Utilizing mutual learning can effectively enhance the performance of peer BNNs.

Automatically generating novel and interesting games is a complex task.

Environment Animals Wildlife World's smallest possum may be hiding in South Australia The tiny mammal weighs less than one pound. Breakthroughs, discoveries, and DIY tips sent six days a week. Weighing less than one pound, the little pygmy possum () is one of the smallest mammals in Australia. These miniscule mammals feed on nectar, pollen, and insects, and differ from opossums . Opossums live in the United States and parts of Canada and have a bare tail instead of a furry tail.

We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs.

The biodegradable design could help keep plastic from becoming fish food. Breakthroughs, discoveries, and DIY tips sent every weekday. Chances are sushi aficionados have left a restaurant take-out in tow and with a handful of adorable, but environmentally problematic, fish-shaped soy sauce packets. These single-use plastic " shoyu-tai " drip bottles are as iconic as they are convenient, but their small size and disposability mean they often end up sliding down sinks and into drains. Once in the big blue, they slowly break down into microplastics that are then eaten by fish .

-- Current medical language models, adapted from large language models (LLMs), typically predict ICD code - based diagnosis from electronic health records (EHRs) because these labels are readily available. However, ICD codes do not capture the nuanced, context - rich reasoning clinicians use for diagnosis. Clinicians synthesize diverse patient data and reference clinical practice guidelines (CPGs) to make evidence - based decisions. This misalignment limits the clinical utility of existing models. We introduce GARMLE - G, a Generation - Augmented Retrieval framework that grounds medical language model outp uts in authoritative CPGs. Unlike conventional Retrieval - Augmented Generation based approaches, GARMLE - G enables hallucination - free outputs by directly retrieving authoritative guideline content without relying on model - generated text. It (1) integrates LLM predictions with EHR data to create semantically rich queries, (2) retrieves relevant CPG knowledge snippets via embedding similarity, and (3) fuses guideline content with model output to generate clinically aligned recommendations. A prototype system for hypertension diagnosis was developed and evaluated on multiple metrics, demonstrating superior retrieval precision, semantic relevance, and clinical guideline adherence compared to RAG - based baselines, while maintaining a lightweight architecture suitable for localized healthcare deployment. This work provides a scalable, low - cost, and hallucination - free method for grounding medical language models in evidence - based clinical practice, with strong potential for broader clinical deployment. The research reported in this paper is financially supported by the National Natural Science Foundation of China (62276156), the project of Shandong Provincial Natural Science Foundation (ZR2024LZH005), the Taishan Scholar Program of Shandong Province of China (No.tsq nz20240809), and the Excellent Youth Foundation of Shandong Natural Science Foundation (2024HWYQ - 055). Wenhao Li is with Shandong Normal University, Jinan, China, 250358 (email: lwh@sdnu.edu.cn) Hongkuan Zhang is with Shandong Normal University, Jinan, China, 250358 (email: 2024217028@stu.sdnu.edu.cn) In the healthcare sector, language models and related tools, such as ChatGPT and ClinicalBERT, have been increasingly applied across multiple scenarios, including disease prediction, clinical decision support, patient interaction, drug discovery, and personalized medicine, significantly driving innovation and transformation in medical technology [1, 2] . As a fundamental task in healthcare, disease diagnosis refers to the process by which health professionals identify the most likely disease or disorder causing a patient's symptoms [3] .

Policy-gradient methods have received increased attention recently as a mechanism for learning to act in partially observable environments. They have shown promise for problems admitting memoryless policies but have been less successful when memory is required. In this paper we develop several improved algorithms for learning policies with memory in an infinite-horizon setting -- directly when a known model of the environment is available, and via simulation otherwise. We compare these algorithms on some large POMDPs, including noisy robot navigation and multi-agent problems.