarette), the strongest variant, TrashDet-l, achieves 19.5 mAP50 with 30.5M parameters, improving accuracy by up to 3.6 mAP50 over prior detectors while using substantially fewer parameters. The TrashDet family spans 1.2M to 30.5M parameters with mAP50 values between 11.4 and 19.5, providing scalable detector options for diverse TinyML deployment budgets on resource-constrained hardware. On the MAX78002 microcontroller with the TrashNet dataset, two specialized variants, TrashDet-ResNet and TrashDet-MBNet, jointly dominate the ai87-fpndetector baseline, with TrashDet-ResNet achieving 7525~$μ$J energy per inference at 26.7 ms latency and 37.45 FPS, and TrashDet-MBNet improving mAP50 by 10.2%; together they reduce energy consumption by up to 88%, latency by up to 78%, and average power by up to 53% compared to existing TinyML detectors.