Hallucination is a persistent issue affecting all large language Models (LLMs), particularly within low-resource languages such as Persian. PerHalluEval (Persian Hallucination Evaluation) is the first dynamic hallucination evaluation benchmark tailored for the Persian language. Our benchmark leverages a three-stage LLM-driven pipeline, augmented with human validation, to generate plausible answers and summaries regarding QA and summarization tasks, focusing on detecting extrinsic and intrinsic hallucinations. Moreover, we used the log probabilities of generated tokens to select the most believable hallucinated instances. In addition, we engaged human annotators to highlight Persian-specific contexts in the QA dataset in order to evaluate LLMs' performance on content specifically related to Persian culture. Our evaluation of 12 LLMs, including open- and closed-source models using PerHalluEval, revealed that the models generally struggle in detecting hallucinated Persian text. We showed that providing external knowledge, i.e., the original document for the summarization task, could mitigate hallucination partially. Furthermore, there was no significant difference in terms of hallucination when comparing LLMs specifically trained for Persian with others.

CDI also offers a plausible approach for automatically making sense of competing arguments in a way that accords with the features enumerated here. This paper is part of an argument that it is now feasible to computationally instantiate a reasonable approximation of a coherence theory of truth [64]: the recent benchmark [12] provides additional quantitative evidence in this direction. By "hard-coding" acceptance of conclusively established propositions, this theory can furthermore be anchored in a correspondence theory of truth [65]. In other words, coherence computations can be required to incorporate privileged information that also coheres with observed reality. While it is easy to imagine attempts to try the same thing with privileged information that does not cohere with observed reality, lies cannot persist when they can easily be unraveled. Even with flawless technology (which this will not be), obstacles will be manifold. For example, in a pluralistic society, legal coherence may actually require sacrificing fairness in some ways [66]. Ultimately, people must decide matters for themselves. It is only reasonable to hope that technology can serve as a reliable tool to help people make their decisions more coherent.

By upgrading an existing deep learning model with the knowledge provided by one of the oldest sets of grayscale satellite imagery, known as CORONA, we improved the AI model's attitude towards the automatic identification of archaeological sites in an envir onment which has been completely transformed in the last five decades, including the complete destruction of many of those same sites. The initial Bing - based convolutional network model was re - trained using CORONA satellite imagery for the district of Abu Ghraib, west of Baghdad, central Mesopotamian floodplain. The results were twofold and surprising. First, the detection precision obtained on the area of interest increased sensibly: in particular, the Intersection - over - Union (IoU) values, at the image segmentation level, surpassed 85%, while the general accuracy in detecting archeological sites reached 90%. Second, our re - trained model allowed the identification of four new sites of archaeological interest (confirmed through field verification), previously not identified by archaeologists with traditional techniques. This has confirmed the efficacy of using AI techniques and the CORONA imagery from the 1960s to discover archaeological sites currently no longer visible, a concrete breakthrough with significant consequences for the study of landscapes with vanishing archaeological evidence induced by anthropization.

We devise an algorithm to generate sets of propositions that objectively instantiate graphs that support coherence-driven inference. We then benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a straightforward transformation of) propositions expressed in natural language, with promising results from a single prompt to models optimized for reasoning. Combining coherence-driven inference with consistency evaluations by neural models may advance the state of the art in machine cognition.

Grading and quality control of Piarom dates, a premium and high-value variety cultivated predominantly in Iran, present significant challenges due to the complexity and variability of defects, as well as the absence of specialized automated systems tailored to this fruit. Traditional manual inspection methods are labor intensive, time consuming, and prone to human error, while existing AI-based sorting solutions are insufficient for addressing the nuanced characteristics of Piarom dates. In this study, we propose an innovative deep learning framework designed specifically for the real-time detection, classification, and grading of Piarom dates. Leveraging a custom dataset comprising over 9,900 high-resolution images annotated across 11 distinct defect categories, our framework integrates state-of-the-art object detection algorithms and Convolutional Neural Networks (CNNs) to achieve high precision in defect identification. Furthermore, we employ advanced segmentation techniques to estimate the area and weight of each date, thereby optimizing the grading process according to industry standards. Experimental results demonstrate that our system significantly outperforms existing methods in terms of accuracy and computational efficiency, making it highly suitable for industrial applications requiring real-time processing. This work not only provides a robust and scalable solution for automating quality control in the Piarom date industry but also contributes to the broader field of AI-driven food inspection technologies, with potential applications across various agricultural products.

Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galicia, the Spanish main producer of cultivated mussels, the opening and closing of the production areas is controlled by a monitoring program. In addition to the closures resulting from the presence of toxicity exceeding the legal threshold, in the absence of a confirmatory sampling and the existence of risk factors, precautionary closures may be applied. These decisions are made by experts without the support or formalisation of the experience on which they are based. Therefore, this work proposes a predictive model capable of supporting the application of precautionary closures. Achieving sensitivity, accuracy and kappa index values of 97.34%, 91.83% and 0.75 respectively, the kNN algorithm has provided the best results. This allows the creation of a system capable of helping in complex situations where forecast errors are more common.

You can become utterly dependent on a new glamorous technology, be it cyber-space, artificial intelligence. . . But does it create a potential achilles heel? Artificial intelligence (AI) has become the technical focal point for advancing naval and Department of Defense (DoD) capabilities. Secretary of the Navy Carlos Del Toro listed AI first among his priorities for innovating U.S. naval forces. Chief of Naval Operations Admiral Michael Gilday listed it as his top priority during his Senate confirmation hearing.2

TEHRAN - Iran's Revolutionary Guard shot down a U.S. surveillance drone Thursday in the Strait of Hormuz, marking the first time the Islamic Republic directly attacked the American military amid tensions over Tehran's unraveling nuclear deal with world powers. The two countries disputed the circumstances leading up to an Iranian surface-to-air missile bringing down the U.S. Navy RQ-4A Global Hawk, an unmanned aircraft with a wingspan larger than a Boeing 737 jetliner and costing over $100 million. Iran said the drone "violated" its territorial airspace, while the U.S. called the missile fire "an unprovoked attack" in international airspace over the narrow mouth of the Persian Gulf and President Donald Trump tweeted that "Iran made a very big mistake!" Trump later appeared to play down the incident, telling reporters in the Oval Office that he had a feeling that "a general or somebody" being "loose and stupid" made a mistake in shooting down the drone. The incident immediately heightened the crisis already gripping the wider region, which is rooted in Trump withdrawing the U.S. a year ago from Iran's 2015 nuclear deal and imposing crippling new sanctions on Tehran.

TEHRAN, Iran – Iran's state-run IRNA news agency says the country's Revolutionary Guard has shot down a U.S. drone. The U.S. military declined to immediately comment. IRNA said Thursday the drone was hit when it entered Iranian airspace near the Kouhmobarak district in southern Iran's Hormozgan province. IRNA, citing the paramilitary Revolutionary Guard, identified the drone as an RQ-4 Global Hawk. Bill Urban, a U.S. Central Command spokesman, declined to comment when asked if an American drone was shot down.

TEHRAN - Iran's Revolutionary Guard said Thursday it shot down a U.S. drone amid heightened tensions between Tehran and Washington over its collapsing nuclear deal. The U.S. military declined to immediately comment. The reported shootdown of the RQ-4 Global Hawk comes after the U.S. military previously alleged Iran fired a missile at another drone last week that responded to the attack on two oil tankers near the Gulf of Oman. The U.S. blames Iran for the attack on the ships, which Tehran denies. The attacks come against the backdrop of heightened tensions between the U.S. and Iran following President Donald Trump's decision to withdraw from Tehran's nuclear deal with world powers a year ago. The White House separately said it was aware of reports of a missile strike on Saudi Arabia amid a campaign targeting the kingdom by Yemen's Iranian-allied Houthi rebels.