OpenRaiser/Intern-Atlas huggingface dataset:
Zhyper: Factorized Hypernetworks for Conditioned LLM Fine-Tuning
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Model (LLM) conditioning refers to instructing an LLM to generate content in accordance with the norms and values of a specific culture, beliefs of a particular political orientation, or any desired text-specified semantic conditioning. Unfortunately, prompt engineering does not ensure that LLMs behave in accordance with a desired conditioning due to the inductive bias of the pre-training and alignment datasets. Prior works have focused on fine-tuning LLMs by directly conditioning the LoRA weights; however, such methods introduce a large number of parameters. As a remedy, we propose Zhyper, a parameter-efficient factorized hypernetwork framework that generates context-aware LoRA adapters from textual descriptions. Experiments on multiple benchmarks show that Zhyper achieves competitive performance with up to 26x fewer parameters than the state-of-the-art baselines. Furthermore, we extend Zhyper to cultural alignment, demonstrating improved generalization to out-of-domain settings and a better capturing of fine-grained contextual values.
Cultural Dimensions of Artificial Intelligence Adoption: Empirical Insights for Wave 1 from a Multinational Longitudinal Pilot Study
Venue: arXiv cs.CY 2025
Year: 2025

The swift diffusion of artificial intelligence (AI) raises critical questions about how cultural contexts shape adoption patterns and their consequences for human daily life. This study investigates the cultural dimensions of AI adoption and their influence on cognitive strategies across nine national contexts in Europe, Africa, Asia, and South America. Drawing on survey data from a diverse pilot sample (n = 21) and guided by cross-cultural psychology, digital ethics, and sociotechnical systems theory, we examine how demographic variables (age, gender, professional role) and cultural orientations (language, values, and institutional exposure) mediate perceptions of trust, ethical acceptability, and reliance on AI. Results reveal two key findings: First, cultural factors, particularly language and age, significantly affect AI adoption and perceptions of reliability with older participants reporting higher engagement with AI for educational purposes. Second, ethical judgment about AI use varied across domains, with professional contexts normalizing its role as a pragmatic collaborator while academic settings emphasized risks of plagiarism. These findings extend prior research on culture and technology adoption by demonstrating that AI use is neither universal nor neutral but culturally contingent, domain-specific, and ethically situated. The study highlights implications for AI use in education, professional practice, and global technology policy, pointing at actions that enable usage of AI in a way that is both culturally adaptive and ethically robust.
Review of Tools for Zero-Code LLM Based Application Development
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are transforming software creation by enabling zero code development platforms. Our survey reviews recent platforms that let users build applications without writing code, by leveraging LLMs as the brains of the development process. We adopt a broad survey methodology, categorizing platforms based on key dimensions such as interface style, backend integration, output type, and extensibility. We analyze both dedicated LLM based app builders (OpenAI's custom GPTs, Bolt.new, Dust.tt, Flowise, Cognosys) and general no code platforms (e.g., Bubble, Glide) that integrate LLM capabilities. We present a taxonomy categorizing these platforms by their interface (conversational, visual, etc.), supported LLM backends, output type (chatbot, full application, workflow), and degree of extensibility. Core features such as autonomous agents, memory management, workflow orchestration, and API integrations are in scope of the survey. We provide a detailed comparison, highlighting each platform's strengths and limitations. Trade offs (customizability, scalability, vendor lock-in) are discussed in comparison with traditional and low code development approaches. Finally, we outline future directions, including multimodal interfaces, on device LLMs, and improved orchestration for democratizing app creation with AI. Our findings indicate that while zero code LLM platforms greatly reduce the barrier to creating AI powered applications, they still face challenges in flexibility and reliability. Overall, the landscape is rapidly evolving, offering exciting opportunities to empower non programmers to create sophisticated software.
Optimal Random Access and Conditional Lower Bounds for 2D Compressed Strings
Venue: arXiv cs.DS 2025
Year: 2025

Compressed indexing is a powerful technique that enables efficient querying over data stored in compressed form, significantly reducing memory usage and often accelerating computation. While extensive progress has been made for one-dimensional strings, many real-world datasets (such as images, maps, and adjacency matrices) are inherently two-dimensional and highly compressible. Unfortunately, naively applying 1D techniques to 2D data leads to suboptimal results, as fundamental structural repetition is lost during linearization. This motivates the development of native 2D compressed indexing schemes that preserve both compression and query efficiency. We present three main contributions that advance the theory of compressed indexing for 2D strings: (1) We design the first data structure that supports optimal-time random access to a 2D string compressed by a 2D grammar. Specifically, for a 2D string $T\inΣ^{r\times c}$ compressed by a 2D grammar $G$ and any constant $ε>0$, we achieve $O(\log n/\log \log n)$ query time and $O(|G|\log^{2+ε}n)$ space, where $n=\max(r,c)$. (2) We prove conditional lower bounds for pattern matching over 2D-grammar compressed strings. Assuming the Orthogonal Vectors Conjecture, no algorithm can solve this problem in time $O(|G|^{2-ε}\cdot |P|^{O(1)})$ for any $ε>0$, demonstrating a separation from the 1D case, where optimal solutions exist. (3) We show that several fundamental 2D queries, such as the 2D longest common extension, rectangle sum, and equality, cannot be supported efficiently under hardness assumptions for rank and symbol occurrence queries on 1D grammar-compressed strings. This is the first evidence connecting the complexity of 2D compressed indexing to long-standing open problems in the 1D setting.
A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion Models have become a cornerstone of modern generative AI for their exceptional generation quality and controllability. However, their inherent \textit{multi-step iterations} and \textit{complex backbone networks} lead to prohibitive computational overhead and generation latency, forming a major bottleneck for real-time applications. Although existing acceleration techniques have made progress, they still face challenges such as limited applicability, high training costs, or quality degradation. Against this backdrop, \textbf{Diffusion Caching} offers a promising training-free, architecture-agnostic, and efficient inference paradigm. Its core mechanism identifies and reuses intrinsic computational redundancies in the diffusion process. By enabling feature-level cross-step reuse and inter-layer scheduling, it reduces computation without modifying model parameters. This paper systematically reviews the theoretical foundations and evolution of Diffusion Caching and proposes a unified framework for its classification and analysis. Through comparative analysis of representative methods, we show that Diffusion Caching evolves from \textit{static reuse} to \textit{dynamic prediction}. This trend enhances caching flexibility across diverse tasks and enables integration with other acceleration techniques such as sampling optimization and model distillation, paving the way for a unified, efficient inference framework for future multimodal and interactive applications. We argue that this paradigm will become a key enabler of real-time and efficient generative AI, injecting new vitality into both theory and practice of \textit{Efficient Generative Intelligence}.
SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas
Venue: arXiv cs.RO 2025
Year: 2025

In this paper, we propose SEA, a novel approach for active robot exploration through semantic map prediction and a reinforcement learning-based hierarchical exploration policy. Unlike existing learning-based methods that rely on one-step waypoint prediction, our approach enhances the agent's long-term environmental understanding to facilitate more efficient exploration. We propose an iterative prediction-exploration framework that explicitly predicts the missing areas of the map based on current observations. The difference between the actual accumulated map and the predicted global map is then used to guide exploration. Additionally, we design a novel reward mechanism that leverages reinforcement learning to update the long-term exploration strategies, enabling us to construct an accurate semantic map within limited steps. Experimental results demonstrate that our method significantly outperforms state-of-the-art exploration strategies, achieving superior coverage ares of the global map within the same time constraints.
SmartSwitch: Advancing LLM Reasoning by Overcoming Underthinking via Promoting Deeper Thought Exploration
Venue: arXiv cs.CL 2025
Year: 2025

The long chain-of-thought (LongCoT) capability is central to the recent breakthroughs achieved by large language models in complex reasoning tasks. However, the accompanying issue of ''underthinking'', where models exhibit shallow reasoning by frequently switching thoughts without sufficient exploration, limits both performance and token efficiency. To address this problem, we propose a simple yet effective reasoning strategy: the SmartSwitch inference framework. This framework can be easily integrated into any large language model as a plug-and-play solution, continuously monitoring the model's reasoning process to detect underthinking and guide it toward deeper exploration of promising but overlooked thoughts. Specifically, the perception module identifies points where thoughts switch and evaluates the potential of the preceding thought using an off-the-shelf process reward model (PRM). If a high-potential thought is found to be prematurely abandoned, the intervention module interrupts the ongoing inference, backtracks to the point before the switch, and inserts a "deepening prompt" to encourage further exploration along that promising path. Extensive experiments on challenging mathematical reasoning benchmarks demonstrate that our method significantly enhances the performance of various large language models of different sizes.
Adapting Multilingual Models to Code-Mixed Tasks via Model Merging
Venue: arXiv cs.CL 2025
Year: 2025

We study model merging as a practical alternative to conventional adaptation strategies for code-mixed NLP. Starting from a multilingual base model, we: (i) perform continued pre-training (CPT) on unlabeled code-mixed text to obtain an adapted checkpoint, (ii) merge checkpoint with the base model, and (iii) fine-tune (FT) on the downstream task data. We evaluate our approach for sentence classification (sentiment and hate speech) task in English-Hindi (En-Hi) and English-Spanish (En-Es) using XLM-R and Llama-3.2-1B models. Our results show that merged models consistently outperform full fine-tuning and CPT->FT. We observe gains of 2--5 points in F1 over full fine-tuning and ~1-2 points over CPT->FT, indicating that unlabeled data is leveraged more effectively via merging than via CPT alone. Zero-/few-shot prompting with larger LLMs (e.g., Llama-3.3-70B) lags behind fine-tuned and merged checkpoints, underscoring limits of in-context learning for code-mixed inputs. We further test cross-pair transfer by training on En-Hi and evaluating on En-Ta and En-Ml: merged checkpoints transfer more strongly than monolingual-English baselines (e.g., TV/TIES variants reaching 0.65-0.68 F1 vs 0.61-0.63 for full fine-tuning), suggesting that code-mixed knowledge is a more reliable substrate for low-resource pairs. We conclude with adaptation recipes matched to common data regimes (labeled only; labeled+unlabeled; transfer-only) and discuss limitations and scaling considerations for broader tasks and larger models.
The Art of Asking: Multilingual Prompt Optimization for Synthetic Data
Venue: arXiv cs.CL 2025
Year: 2025

Synthetic data has become a cornerstone for scaling large language models, yet its multilingual use remains bottlenecked by translation-based prompts. This strategy inherits English-centric framing and style and neglects cultural dimensions, ultimately constraining model generalization. We argue that the overlooked prompt space-the very inputs that define training distributions-offers a more powerful lever for improving multilingual performance. We introduce a lightweight framework for prompt-space optimization, where translated prompts are systematically transformed for Naturalness, Cultural Adaptation, and Difficulty Enhancement. Using an off-the-shelf multilingual LLM, we apply these transformations to prompts for 12 languages spanning 7 families. Under identical data conditions, our approaches achieve substantial and consistent downstream improvements over the translation-only baseline: +4.7% on Global-MMLU accuracy, +2.4% on Flores XCometXL and +35.3% wins in preferences on mArenaHard. We establish prompt-space optimization as a simple yet powerful paradigm for building multilingual LLMs that are more robust, culturally grounded, and globally capable.
olmOCR 2: Unit Test Rewards for Document OCR
Venue: arXiv cs.CV 2025
Year: 2025

We present olmOCR 2, the latest in our family of powerful OCR systems for converting digitized print documents, like PDFs, into clean, naturally ordered plain text. olmOCR 2 is powered by olmOCR-2-7B-1025, a specialized, 7B vision language model (VLM) trained using reinforcement learning with verifiable rewards (RLVR), where our rewards are a diverse set of binary unit tests. To scale unit test creation, we develop a pipeline for generating synthetic documents with diverse and challenging layouts, known ground-truth HTML source code, and extracted test cases. We show that RL training on these test cases results in state-of-the-art performance on olmOCR-Bench, our English-language OCR benchmark, with the largest improvements in math formula conversion, table parsing, and multi-column layouts compared to previous versions. We release our model, data and code under permissive open licenses.
Finite Element and Machine Learning Modeling of Autogenous Self-Healing Concrete
Venue: arXiv cs.CE 2025
Year: 2025

A time-dependent modeling framework for autogenous self-healing concrete that couples moisture diffusion with damage evolution was developed. Water transport follows Fick's second law with a damage-dependent diffusivity obtained by power-law interpolation between intact concrete and crack space. Healing reduces damage in proportion to local moisture and a smoothed cement availability field computed via a Helmholtz filter. Two finite element variants were implemented in FEniCSx over time horizons up to $5\times10^6$ seconds: a Crack Diffusion Model (CDM) with standard diffusion and a Crack Membrane Model (CMM) that gates cross-crack transport until a critical moisture threshold is reached. Key control parameters are the initial crack orientation and size, the diffusion coefficients of intact and cracked concrete, the healing rate constant, and the cement availability smoothing parameter. Simulations on a unit square domain show that healing time varies non-monotonically with crack orientation, peaking near $45^\circ$ and $135^\circ$ and minimizing near $90^\circ$, consistent with diffusion distance to crack endpoints dominating the process. The dependence on crack width reverses with material parameters: healing time increases when $D_{\text{cracked}}<D_{\text{intact}}$ and decreases when $D_{\text{cracked}}>D_{\text{intact}}$. The CMM reproduces staged moisture penetration with delayed gate activation but lengthens total healing time, whereas the CDM is efficient for parametric sweeps. Machine learning classifiers trained on one million simulation samples predict binary healing outcomes $H(σ,γ,t)$ (healed or not) with high accuracy (up to 0.998 for neural networks). Although experimental calibration is still required, the framework provides a versatile tool for guiding laboratory studies and implementations of self-healing concrete.
Can Reasoning Models Obfuscate Reasoning? Stress-Testing Chain-of-Thought Monitorability
Venue: arXiv cs.CR 2025
Year: 2025

Recent findings suggest that misaligned models may exhibit deceptive behavior, raising concerns about output trustworthiness. Chain-of-thought (CoT) is a promising tool for alignment monitoring: when models articulate their reasoning faithfully, monitors can detect and mitigate harmful behaviors before undesirable outcomes occur. However, a key uncertainty is: Can models obfuscate their CoT in order to pursue hidden adversarial objectives while evading detection? To answer this question and thus stress-test CoT monitorability, we develop a composable and quantifiable taxonomy of prompts to elicit CoT obfuscation. We evaluate both internal CoT (reasoning traces) and external CoT (prompted reasoning in outputs) using toy tasks and more realistic environments in SHADE-Arena. We show that: (i) CoT monitoring performs accurately and efficiently without obfuscation pressure. (ii) Under strong obfuscation pressure, some models successfully complete adversarial tasks while evading detection. (iii) Models do not obfuscate their internal CoT as much as their external CoT (under prompt pressure). These results suggest that while CoT provides valuable oversight in benign settings, robust deployment requires model-specific stress-testing of monitorability.
Cyberattack Detection in Critical Infrastructure and Supply Chains
Venue: arXiv cs.CR 2025
Year: 2025

Cyberattack detection in Critical Infrastructure and Supply Chains has become challenging in Industry 4.0. Intrusion Detection Systems (IDS) are deployed to counter the cyberattacks. However, an IDS effectively detects attacks based on the known signatures and patterns, Zero-day attacks go undetected. To overcome this drawback in IDS, the integration of a Dense Neural Network (DNN) with Data Augmentation is proposed. It makes IDS intelligent and enables it to self-learn with high accuracy when a novel attack is encountered. The network flow captures datasets are highly imbalanced same as the real network itself. The Data Augmentation plays a crucial role in balancing the data. The balancing of data is challenging as the minority class is as low as 0.000004\% of the dataset, and the abundant class is higher than 80\% of the dataset. Synthetic Minority Oversampling Technique is used for balancing the data. However, higher accuracies are achieved with balanced test data, lower accuracies are noticeable with the original imbalanced test data suggesting overfitting. A comparison with state-of-the-art research using Synthetic Minority Oversampling Technique with Edited Nearest Neighbor shows the classification of classes remains poor for the original dataset. This suggests highly imbalanced datasets of network flow require a different method of data augmentation.
An Integrated Approach to Neural Architecture Search for Deep Q-Networks
Venue: arXiv cs.LG 2025
Year: 2025

The performance of deep reinforcement learning agents is fundamentally constrained by their neural network architecture, a choice traditionally made through expensive hyperparameter searches and then fixed throughout training. This work investigates whether online, adaptive architecture optimization can escape this constraint and outperform static designs. We introduce NAS-DQN, an agent that integrates a learned neural architecture search controller directly into the DRL training loop, enabling dynamic network reconfiguration based on cumulative performance feedback. We evaluate NAS-DQN against three fixed-architecture baselines and a random search control on a continuous control task, conducting experiments over multiple random seeds. Our results demonstrate that NAS-DQN achieves superior final performance, sample efficiency, and policy stability while incurring negligible computational overhead. Critically, the learned search strategy substantially outperforms both undirected random architecture exploration and poorly-chosen fixed designs, indicating that intelligent, performance-guided search is the key mechanism driving success. These findings establish that architecture adaptation is not merely beneficial but necessary for optimal sample efficiency in online deep reinforcement learning, and suggest that the design of RL agents need not be a static offline choice but can instead be seamlessly integrated as a dynamic component of the learning process itself.
From Large to Small: Transferring CUDA Optimization Expertise via Reasoning Graph
Venue: arXiv cs.LG 2025
Year: 2025

Despite significant evolution of CUDA programming and domain-specific libraries, effectively utilizing GPUs with massively parallel engines remains difficult. Large language models (LLMs) show strong potential in generating optimized CUDA code from sequential code. However, using LLMs in practice faces two major challenges: cloud-based APIs pose risks of code leakage, and local deployment is often computationally expensive and inefficient. These drawbacks have spurred interest in small language models (SLMs), which are more lightweight and privacy-friendly. Encouragingly, recent studies show that SLMs can achieve performance comparable to LLMs on specific tasks. While SLMs can match LLMs on domain-specific tasks, their limited reasoning abilities lead to suboptimal performance in complex CUDA generation according to our experiments. To bridge this gap, we propose ReGraphT, a training-free, retrieval-augmented generation framework that transfers LLM-level reasoning to smaller models. ReGraphT organizes CUDA optimization trajectories into a structured reasoning graph, modeling the combined CUDA optimizations as state transitions, and leverages Monte Carlo Graph Search (MCGS) for efficient exploration. We also present a CUDA-specific benchmark with difficulty tiers defined by reasoning complexity to evaluate models more comprehensively. Experiments show that ReGraphT outperforms HPC-specific fine-tuned models and other retrieval-augmented approaches, achieving an average 2.33X speedup on CUDAEval and ParEval. When paired with DeepSeek-Coder-V2-Lite-Instruct and Qwen2.5-Coder-7B-Instruct, ReGraphT enables SLMs to approach LLM-level performance without the associated privacy risks or excessive computing overhead.