You're viewing the US edition You can switch to the UK, AU or IE homepage at any time using this menu. Transgender New York Times executive who was shot dead'by his in-laws' abused BOTH his young children and left them with head injuries, wife claims Christopher Reeve's son Will, 34, reveals rare testicular cancer diagnosis Madonna's family make intervention: After forgetful and embarrassing VMAs comeback, insiders reveal clash between'control freak' star and her team Nobody wants to say this about Cindy Crawford after her son Presley Gerber's death... but maybe someone should: MAUREEN CALLAHAN'Triple flood threat' triggers urgent warnings for millions as massive storm engulfs 15 US states Taylor Swift's unreleased suicide note song: Tracks the world was never meant to hear... including one her co-writer said is'the most chilling he's ever heard' Riley Gaines suffers devastating blow in bitter legal fight against transgender athletes in women's sport at hands of Biden-appointed ...

Environment Animals Pets Dogs Dog brains act surprisingly human when told'no' Self-control is a serious survival skill. 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. Self-control can be seen in the brain's frontal lobes. Sit, stay, roll over, come, fetch, and no. All common commands that responsible dog owners use to train and communicate with their pets.

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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.