Agentic Engineering Patterns > When we lose track of how code written by our agents works we take on cognitive debt. For a lot of things this doesn’t matter: if the code fetches some data from a database and outputs it as JSON the implementation details are likely simple enough that we don’t need to care. We can try out the new feature and make a very solid guess at how it works, then glance over the code […]
During disasters, cascading failures across power grids, communication networks, and social behavior amplify community fear and undermine cooperation. Existing cyber-physical-social (CPS) models simulate these coupled dynamics but lack mechanisms for active intervention. We extend the CPS resilience model of Valinejad and Mili (2023) with control channels for three agencies, communication, power, and emergency management, and formulate the resulting system as a three-player non-zero-sum differential game solved via online actor-critic reinforcement learning. Simulations based on Hurricane Harvey data show […]
arXiv:2603.24797v1 Announce Type: new Abstract: To fully capture the meaning of a sentence, semantic representations should encode aspect, which describes the internal temporal structure of events. In graph-based meaning representation frameworks such as Uniform Meaning Representations (UMR), aspect lets one know how events unfold over time, including distinctions such as states, activities, and completed events. Despite its importance, aspect remains sparsely annotated across semantic meaning representation frameworks. This has, in turn, hindered not only current manual annotation, but […]
I’m learning the foundations of RL in alignment with the Alberta Plan for AI research and have been running through sets of experiments to both learn and experiment. To that end I spent the last month validating different methods for streaming deep RL on a non-stationary, adversarial dataset of real SSH honeypot observations. This work focuses on prediction and is in line with steps 1 & 2 of the Alberta Plan (Sutton, Bowling, & Pilarski 2022). After implementing […]
In the post Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI, we introduced the Amazon Nova LLM-as-a-judge capability, which is a specialized evaluation model available through Amazon SageMaker AI that you can use to systematically measure the relative performance of generative AI systems. SageMaker AI now offers a rubric-based large language model (LLM) judge powered by Amazon Nova. Instead of using the same general rules for every task, it automatically creates specific evaluation criteria […]
Path planning is critical for multi-robot systems (MRS), directly affecting task efficiency, execution time and operational cost. Despite extensive research and the successful application of numerous algorithms, achieving globally optimal solutions in cluttered or dynamic environments remains a significant challenge. Issues such as scalability with in-creasing numbers of robots, computational efficiency, system robustness, and coordination complexity continue to drive the development of more reliable approaches. This study reviews modelling approaches, optimisation criteria, and solution algorithms based on roadmap […]
arXiv:2603.22288v1 Announce Type: new Abstract: Prompting strategies affect LLM reasoning performance, but their role in chart-based QA remains underexplored. We present a systematic evaluation of four widely used prompting paradigms (Zero-Shot, Few-Shot, Zero-Shot Chain-of-Thought, and Few-Shot Chain-of-Thought) across GPT-3.5, GPT-4, and GPT-4o on the ChartQA dataset. Our framework operates exclusively on structured chart data, isolating prompt structure as the only experimental variable, and evaluates performance using two metrics: Accuracy and Exact Match. Results from 1,200 diverse ChartQA samples […]
arXiv:2601.16235v1 Announce Type: new Abstract: Personalized speech enhancement (PSE) has shown convincing results when it comes to extracting a known target voice among interfering ones. The corresponding systems usually incorporate a representation of the target voice within the enhancement system, which is extracted from an enrollment clip of the target voice with upstream models. Those models are generally heavy as the speaker embedding’s quality directly affects PSE performances. Yet, embeddings generated beforehand cannot account for the variations of […]
The era of large language models (LLMs) is here, bringing with it rapidly evolving libraries like ChromaDB that help augment LLM applications. You’ve most likely heard of chatbots like OpenAI’s ChatGPT, and perhaps you’ve even experienced their remarkable ability to reason about natural language processing (NLP) problems. Modern LLMs, while imperfect, can accurately solve a wide range of problems and provide correct answers to many questions. However, due to the limits of their training and the number of […]
arXiv:2604.07409v1 Announce Type: new Abstract: Layout plays a crucial role in graphic design and poster generation. Recently, the application of deep learning models for layout generation has gained significant attention. This paper focuses on using a GAN-based model conditioned on images to generate advertising poster graphic layouts, requiring a dataset of paired product images and layouts. To address this task, we introduce the Content-aware Graphic Layout Dataset (CGL-Dataset), consisting of 60,548 paired inpainted posters with annotations and 121,000 […]