An Immigration and Customs Enforcement (ICE) officer has shot a man in the leg in the US city of Minneapolis, where an ICE agent shot dead a woman last week. In a statement, the Department of Homeland Security (DHS) said federal officers initially pursued the man in a car chase because he was illegally in the US from Venezuela. The City of Minneapolis confirmed a man was shot and taken to hospital for non-life threatening injuries. An ICE officer was also taken to hospital to be treated for injuries, the DHS said. Minneapolis city officials said on X: We understand there is anger.

Are world models a necessary ingredient for flexible, goal-directed behaviour, or is model-free learning sufficient? We provide a formal answer to this question, showing that any agent capable of generalizing to multi-step goal-directed tasks must have learned a predictive model of its environment. We show that this model can be extracted from the agent's policy, and that increasing the agents performance or the complexity of the goals it can achieve requires learning increasingly accurate world models. This has a number of consequences: from developing safe and general agents, to bounding agent capabilities in complex environments, and providing new algorithms for eliciting world models from agents.

Multi-agents has exhibited significant intelligence in real-word simulations with Large language models (LLMs) due to the capabilities of social cognition and knowledge retrieval. However, existing research on agents equipped with effective cognition chains including reasoning, planning, decision-making and reflecting remains limited, especially in the dynamically interactive scenarios. In addition, unlike human, prompt-based responses face challenges in psychological state perception and empirical calibration during uncertain gaming process, which can inevitably lead to cognition bias. In light of above, we introduce PolicyEvol-Agent, a comprehensive LLM-empowered framework characterized by systematically acquiring intentions of others and adaptively optimizing irrational strategies for continual enhancement. Specifically, PolicyEvol-Agent first obtains reflective expertise patterns and then integrates a range of cognitive operations with Theory of Mind alongside internal and external perspectives. Simulation results, outperforming RL-based models and agent-based methods, demonstrate the superiority of PolicyEvol-Agent for final gaming victory. Moreover, the policy evolution mechanism reveals the effectiveness of dynamic guideline adjustments in both automatic and human evaluation.

Apple is delaying its updated version of Siri that understands personal context and can take action inside of apps, according to a statement the company shared with Daring Fireball. The company didn't offer a date as to when the upgrades to Siri will actually launch beyond that they're "rolling them out in the coming year." Here's the full statement reproduced below: Siri helps our users find what they need and get things done quickly, and in just the past six months, we've made Siri more conversational, introduced new features like type to Siri and product knowledge, and added an integration with ChatGPT. We've also been working on a more personalized Siri, giving it more awareness of your personal context, as well as the ability to take action for you within and across your apps. It's going to take us longer than we thought to deliver on these features and we anticipate rolling them out in the coming year.

'Gutfeld!' guest host Kat Timpf and the panel discuss the ongoing'drone drama.' New York and New Jersey residents are far from the only people having issues with drones. The NFL is in the midst of its own fight against the devices and has called on congressional lawmakers to act. The leagues hope lawmakers will pass a bill to help curb the number of devices that violate airspace on gamedays. A drone flies in the air as it holds an NFL football between the NFC and AFC during the Pro Bowl Skills Showdown at Wide World of Sports on Jan. 25, 2017 in Orlando, Florida.

Alert fatigue is a common issue faced by software teams using the DevSecOps paradigm. The overwhelming number of warnings and alerts generated by security and code scanning tools, particularly in smaller teams where resources are limited, leads to desensitization and diminished responsiveness to security warnings, potentially exposing systems to vulnerabilities. This paper explores the potential of LLMs in generating actionable security reports that emphasize the financial impact and consequences of detected security issues, such as credential leaks, if they remain unaddressed. A survey conducted among developers indicates that LLM-generated reports significantly enhance the likelihood of immediate action on security issues by providing clear, comprehensive, and motivating insights. Integrating these reports into DevSecOps workflows can mitigate attention saturation and alert fatigue, ensuring that critical security warnings are addressed effectively.