OpenRaiser/Intern-Atlas huggingface dataset:
ArmFormer: Lightweight Transformer Architecture for Real-Time Multi-Class Weapon Segmentation and Classification
Venue: arXiv cs.CV 2025
Year: 2025

The escalating threat of weapon-related violence necessitates automated detection systems capable of pixel-level precision for accurate threat assessment in real-time security applications. Traditional weapon detection approaches rely on object detection frameworks that provide only coarse bounding box localizations, lacking the fine-grained segmentation required for comprehensive threat analysis. Furthermore, existing semantic segmentation models either sacrifice accuracy for computational efficiency or require excessive computational resources incompatible with edge deployment scenarios. This paper presents ArmFormer, a lightweight transformer-based semantic segmentation framework that strategically integrates Convolutional Block Attention Module (CBAM) with MixVisionTransformer architecture to achieve superior accuracy while maintaining computational efficiency suitable for resource-constrained edge devices. Our approach combines CBAM-enhanced encoder backbone with attention-integrated hamburger decoder to enable multi-class weapon segmentation across five categories: handgun, rifle, knife, revolver, and human. Comprehensive experiments demonstrate that ArmFormer achieves state-of-the-art performance with 80.64% mIoU and 89.13% mFscore while maintaining real-time inference at 82.26 FPS. With only 4.886G FLOPs and 3.66M parameters, ArmFormer outperforms heavyweight models requiring up to 48x more computation, establishing it as the optimal solution for deployment on portable security cameras, surveillance drones, and embedded AI accelerators in distributed security infrastructure.
Sustainable and Adaptive Growth in Computing Education
Venue: arXiv cs.CY 2025
Year: 2025

Computing Education faces significant challenges in equipping graduates with the resilience necessary to remain relevant amid rapid technological change. While existing curricula cultivate computing competencies, they often fail to integrate strategies for sustaining and adapting these skills, leading to reduced career resilience and recurrent industry layoffs. The lack of educational emphasis on sustainability and adaptability amid industry changes perpetuates a vicious cycle: As industries shift, skill fragmentation and decay lead to displacement, which in turn causes further skill degradation. The ongoing deficiency in adaptability and sustainability among learners is reflected in the frequent and intense shifts across the industry. This issue is particularly evident in domains marked by high technological volatility such as computer graphics and game development, where computing concepts, including computational thinking and performance optimization, are uniquely and continuously challenged. To foster sustainable and adaptive growth, this paper introduces, a new framework which addresses the question: How can computing education and professional development be connected to in these volatile sectors? It integrates two iterative, interconnected cycles, an educational and a professional, by linking education with profession to establish a lifelong, renewable practice. This approach allows computing professionals to excel and maintain relevance amid constant changes across their industry.
Chem-R: Learning to Reason as a Chemist
Venue: arXiv cs.CE 2025
Year: 2025

Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories, and exhibit suboptimal performance across diverse chemical tasks. To address these challenges, we propose Chem-R, a generalizable Chemical Reasoning model designed to emulate the deliberative processes of chemists. Chem-R is trained through a three-phase framework that progressively builds advanced reasoning capabilities, including: 1) Chemical Foundation Training, which establishes core chemical knowledge. 2) Chemical Reasoning Protocol Distillation, incorporating structured, expert-like reasoning traces to guide systematic and reliable problem solving. 3) Multi-task Group Relative Policy Optimization that optimizes the model for balanced performance across diverse molecular- and reaction-level tasks. This structured pipeline enables Chem-R to achieve state-of-the-art performance on comprehensive benchmarks, surpassing leading large language models, including Gemini-2.5-Pro and DeepSeek-R1, by up to 32% on molecular tasks and 48% on reaction tasks. Meanwhile, Chem-R also consistently outperforms the existing chemical foundation models across both molecular and reaction level tasks. These results highlight Chem-R's robust generalization, interpretability, and potential as a foundation for next-generation AI-driven chemical discovery. The code and model are available at https://github.com/davidweidawang/Chem-R.
JAX Autodiff from a Linear Logic Perspective (Extended Version)
Venue: arXiv cs.PL 2025
Year: 2025

Autodiff refers to the core of the automatic differentiation systems developed in projects like JAX and Dex. Autodiff has recently been formalised in a linear typed calculus by Radul et al in arXiv:2204.10923. Although this formalisation suffices to express the main program transformations of Autodiff, the calculus is very specific to this task, and it is not clear whether the type system yields a substructural logic that has interest on its own. We propose an encoding of Autodiff into a linear $λ$-calculus that enjoys a Curry-Howard correspondence with Girard's linear logic. We prove that the encoding is sound both qualitatively (the encoded terms are extensionally equivalent to the original ones) and quantitatively (the encoding preserves the original work cost as described in arXiv:2204.10923). As a byproduct, we show that unzipping, one of the transformations used to implement backpropagation in Autodiff, is, in fact, optional.
Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback
Venue: arXiv cs.CV 2025
Year: 2025

Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their ability to explore and generalize beyond training distributions. To this end, we introduce Edit-R1, a novel post-training framework for instruction-based image editing based on policy optimization. Specifically, we utilize Diffusion Negative-aware Finetuning (DiffusionNFT), a likelihood-free policy optimization method consistent with the flow matching forward process, thereby enabling the use of higher-order samplers and more efficient training. Another key challenge here is the absence of a universal reward model, resulting from the diverse nature of editing instructions and tasks. To bridge this gap, we employ a Multimodal Large Language Model (MLLM) as a unified, training-free reward model, leveraging its output logits to provide fine-grained feedback. Furthermore, we carefully design a low-variance group filtering mechanism to reduce MLLM scoring noise and stabilize optimization. \texttt{UniWorld-V2}, trained with this framework, achieves \textbf{state-of-the-art} results on the ImgEdit and GEdit-Bench benchmarks, scoring 4.49 and 7.83, respectively. Crucially, our framework is model-agnostic, delivering substantial performance gains when applied to diverse base models like Qwen-Image-Edit and FLUX-Kontext, demonstrating its wide applicability. Code and models are publicly available to support further research.
Investigating Safety Vulnerabilities of Large Audio-Language Models Under Speaker Emotional Variations
Venue: arXiv cs.SD 2025
Year: 2025

Large audio-language models (LALMs) extend text-based LLMs with auditory understanding, offering new opportunities for multimodal applications. While their perception, reasoning, and task performance have been widely studied, their safety alignment under paralinguistic variation remains underexplored. This work systematically investigates the role of speaker emotion. We construct a dataset of malicious speech instructions expressed across multiple emotions and intensities, and evaluate several state-of-the-art LALMs. Our results reveal substantial safety inconsistencies: different emotions elicit varying levels of unsafe responses, and the effect of intensity is non-monotonic, with medium expressions often posing the greatest risk. These findings highlight an overlooked vulnerability in LALMs and call for alignment strategies explicitly designed to ensure robustness under emotional variation, a prerequisite for trustworthy deployment in real-world settings.
Local News Hijacking: A Review of International Instances
Venue: arXiv cs.CY 2025
Year: 2025

In the rise of the digital era, it's easier than ever to create nefarious websites to spread misinformation. A more recent phenomenon in the United States has been the creation of inauthentic local news websites to further an information operation campaign. This paper is a review of the 7 instances in which local news websites were created to influence residents of a region between 2007 and 2024. By breaking down the ways in which these sites operated, we discovered commonalities in the approach - resurrecting "zombie" papers that were previously established authentic local news organizations, sharing these sites on social media, and using website templates from WordPress. By analyzing these commonalities, we propose ways to mitigate the occurrence of these campaigns in the future.
Real-Time World Crafting: Generating Structured Game Behaviors from Natural Language with Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

We present a novel architecture for safely integrating Large Language Models (LLMs) into interactive game engines, allowing players to "program" new behaviors using natural language. Our framework mitigates risks by using an LLM to translate commands into a constrained Domain-Specific Language (DSL), which configures a custom Entity-Component-System (ECS) at runtime. We evaluated this system in a 2D spell-crafting game prototype by experimentally assessing models from the Gemini, GPT, and Claude families with various prompting strategies. A validated LLM judge qualitatively rated the outputs, showing that while larger models better captured creative intent, the optimal prompting strategy is task-dependent: Chain-of-Thought improved creative alignment, while few-shot examples were necessary to generate more complex DSL scripts. This work offers a validated LLM-ECS pattern for emergent gameplay and a quantitative performance comparison for developers.
Lark: Biologically Inspired Neuroevolution for Multi-Stakeholder LLM Agents
Venue: arXiv cs.MA 2025
Year: 2025

We present Lark, a biologically inspired decision-making framework that couples LLM-driven reasoning with an evolutionary, stakeholder-aware Multi-Agent System (MAS). To address verbosity and stakeholder trade-offs, we integrate four mechanisms: (i) plasticity, which applies concise adjustments to candidate solutions; (ii) duplication and maturation, which copy high-performing candidates and specialize them into new modules; (iii) ranked-choice stakeholder aggregation using influence-weighted Borda scoring; and (iv) compute awareness via token-based penalties that reward brevity. The system iteratively proposes diverse strategies, applies plasticity tweaks, simulates stakeholder evaluations, aggregates preferences, selects top candidates, and performs duplication/maturation while factoring compute cost into final scores. In a controlled evaluation over 30 rounds comparing 14 systems, Lark Full achieves a mean rank of 2.55 (95% CI [2.17, 2.93]) and a mean composite score of 29.4/50 (95% CI [26.34, 32.46]), finishing Top-3 in 80% of rounds while remaining cost competitive with leading commercial models ($0.016 per task). Paired Wilcoxon tests confirm that all four mechanisms contribute significantly as ablating duplication/maturation yields the largest deficit (ΔScore = 3.5, Cohen's d_z = 2.53, p < 0.001), followed by plasticity (ΔScore = 3.4, d_z = 1.86), ranked-choice voting (ΔScore = 2.4, d_z = 1.20), and token penalties (ΔScore = 2.2, d_z = 1.63). Rather than a formal Markov Decision Process with constrained optimization, Lark is a practical, compute-aware neuroevolutionary loop that scales stakeholder-aligned strategy generation and makes trade-offs transparent through per-step metrics. Our work presents proof-of-concept findings and invites community feedback as we expand toward real-world validation studies.
MuonBP: Faster Muon via Block-Periodic Orthogonalization
Venue: arXiv cs.LG 2025
Year: 2025

Gradient orthogonalization is a simple strategy that shows great utility in speeding up gradient descent. The Muon optimizer (Jordan, Jin, et al., 2024) combines gradient orthogonalization with first-order momentum and achieves significant improvement in data efficiency over Adam/AdamW (Loshchilov and Hutter, 2019) for language model training. However, when using model parallelism, gradient orthogonalization introduces additional overhead compared to coordinate-wise optimizers (such as AdamW) due to additional gather and scatter operations on gradient matrix shards from different devices. This additional communication can amount to a throughput hit of 5%-10% compared to Adam/AdamW. To remedy this, we propose Muon with Block-Periodic Orthogonalization (MuonBP), which applies orthogonalization independently to matrix shards on each device and periodically performs full orthogonalization to maintain training stability at scale. We show how to adjust the learning rate from the baseline to MuonBP and give convergence guarantees for this algorithm. Crucially, our theory dictates that we use two stepsizes: one for the blockwise orthogonalization steps, and one for the full orthogonalization steps. Our method is simple, requires minimal hyperparameter adjustments, and achieves competitive iteration complexity compared with baseline Muon while providing per-iteration throughput comparable to coordinate-wise methods such as AdamW. When training an 8B model with eight-way tensor parallelism and ZeRO optimizer state sharding, MuonBP achieves 8% throughput increase compared to Muon with no degradation in performance.
Integrating Metaverse Technologies in Medical Education: Examining Acceptance Factors Among Current and Future Healthcare Providers
Venue: arXiv cs.HC 2025
Year: 2025

This study investigates behavioral intention to use healthcare metaverse platforms among medical students and physicians in Turkey, where such technologies are in early stages of adoption. A multi-theoretical research model was developed by integrating constructs from the Innovation Diffusion Theory, Embodied Social Presence Theory, Interaction Equivalency Theorem and Technology Acceptance Model. Data from 718 participants were analyzed using partial least squares structural equation modeling. Results show that satisfaction, perceived usefulness, perceived ease of use, learner interactions, and technology readiness significantly enhance adoption, while technology anxiety and complexity have negative effects. Learner learner and learner teacher interactions strongly predict satisfaction, which subsequently increases behavioral intention. Perceived ease of use fully mediates the relationship between technology anxiety and perceived usefulness. However, technology anxiety does not significantly moderate the effects of perceived usefulness or ease of use on behavioral intention. The model explains 71.8% of the variance in behavioral intention, indicating strong explanatory power. The findings offer practical implications for educators, curriculum designers, and developers aiming to integrate metaverse platforms into healthcare training in digitally transitioning educational systems.
Parameter-Efficient Fine-Tuning for Low-Resource Languages: A Comparative Study of LLMs for Bengali Hate Speech Detection
Venue: arXiv cs.CL 2025
Year: 2025

Bengali social media platforms have witnessed a sharp increase in hate speech, disproportionately affecting women and adolescents. While datasets such as BD-SHS provide a basis for structured evaluation, most prior approaches rely on either computationally costly full-model fine-tuning or proprietary APIs. This paper presents the first application of Parameter-Efficient Fine-Tuning (PEFT) for Bengali hate speech detection using LoRA and QLoRA. Three instruction-tuned large language models - Gemma-3-4B, Llama-3.2-3B, and Mistral-7B - were fine-tuned on the BD-SHS dataset of 50,281 annotated comments. Each model was adapted by training fewer than 1% of its parameters, enabling experiments on a single consumer-grade GPU. The results show that Llama-3.2-3B achieved the highest F1-score of 92.23%, followed by Mistral-7B at 88.94% and Gemma-3-4B at 80.25%. These findings establish PEFT as a practical and replicable strategy for Bengali and related low-resource languages.
STARK: Strategic Team of Agents for Refining Kernels
Venue: arXiv cs.AI 2025
Year: 2025

The efficiency of GPU kernels is central to the progress of modern AI, yet optimizing them remains a difficult and labor-intensive task due to complex interactions between memory hierarchies, thread scheduling, and hardware-specific characteristics. While recent advances in large language models (LLMs) provide new opportunities for automated code generation, existing approaches largely treat LLMs as single-shot generators or naive refinement tools, limiting their effectiveness in navigating the irregular kernel optimization landscape. We introduce an LLM agentic framework for GPU kernel optimization that systematically explores the design space through multi-agent collaboration, grounded instruction, dynamic context management, and strategic search. This framework mimics the workflow of expert engineers, enabling LLMs to reason about hardware trade-offs, incorporate profiling feedback, and refine kernels iteratively. We evaluate our approach on KernelBench, a benchmark for LLM-based kernel optimization, and demonstrate substantial improvements over baseline agents: our system produces correct solutions where baselines often fail, and achieves kernels with up to 16x faster runtime performance. These results highlight the potential of agentic LLM frameworks to advance fully automated, scalable GPU kernel optimization.
Online Learning Defense against Iterative Jailbreak Attacks via Prompt Optimization
Venue: arXiv cs.CL 2025
Year: 2025

Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the model's previous responses to guide each new iteration -- have been found to be a highly effective attack strategy. Despite being an effective attack strategy against LLMs and their safety mechanisms, existing defenses do not proactively disrupt this dynamic trial-and-error cycle. In this study, we propose a novel framework that dynamically updates its defense strategy through online learning in response to each new prompt from iterative jailbreak methods. Leveraging the distinctions between harmful jailbreak-generated prompts and typical harmless prompts, we introduce a reinforcement learning-based approach that optimizes prompts to ensure appropriate responses for harmless tasks while explicitly rejecting harmful prompts. Additionally, to curb overfitting to the narrow band of partial input rewrites explored during an attack, we introduce Past-Direction Gradient Damping (PDGD). Experiments conducted on three LLMs show that our approach significantly outperforms five existing defense methods against five iterative jailbreak methods. Moreover, our results indicate that our prompt optimization strategy simultaneously enhances response quality for harmless tasks.
Extended LSTM: Adaptive Feature Gating for Toxic Comment Classification
Venue: arXiv cs.CL 2025
Year: 2025

Toxic comment detection remains a challenging task, where transformer-based models (e.g., BERT) incur high computational costs and degrade on minority toxicity classes, while classical ensembles lack semantic adaptability. We propose xLSTM, a parameter-efficient and theoretically grounded framework that unifies cosine-similarity gating, adaptive feature prioritization, and principled class rebalancing. A learnable reference vector {v} in {R}^d modulates contextual embeddings via cosine similarity, amplifying toxic cues and attenuating benign signals to yield stronger gradients under severe class imbalance. xLSTM integrates multi-source embeddings (GloVe, FastText, BERT CLS) through a projection layer, a character-level BiLSTM for morphological cues, embedding-space SMOTE for minority augmentation, and adaptive focal loss with dynamic class weighting. On the Jigsaw Toxic Comment benchmark, xLSTM attains 96.0% accuracy and 0.88 macro-F1, outperforming BERT by 33% on threat and 28% on identity_hate categories, with 15 times fewer parameters and 50ms inference latency. Cosine gating contributes a +4.8% F1 gain in ablations. The results establish a new efficiency adaptability frontier, demonstrating that lightweight, theoretically informed architectures can surpass large pretrained models on imbalanced, domain-specific NLP tasks.
Bitwidth-Specific Logarithmic Arithmetic for Future Hardware-Accelerated Training
Venue: arXiv cs.LG 2025
Year: 2025

While advancements in quantization have significantly reduced the computational costs of inference in deep learning, training still predominantly relies on complex floating-point arithmetic. Low-precision fixed-point training presents a compelling alternative. This work introduces a novel enhancement in low-precision logarithmic fixed-point training, geared towards future hardware accelerator designs. We propose incorporating bitwidth in the design of approximations to arithmetic operations. To this end, we introduce a new hardware-friendly, piece-wise linear approximation for logarithmic addition. Using simulated annealing, we optimize this approximation at different precision levels. A C++ bit-true simulation demonstrates training of VGG-11 and VGG-16 models on CIFAR-100 and TinyImageNet, respectively, using 12-bit integer arithmetic with minimal accuracy degradation compared to 32-bit floating-point training. Our hardware study reveals up to 32.5% reduction in area and 53.5% reduction in energy consumption for the proposed LNS multiply-accumulate units compared to that of linear fixed-point equivalents.