Large Language Models (LLMs) generate fluent, plausible text that can mislead users into mistaking simulated coherence for genuine understanding. This paper introduces the Epistemic Suite, a post-foundational diagnostic methodology for surfacing the epistemic conditions under which AI outputs are produced and received. Rather than determining truth or falsity, the Suite operates through twenty diagnostic lenses, applied by practitioners as context warrants, to reveal patterns such as confidence laundering, narrative compression, displaced authority, and temporal drift. It is grounded in three design principles: diagnosing production before evaluating claims, preferring diagnostic traction over foundational settlement, and embedding reflexivity as a structural requirement rather than an ethical ornament. When enacted, the Suite shifts language models into a diagnostic stance, producing inspectable artifacts-flags, annotations, contradiction maps, and suspension logs (the FACS bundle)-that create an intermediary layer between AI output and human judgment. A key innovation is epistemic suspension, a practitioner-enacted circuit breaker that halts continuation when warrant is exceeded, with resumption based on judgment rather than rule. The methodology also includes an Epistemic Triage Protocol and a Meta-Governance Layer to manage proportionality and link activation to relational accountability, consent, historical context, and pluralism safeguards. Unlike internalist approaches that embed alignment into model architectures (e.g., RLHF or epistemic-integrity proposals), the Suite operates externally as scaffolding, preserving expendability and refusal as safeguards rather than failures. It preserves the distinction between performance and understanding, enabling accountable deliberation while maintaining epistemic modesty.

Germany's Munich airport has resumed operations after drone sightings led to the cancellation of 17 flights, the diversion of 15 others and the stranding of some 3,000 passengers. Flights had restarted by early Friday, with flight tracking websites showing planes departing the airport at about 5:50am (03:50 GMT). At least 19 Lufthansa flights were affected, either cancelled or re-routed, because of the airport suspension, the spokesperson added. Earlier, the airport said that drone sightings were first reported by German air traffic control at 10:18pm local time [20:18 GMT] on Thursday, leading initially to a restriction on flights, which was then upgraded to a full suspension. Germany's DPA news agency said police reported that several people had seen a drone near the airport, with later sightings of a drone over the airport grounds.

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by a finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months, and is a significant cost in practice. We have developed a Bayesian optimization system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing a generalized inverse of a high-dimensional non-linear function that does not require e.g. gradient information, which is often unavailable from discipline models. We furthermore develop a two-tier convergence criterion based on (i) convergence to a solution optimally satisfying all specified design criteria, or (ii) convergence to a minimum-norm solution. We demonstrate the proposed approach on a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient, and that the novel convergence criteria can be implemented straightforwardly based on existing concepts and subroutines in popular Bayesian optimization software packages.

If, like me, you suffer from motion sickness, then you know just how quickly a trip down Britain's winding back roads can turn into a nausea-inducing nightmare. But if you struggle to hold on to your lunch as the car starts to lurch, there may soon be a solution. ClearMotion, a Boston-based startup, claims that its latest generation of cutting-edge suspension can'eliminate motion sickness' for good. So, with anti-nausea tablets in hand, MailOnline's reporter, Wiliam Hunter, took a trip to their Warwickshire testing facility to try it for himself. With compact motors tucked away above each wheel and a sophisticated onboard computer, the system can push and pull the wheels to cancel out bumps in the road.

Big Chinese tech companies appear to have turned off some AI functions to prevent cheating during the country's highly competitive university entrance exams. More than 13.3 million students are sitting the four-day gaokao exams, which began on Saturday and determine if and where students can secure a limited place at university. This year, students hoping to get some assistance from increasingly advanced AI tools have been stymied. In screenshots shared online, one Chinese user posted a photo of an exam question to Doubao, owned by TikTok's parent company, ByteDance. The app responded: "During the college entrance examination, according to relevant requirements, the question answering service will be suspended".

Kyiv, Ukraine – On Sunday, a top Russian security official in Moscow lauded dozens of servicemen who used an abandoned natural gas pipeline as a tunnel to infiltrate a Ukraine-occupied area in the western Russian region of Kursk. "The lid of a boiling cauldron is almost closed! Good job!" Dmitry Medvedev, who served as president and prime minister before becoming deputy head of Russia's Security Council, wrote on Telegram. But a Ukrainian serviceman deployed in Kursk offered a starkly different version of how the Russians barely got out of the pipeline on Saturday – only to be reportedly killed en masse. "Some suffocated right [in the pipeline], some turned back. About a hundred came out in our rear, split into two groups and were almost immediately ambushed by our special forces. And [also killed by] a massive squall of artillery," Evhen Sazonov wrote on Telegram.

This paper presents the analytic modeling of mobile heavy-duty manipulators with actively articulated suspension and its optimal control to maximize its static and dynamic stabilization. By adopting the screw theory formalism, we consider the suspension mechanism as a rigid multibody composed of two closed kinematic chains. This mechanical modeling allows us to compute the spatial inertial parameters of the whole platform as a function of the suspension's linear actuators through the articulated-body inertia method. Our solution enhances the computation accuracy of the wheels' reaction normal forces by providing an exact solution for the center of mass and inertia tensor of the mobile manipulator. Moreover, these inertial parameters and the normal forces are used to define metrics of both static and dynamic stability of the mobile manipulator and formulate a nonlinear programming problem that optimizes such metrics to generate an optimal stability motion that prevents the platform's overturning, such optimal position of the actuator is tracked with a state-feedback hydraulic valve control. We demonstrate our method's efficiency in terms of C++ computational speed, accuracy and performance improvement by simulating a 7 degrees-of-freedom heavy-duty parallel-serial mobile manipulator with four wheels and actively articulated suspension.

This paper establishes a connection between the fields of machine learning (ML) and philosophy concerning the phenomenon of behaving neutrally. It investigates a specific class of ML systems capable of delivering a neutral response to a given task, referred to as abstaining machine learning systems, that has not yet been studied from a philosophical perspective. The paper introduces and explains various abstaining machine learning systems, and categorizes them into distinct types. An examination is conducted on how abstention in the different machine learning system types aligns with the epistemological counterpart of suspended judgment, addressing both the nature of suspension and its normative profile. Additionally, a philosophical analysis is suggested on the autonomy and explainability of the abstaining response. It is argued, specifically, that one of the distinguished types of abstaining systems is preferable as it aligns more closely with our criteria for suspended judgment. Moreover, it is better equipped to autonomously generate abstaining outputs and offer explanations for abstaining outputs when compared to the other type.

Large Language Models (LLMs) are capable of producing content that perpetuates stereotypes, discrimination, and toxicity. The recently proposed moral self-correction is a computationally efficient method for reducing harmful content in the responses of LLMs. However, the process of how injecting self-correction instructions can modify the behavior of LLMs remains under-explored. In this paper, we explore the effectiveness of moral self-correction by answering three research questions: (1) In what scenarios does moral self-correction work? (2) What are the internal mechanisms of LLMs, e.g., hidden states, that are influenced by moral self-correction instructions? (3) Is intrinsic moral self-correction actually superficial? We argue that self-correction can help LLMs find a shortcut to more morally correct output, rather than truly reducing the immorality stored in hidden states. Through empirical investigation with tasks of language generation and multi-choice question answering, we conclude: (i) LLMs exhibit good performance across both tasks, and self-correction instructions are particularly beneficial when the correct answer is already top-ranked; (ii) The morality levels in intermediate hidden states are strong indicators as to whether one instruction would be more effective than another; (iii) Based on our analysis of intermediate hidden states and task case studies of self-correction behaviors, we are first to propose the hypothesis that intrinsic moral self-correction is in fact superficial.

Planetary exploration requires traversal in environments with rugged terrains. In addition, Mars rovers and other planetary exploration robots often carry sensitive scientific experiments and components onboard, which must be protected from mechanical harm. This paper deals with an active suspension system focused on chassis stabilisation and an efficient traversal method while encountering unavoidable obstacles. Soft Actor-Critic (SAC) was applied along with Proportional Integral Derivative (PID) control to stabilise the chassis and traverse large obstacles at low speeds. The model uses the rover's distance from surrounding obstacles, the height of the obstacle, and the chassis' orientation to actuate the control links of the suspension accurately. Simulations carried out in the Gazebo environment are used to validate the proposed active system.