Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, particularly Large Language Models (LLMs), show promise in automating constraint specification from heterogeneous raw manufacturing data, their direct application faces challenges due to natural language ambiguity, non-deterministic outputs, and limited domain-specific knowledge. This paper presents a constraint-centric architecture that regulates LLMs to perform reliable automated constraint specification for production scheduling. The architecture defines a hierarchical structural space organized across three levels, implemented through domain-specific representation to ensure precision and reliability while maintaining flexibility. Furthermore, an automated production scenario adaptation algorithm is designed and deployed to efficiently customize the architecture for specific manufacturing configurations. Experimental results demonstrate that the proposed approach successfully balances the generative capabilities of LLMs with the reliability requirements of manufacturing systems, significantly outperforming pure LLM-based approaches in constraint specification tasks.

This paper presents CADL (Cognitive-Adaptive Deception Layer), an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning (Random Forest, XGBoost, Neural Networks) combined with behavioral profiling to identify and adapt responses to network intrusions. Through a coordinated signal bus architecture, security components share real-time intelligence, enabling collective decision-making. The system profiles attackers based on temporal patterns and deploys customized deception strategies across five escalation levels. Evaluation on 50,000 CICIDS2017 test samples demonstrates that CADL significantly outperforms traditional intrusion detection systems (Snort: 71.2%, Suricata: 68.5%) while maintaining production-ready false positive rates. The framework's behavioral analysis achieves 89% accuracy in classifying attacker profiles. We provide open-source implementation and transparent performance metrics, offering an accessible alternative to commercial deception platforms costing $150-400 per host annually.

Omer Abid, Gholamreza Rafiee * Abstract -- Medulloblastoma is a malignant pediatric brain cancer, and the discovery of molecular subgroups is enabling personalized treatment strategies. In 2019, a consensus identified eight novel subtypes within Groups 3 and 4, each displaying heterogeneous chara cteristics. Classifiers are essential for translating these findings into clinical practice by supporting clinical trials, personalized therapy development and application, and patient monitoring. This study presents a DNA methylation - based, cross - platform machine learning classifier capable of distinguishing these subtypes on both HM450 and EPIC methylation array samples . Across two independent test sets, the model achieved weighted F1 = 0.95 and balanced accuracy = 0.957, consistent across platforms. As the first cross - platform solution, it provides backward compatibility while extending applicability to a newer platform, also enhancing accessibility. It also has the potential to become the first publicly available classifier for these subtypes once deployed through a web application, as planned in the future . Th is work overall takes steps in the direction of advancing precision medicine and improving clinical outcomes for patients within the majority prevalence medulloblastoma subgroups, g roups 3 and 4. Keywords -- Medulloblastoma, Molecular Subgroup Classification, Machine Learning, AI for Health Medulloblastoma is a malignant brain cancer widely known for its prevalence in children. Through extensive treatment strategies based on surgery, chemotherapy and radiation therapy, approximately 75% of the patient are able to survive in the long term [1]. These treatments whi le crucial also come along with negative side effects, effecting patients' li ves [1] [2], especially considering the implications on the growing children. However, with advancement in genomics, molecular subgroups have been discover ed within the disease . T hese subgroups have shown to be heterogenous in clinical, biological and outcomes perspective [3] . These in fact are now considered better definition of disease behaviour than conventional techniques [3] .

Abstract--Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered, such reconnaissance can reveal grid topology, consumption patterns, and operational behavior, creating a gateway to more severe targeted attacks. Detecting this threat is difficult because the signals it produces are faint, short-lived, and often disappear when traffic is examined by a single node or along a single timeline. This paper introduces a graph-centric, multimodal detector that fuses physical-layer (Channel State Information (CSI), Signal-to-Noise Ratio (SNR)) and behavioral (latency, Packet Error Rate (PER), event context) indicators over ego-centric star subgraphs and short temporal windows to detect passive attacks. T o capture stealthy perturbations, a two-stage encoder is introduced: graph convolution aggregates spatial context across ego-centric star subgraphs, while a bidirectional GRU models short-term temporal dependencies. The encoder transforms heterogeneous features into a unified spatio-temporal representation suitable for classification. Training occurs in a federated learning setup under FedProx, improving robustness to heterogeneous local raw data and contributing to the trustworthiness of decentralized training; raw measurements remain on client devices. The model achieves a testing accuracy of 98.32% per-timestep (F1 The results demonstrate that combining spatial and temporal context enables reliable detection of stealthy reconnaissance while maintaining low false-positive rates, making the approach suitable for non-IID federated smart-grid deployments. Smart grids [1] define new energy systems constructed on the notion of bidirectional communication between consumers and utilities. They enable the management of real-time data across distributed nodes. However, this open communication exposes the grid to significant risks of passive attacks, which pose a threat to privacy, trust, and stability [2].

The expanding capabilities of Large Language Models (LLMs) have enabled their application in increasingly diverse and sophisticated generation tasks Zhao et al. (2025), from acting as AI agents that produce structured data to solving complex scientific problems and writing functional code Chen et al. (2021); Guo et al. (2024). However, this proliferation of high-quality, machine-generated content poses formidable challenges for authenticity verification Burrus et al. (2024); A yoobi et al. (2024) and the prevention of misuse A yoobi et al. (2023); Dammu et al. (2024). Text watermarking, which embeds imperceptible statistical signals into generated text, has emerged as a promising solution for establishing content provenance Liu et al. (2024); Chen et al. (2023); Y oo et al. (2023). The dominant paradigm involves augmenting the model's output logits; a foundational method, for example, partitions the vocabulary into "green" and "red" lists and adds a positive bias to the logits of green-listed tokens to embed a detectable signature Kirchenbauer et al. (2023). Initial research quickly identified a primary limitation of this approach: its performance degrades significantly in low-entropy contexts, such as code generation, where modifying deterministic tokens can corrupt functional correctness. To address this, subsequent work has focused on entropy-aware adaptations. SWEET Lee et al. (2023) introduced a static entropy threshold, selectively applying the watermark only to high-entropy tokens to preserve low-entropy syntactic structures. Building on this, EWD Lu et al. (2024) refined the detection process by assigning weights to tokens proportional to their entropy, improving sensitivity without a hard threshold. While these methods marked important progress for single-domain tasks, they addressed only part of the problem.

Neural Information Processing Systems http://nips.cc/

We thank the reviewers for their insightful feedback. We address reviewer comments below and begin by situating the paper's intended contribution: Why is this our goal? POMDP planners incur the complexity of full, closed-loop planning only when necessary. V oI is "contrary to the core concept of POMDPs", V oI macro-actions expand the set of problems that can be efficiently What is not our goal? The primary critique of reviewers is the limited scope of our experimental results.

We thank the reviewers for acknowledging our contributions and for providing valuable feedback. The NVIDIA Titan X (Pascal) is rated at 11.0 TFLOPS, so the latency of The critic architecture also follows the WGAN-GP paper. We clarified the concatenation process in our paper and have added the missing hidden-layer citation. Thank you for pointing this out. The GWIN continues to have a positive impact.

The field of machine programming (MP), the automation of the development of software, is making notable research advances.