OpenRaiser/Intern-Atlas huggingface dataset:
Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language-Action (VLA) models adapt large vision-language backbones to map images and instructions into robot actions. However, prevailing VLAs either generate actions auto-regressively in a fixed left-to-right order or attach separate MLP or diffusion heads outside the backbone, leading to fragmented information pathways and specialized training requirements that hinder a unified, scalable architecture. We present Discrete Diffusion VLA, a unified-transformer policy that models discretized action chunks with discrete diffusion. The design retains diffusion's progressive refinement paradigm while remaining natively compatible with the discrete token interface of VLMs. Our method achieves an adaptive decoding order that resolves easy action elements before harder ones and uses secondary re-masking to revisit uncertain predictions across refinement rounds, which improves consistency and enables robust error correction. This unified decoder preserves pre-trained vision-language priors, supports parallel decoding, breaks the autoregressive bottleneck, and reduces the number of function evaluations. Discrete Diffusion VLA achieves 96.3% avg. success rates on LIBERO, 71.2% visual matching on SimplerEnv-Fractal and 54.2% overall on SimplerEnv-Bridge. We also provide ablation study on vision-language ability retention on LIBERO-OOD (Out-of-Distribution) benchmark, with our method improving over autoregressive, MLP decoder and continuous diffusion baselines. These findings indicate that discrete-diffusion VLA supports precise action modeling and consistent training, laying groundwork for scaling VLA to larger models and datasets. Our code is available at https://github.com/Liang-ZX/DiscreteDiffusionVLA/tree/libero.
ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication
Venue: arXiv cs.NI 2025
Year: 2025

In disaster-stricken and large-scale urban emergency scenarios, ensuring reliable communication remains a formidable challenge, as collapsed infrastructure, unpredictable mobility, and severely constrained resources disrupt conventional networks. Delay-Tolerant Networks (DTNs), though resilient through their store-carry-forward paradigm, reveal the fundamental weaknesses of classical protocols - Epidemic, Spray-and-Wait, and MaxProp - when confronted with sparse encounters, buffer shortages, and volatile connectivity. To address these obstacles, this study proposes ML-MaxProp, a hybrid routing protocol that strengthens MaxProp with supervised machine learning. By leveraging contextual features such as encounter frequency, hop count, buffer occupancy, message age, and time-to-live (TTL), ML-MaxProp predicts relay suitability in real time, transforming rigid heuristics into adaptive intelligence. Extensive simulations in the ONE environment using the Helsinki SPMBM mobility model show that ML-MaxProp consistently surpasses baseline protocols, achieving higher delivery probability, lower latency, and reduced overhead. Statistical validation further shows that these improvements are both significant and robust, even under highly resource-constrained and unstable conditions. Overall, this work shows that ML-MaxProp is not just an incremental refinement but a lightweight, adaptive, and practical solution to one of the hardest challenges in DTNs: sustaining mission-critical communication when infrastructure collapses and every forwarding decision becomes critical.
Disabling Self-Correction in Retrieval-Augmented Generation via Stealthy Retriever Poisoning
Venue: arXiv cs.CR 2025
Year: 2025

Retrieval-Augmented Generation (RAG) has become a standard approach for improving the reliability of large language models (LLMs). Prior work demonstrates the vulnerability of RAG systems by misleading them into generating attacker-chosen outputs through poisoning the knowledge base. However, this paper uncovers that such attacks could be mitigated by the strong \textit{self-correction ability (SCA)} of modern LLMs, which can reject false context once properly configured. This SCA poses a significant challenge for attackers aiming to manipulate RAG systems. In contrast to previous poisoning methods, which primarily target the knowledge base, we introduce \textsc{DisarmRAG}, a new poisoning paradigm that compromises the retriever itself to suppress the SCA and enforce attacker-chosen outputs. This compromisation enables the attacker to straightforwardly embed anti-SCA instructions into the context provided to the generator, thereby bypassing the SCA. To this end, we present a contrastive-learning-based model editing technique that performs localized and stealthy edits, ensuring the retriever returns a malicious instruction only for specific victim queries while preserving benign retrieval behavior. To further strengthen the attack, we design an iterative co-optimization framework that automatically discovers robust instructions capable of bypassing prompt-based defenses. We extensively evaluate DisarmRAG across six LLMs and three QA benchmarks. Our results show near-perfect retrieval of malicious instructions, which successfully suppress SCA and achieve attack success rates exceeding 90\% under diverse defensive prompts. Also, the edited retriever remains stealthy under several detection methods, highlighting the urgent need for retriever-centric defenses.
Smart Contract Intent Detection with Pre-trained Programming Language Model
Venue: arXiv cs.SE 2025
Year: 2025

Malicious developer intents in smart contracts constitute significant security threats to decentralized applications, leading to substantial economic losses. To address this, SmartIntentNN was previously introduced as a deep learning model for detecting unsafe developer intents. By combining the Universal Sentence Encoder, a K-means clustering-based intent highlighting mechanism, and a Bidirectional Long Short-Term Memory (BiLSTM) network, the model achieved an F1 score of 0.8633 on an evaluation set of 10,000 real-world smart contracts across ten distinct intent categories. In this study, we present an enhanced version of this model, SmartIntentNN2 (Smart Contract Intent Neural Network V2). The primary enhancement is the integration of a BERT-based pre-trained programming language model, which we domain-adaptively pre-train on a dataset of 16,000 real-world smart contracts using a Masked Language Modeling objective. SmartIntentNN2 retains the BiLSTM-based multi-label classification network for intent detection. On the same evaluation set of 10,000 smart contracts, SmartIntentNN2 achieves superior performance with an accuracy of 0.9789, precision of 0.9090, recall of 0.9476, and an F1 score of 0.9279, substantially outperforming its predecessor and other baseline models. Notably, SmartIntentNN2 also delivers a 65.5% relative improvement in F1 score over GPT-4.1 on this specialized task. These results establish SmartIntentNN2 as a new state-of-the-art model for smart contract intent detection.
CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning
Venue: arXiv cs.CV 2025
Year: 2025

Autonomous agents for Graphical User Interfaces (GUIs) face significant challenges in specialized domains such as scientific computing, where both long-horizon planning and precise execution are required. Existing approaches suffer from a trade-off: generalist agents excel at planning but perform poorly in execution, while specialized agents demonstrate the opposite weakness. Recent compositional frameworks attempt to bridge this gap by combining a planner and an actor, but they are typically static and non-trainable, which prevents adaptation from experience. This is a critical limitation given the scarcity of high-quality data in scientific domains. To address these limitations, we introduce CODA, a novel and trainable compositional framework that integrates a generalist planner (Cerebrum) with a specialist executor (Cerebellum), trained via a dedicated two-stage pipeline. In the first stage, Specialization, we apply a decoupled GRPO approach to train an expert planner for each scientific application individually, bootstrapping from a small set of task trajectories. In the second stage, Generalization, we aggregate all successful trajectories from the specialized experts to build a consolidated dataset, which is then used for supervised fine-tuning of the final planner. This equips CODA with both robust execution and cross-domain generalization. Evaluated on four challenging applications from the ScienceBoard benchmark, CODA significantly outperforms baselines and establishes a new state of the art among open-source models.
LLM Agents for Generating Microservice-based Applications: how complex is your specification?
Venue: arXiv cs.SE 2025
Year: 2025

In this paper we evaluate the capabilities of LLM Agents in generating code for real-world problems. Specifically, we explore code synthesis for microservice-based applications, a widely used architectural pattern for building applications. We define a standard template for specifying these applications, and we propose a metric for scoring the difficulty of a specification. The higher the score, the more difficult it is to generate code for the specification. Our experimental results show that agents using strong LLMs (like GPT-3o-mini) do fairly well on medium difficulty specifications but do poorly on those of higher difficulty levels. This is due to more intricate business logic, a greater use of external services, database integration and inclusion of non-functional capabilities such as authentication. We analyzed the errors in LLM-synthesized code and report on the key challenges LLM Agents face in generating code for these specifications. Finally, we show that using a fine-grained approach to code generation improves the correctness of the generated code.
Task-Aware Tuning of Time Constants in Spiking Neural Networks for Multimodal Classification
Venue: arXiv cs.NE 2025
Year: 2025

Spiking Neural Networks (SNNs) are promising candidates for low-power edge computing in domains such as wearable sensing and time-series analysis. A key neuronal parameter, the leaky time constant (LTC), governs temporal integration of information in Leaky Integrateand-Fire (LIF) neurons, yet its impact on feedforward SNN performance across different data modalities remains underexplored. This study investigates the role of LTC in a temporally adaptive feedforward SNN applied to static image, dynamic image, and biosignal time-series classification. Presented experiments demonstrate that LTCs critically affect inference accuracy, synaptic weight distributions, and firing dynamics. For static and dynamic images, intermediate LTCs yield higher accuracy and compact, centered weight histograms, reflecting stable feature encoding. In time-series tasks, optimal LTCs enhance temporal feature retention and result in broader weight sparsity, allowing for tolerance of LTC variations. The provided results show that inference accuracy peaks at specific LTC ranges, with significant degradation beyond this optimal band due to over-integration or excessive forgetting. Firing rate analysis reveals a strong interplay between LTC, network depth, and energy efficiency, underscoring the importance of balanced spiking activity. These findings reveal that task-specific LTC tuning is essential for efficient spike coding and robust learning. The results provide practical guidelines for hardware-aware SNN optimization and highlight how neuronal time constants can be designed to match task dynamics. This work contributes toward scalable, ultra-lowpower SNN deployment for real-time classification tasks in neuromorphic computing.
Spatio-Temporal Pruning for Compressed Spiking Large Language Models
Venue: arXiv cs.NE 2025
Year: 2025

Large Language Models (LLMs) present significant challenges for deployment in energy-constrained environments due to their large model sizes and high inference latency. Spiking Neural Networks (SNNs), inspired by the sparse event-driven neural processing and energy-efficient information transmission in the brain, offer a promising alternative for achieving low-power computing. Integrating the event-driven efficiency of spiking neurons with the advanced capabilities of LLMs represents a promising direction for power-efficient LLMs. This work specifically delves into the design of compressed spiking LLMs. Here, we revisit spatial and temporal pruning from the perspective of SNNs and propose a novel spatio-temporal pruning framework for Spiking LLMs to optimize computational efficiency while preserving high performance. Our spatial pruning technique reduces the number of active neurons and attention heads, effectively lowering the computational complexity of the model. Meanwhile, temporal pruning minimizes inference latency by dynamically adjusting the number of timesteps required for different layers. By combining these approaches with other compression techniques, we present the first work in the domain of Spiking LLMs to jointly explore spatial pruning, temporal pruning, extreme quantization and knowledge distillation strategies. Extensive experimental evaluation of our proposed framework for SpikingBERT on the large-scale GLUE benchmark demonstrates the efficacy of our approach in terms of computational operations and inference latency. Our approach offers a compelling solution for real-time, low-power natural language processing applications, making Spiking LLMs more practical for deployment on edge devices and in power-constrained settings.
Evaluation of A National Digitally-Enabled Health Promotion Campaign for Mental Health Awareness using Social Media Platforms Tik Tok, Facebook, Instagram, and YouTube
Venue: arXiv cs.SI 2025
Year: 2025

Mental health disorders rank among the 10 leading contributors to the global burden of diseases, yet persistent stigma and care barriers delay early intervention. This has inspired efforts to leverage digital platforms for scalable health promotion to engage at-risk populations. To evaluate the effectiveness of a digitally-enabled mental health promotion (DEHP) campaign, we conducted an observational cross-sectional study of a 3-month (February-April 2025) nation-wide campaign in Singapore. Campaign materials were developed using a marketing funnel framework and disseminated across YouTube, Facebook, Instagram, and TikTok. This included narrative videos and infographics to promote symptom awareness, coping strategies, and/or patient navigation to Singapore's Mindline website, as the intended endpoint for user engagement and support. Primary outcomes include anonymised performance analytics (impressions, unique reach, video content view, engagements) stratified by demographics, device types, and sector. Secondary outcomes measured cost-efficiency metrics and traffic to the Mindline website respectively. This campaign generated 3.49 million total impressions and reached 1.39 million unique residents, with a Cost Per Click at 29.33 SGD, Cost Per Mille at 26.90 SGD and Cost Per Action at 6.06 SGD. Narrative videos accumulated over 630,000 views and 18,768 engagements. Overall, we demonstrate that DEHP campaigns can achieve national engagement for mental health awareness through multi-channel distribution and creative, narrative-driven designs.
IntentionReasoner: Facilitating Adaptive LLM Safeguards through Intent Reasoning and Selective Query Refinement
Venue: arXiv cs.AI 2025
Year: 2025

The rapid advancement of large language models (LLMs) has driven their adoption across diverse domains, yet their ability to generate harmful content poses significant safety challenges. While extensive research has focused on mitigating harmful outputs, such efforts often come at the cost of excessively rejecting harmless prompts. Striking a balance among safety, over-refusal, and utility remains a critical challenge. In this work, we introduce IntentionReasoner, a novel safeguard mechanism that leverages a dedicated guard model to perform intent reasoning, multi-level safety classification, and query rewriting to neutralize potentially harmful intent in edge-case queries. Specifically, we first construct a comprehensive dataset comprising approximately 163,000 queries, each annotated with intent reasoning, safety labels, and rewritten versions. Supervised fine-tuning is then applied to equip the guard model with foundational capabilities in format adherence, intent analysis, and safe rewriting. Finally, we apply a tailored multi-reward optimization strategy that integrates rule-based heuristics and reward model signals within a reinforcement learning framework to further enhance performance. Extensive experiments show that IntentionReasoner excels in multiple safeguard benchmarks, generation quality evaluations, and jailbreak attack scenarios, significantly enhancing safety while effectively reducing over-refusal rates and improving the quality of responses.
Grounding Multimodal Large Language Models with Quantitative Skin Attributes: A Retrieval Study
Venue: arXiv cs.CV 2025
Year: 2025

Artificial Intelligence models have demonstrated significant success in diagnosing skin diseases, including cancer, showing the potential to assist clinicians in their analysis. However, the interpretability of model predictions must be significantly improved before they can be used in practice. To this end, we explore the combination of two promising approaches: Multimodal Large Language Models (MLLMs) and quantitative attribute usage. MLLMs offer a potential avenue for increased interpretability, providing reasoning for diagnosis in natural language through an interactive format. Separately, a number of quantitative attributes that are related to lesion appearance (e.g., lesion area) have recently been found predictive of malignancy with high accuracy. Predictions grounded as a function of such concepts have the potential for improved interpretability. We provide evidence that MLLM embedding spaces can be grounded in such attributes, through fine-tuning to predict their values from images. Concretely, we evaluate this grounding in the embedding space through an attribute-specific content-based image retrieval case study using the SLICE-3D dataset.
A Comprehensive Survey of 5G URLLC and Challenges in the 6G Era
Venue: arXiv cs.NI 2025
Year: 2025

As the wireless communication paradigm is being transformed from human centered communication services towards machine centered communication services, the requirements of rate, latency and reliability for these services have also been transformed drastically. Thus the concept of Ultra Reliable and Low Latency Communication (URLLC) has emerged as a dominant theme for 5G and 6G systems. Though the latency and reliability requirement varies from one use case to another, URLLC services generally aim to achieve very high reliability in the range of 99.999\% while ensuring the latency of up to 1 ms. These two targets are however inherently opposed to one another. Significant amounts of work have been carried out to meet these ambitious but conflicting targets. In this article a comprehensive survey of the URLLC approaches in 5G systems are analysed in detail. Effort has been made to trace the history and evolution of latency and reliability issues in wireless communication. A layered approach is taken where physical layer, Medium Access Control (MAC) layer as well as cross layer techniques are discussed in detail. It also covers the design consideration for various 5G and beyond verticals. Finally the article concludes by providing a detailed discussion on challenges and future outlook with particular focus on the emerging 6G paradigm.
The Role of Teacher Calibration in Knowledge Distillation
Venue: arXiv cs.LG 2025
Year: 2025

Knowledge Distillation (KD) has emerged as an effective model compression technique in deep learning, enabling the transfer of knowledge from a large teacher model to a compact student model. While KD has demonstrated significant success, it is not yet fully understood which factors contribute to improving the student's performance. In this paper, we reveal a strong correlation between the teacher's calibration error and the student's accuracy. Therefore, we claim that the calibration of the teacher model is an important factor for effective KD. Furthermore, we demonstrate that the performance of KD can be improved by simply employing a calibration method that reduces the teacher's calibration error. Our algorithm is versatile, demonstrating effectiveness across various tasks from classification to detection. Moreover, it can be easily integrated with existing state-of-the-art methods, consistently achieving superior performance.
A Novel Framework for Automated Explain Vision Model Using Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

The development of many vision models mainly focuses on improving their performance using metrics such as accuracy, IoU, and mAP, with less attention to explainability due to the complexity of applying xAI methods to provide a meaningful explanation of trained models. Although many existing xAI methods aim to explain vision models sample-by-sample, methods explaining the general behavior of vision models, which can only be captured after running on a large dataset, are still underexplored. Furthermore, understanding the behavior of vision models on general images can be very important to prevent biased judgments and help identify the model's trends and patterns. With the application of Vision-Language Models, this paper proposes a pipeline to explain vision models at both the sample and dataset levels. The proposed pipeline can be used to discover failure cases and gain insights into vision models with minimal effort, thereby integrating vision model development with xAI analysis to advance image analysis.
Validating Generative Agent-Based Models for Logistics and Supply Chain Management Research
Venue: arXiv cs.MA 2025
Year: 2025

Generative Agent-Based Models (GABMs) powered by large language models (LLMs) offer promising potential for empirical logistics and supply chain management (LSCM) research by enabling realistic simulation of complex human behaviors. Unlike traditional agent-based models, GABMs generate human-like responses through natural language reasoning, which creates potential for new perspectives on emergent LSCM phenomena. However, the validity of LLMs as proxies for human behavior in LSCM simulations is unknown. This study evaluates LLM equivalence of human behavior through a controlled experiment examining dyadic customer-worker engagements in food delivery scenarios. I test six state-of-the-art LLMs against 957 human participants (477 dyads) using a moderated mediation design. This study reveals a need to validate GABMs on two levels: (1) human equivalence testing, and (2) decision process validation. Results reveal GABMs can effectively simulate human behaviors in LSCM; however, an equivalence-versus-process paradox emerges. While a series of Two One-Sided Tests (TOST) for equivalence reveals some LLMs demonstrate surface-level equivalence to humans, structural equation modeling (SEM) reveals artificial decision processes not present in human participants for some LLMs. These findings show GABMs as a potentially viable methodological instrument in LSCM with proper validation checks. The dual-validation framework also provides LSCM researchers with a guide to rigorous GABM development. For practitioners, this study offers evidence-based assessment for LLM selection for operational tasks.