Appendix A describes the Unity-based interface implemented in EVAAA, including an environment setup, prefab structures, and object instantiation. Appendix B provides a comprehensive introduction to Essential Variables (EVs), including their design, dynamics, and role in internal state regulation. Appendix C explains the implementation of the reward system and its connection to the balance of internal states. Appendix E outlines the modular configuration to generate EVAAA environments, along with the instructions for environment customization. Appendix F presents the structure and progression of naturalistic training environments. Appendix G describes the design of unseen experimental testbeds for evaluation. Appendix I provides analyses of agent behavior across training and test environments, including emergent behavioral patterns. All code and data are publicly available at: https://github.com/cocoanlab/evaaa A.1 Prefabs Environmental elements such as terrain, resources, obstacles, and predators are implemented as reusable and configurable Unity prefabs. Prefabs are grouped into Agents, Environment, and Materials. Each category includes reusable components for constructing and customizing interactive scenes: Agents (main agent and predators), Environment (terrain and containers), and Materials (varied textures and colors for visual distinction). This modular system enables rapid prototyping, task generation, condition randomization, and reproducible scene setup. Prefabs can be customized through the Unity Editor or programmatically at runtime, and reused across scenes without manual rebuilding.

LLM-as-a-Judge has emerged as a promising tool for automatically evaluating generated outputs, but its reliability is often undermined by potential biases in judgment. Existing efforts to mitigate these biases face key limitations: in-context learning-based methods fail to address rooted biases due to the evaluator's limited capacity for self-reflection, whereas fine-tuning is not applicable to all evaluator types, especially closed-source models. To address this challenge, we introduce the Reasoning-based Bias Detector (RBD), which is a plug-in module that identifies biased evaluations and generates structured reasoning to guide evaluator self-correction. Rather than modifying the evaluator itself, RBD operates externally and engages in an iterative process of bias detection and feedback-driven revision. To support its development, we design a complete pipeline consisting of biased dataset construction, supervision collection, distilled reasoning-based fine-tuning of RBD, and integration with LLM evaluators. We fine-tune four sizes of RBD models, ranging from 1.5B to 14B, and observe consistent performance improvements across all scales. Experimental results on 4 bias types--verbosity, position, bandwagon, and sentiment--evaluated using 8 LLM evaluators demonstrate RBD's strong effectiveness. For example, the RBD-8B model improves evaluation accuracy by an average of 18.5% and consistency by 10.9%, and surpasses prompting-based baselines and fine-tuned judges by 12.8% and 17.2%, respectively.

The kraken-like apex predators were smart, too. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The fossils prove octopuses existed at least 5 million years earlier than originally thought. Breakthroughs, discoveries, and DIY tips sent six days a week. Around 100 million years ago, real kraken-like creatures stalked Earth's prehistoric oceans.

'Lucy' probably needed to watch her back. Researchers led by the University of Iowa have described and named a new crocodile species that roamed a region in Africa more than 3 million years ago. The species is named Lucy's hunter, because it overlapped with the famed Lucy and her hominin kin and would have hunted them. Breakthroughs, discoveries, and DIY tips sent six days a week. Humans have contended with crocodiles for a long time.

Recurrent experience replay in distributed reinforcement learning.

Breakthroughs, discoveries, and DIY tips sent six days a week. Approximately 290 million years ago, a carnivorous dinosaur stomping around present-day Germany had a tummy ache. The Paleozoic predator eventually vomited up its stomach contents, and then hopefully continued to live its best dino life. Unlike most ancient regurgitated meals, this particular mixture of half-eaten prey and digestive bacteria successfully fossilized into what's known as a regurgitalite. In 2021, paleontologists discovered the extremely rare find while working in the famous Bromacker Permian dig site, about 155 miles southwest of Berlin.

Roblox's AI-Powered Age Verification Is a Complete Mess Kids are being identified as adults--and vice versa--on Roblox, while age-verified accounts are already being sold online. Just days after launching, Roblox's much-hyped AI-powered age verification system is a complete mess. Roblox's face scanning system, which estimates peoples' ages before they can access the platform's chat functions, rolled out in the US and other countries around the world last week, after initially launching in a few locations in December. Roblox says it is implementing the system to allow users to safely chat with users of similar ages. But players are already in revolt because they can no longer chat to their friends, developers are demanding Roblox roll back the update, and crucially, experts say that not only is the AI mis-aging young players as adults and vice versa, the system does little to help address the problem it was designed to tackle: the flood of predators using the platform to groom young children.