Who hid the 409 rubles during the Russian Revolution remains a mystery. Most of the coins are 10-ruble gold pieces minted prior to the Russian Revolution. Breakthroughs, discoveries, and DIY tips sent six days a week. A remarkable trove of Russian history is finally seeing the light of day after spending nearly 110 years hidden underground. While surveying a historic home in the small town of Torzhok about 135 miles northwest of Moscow, archaeologists discovered a clay container filled with 409 gold coins buried beneath the structure's foundation.

However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques.

A mixture of archeology and chemistry brings the aroma of mummification to museums. Breakthroughs, discoveries, and DIY tips sent six days a week. Visiting a museum could soon be a truly multisensory experience--smells included. Thanks to recent advances in the field of biomolecular archeology, scientists can now detect traces of molecular fingerprints on ancient artifacts. From these tiny particles, scientists can determine how the objects may have smelled .

Treasures unearthed by hundreds of archaeologists so far during work on the controversial planned HS2 train line have been shown exclusively to the BBC. The 450,000 objects, which are being held in a secret warehouse, include a possible Roman gladiator's tag, a hand axe that may be more than 40,000 years old and 19th Century gold dentures. It is an unprecedented amount and array of items, which will yield new insights into Britain's past, says the Centre for British Archaeology. Major building developments in the UK need land to be assessed by archaeologists as part of the planning process, to protect heritage sites. Since 2018 around 1,000 archaeologists have been involved in 60 digs along the route HS2 is set to take between London to Birmingham.

The site, a few kilometers from her own hut in Ipatsé, a Kuikuro village in the Xingu Indigenous territory, was once the backyard of her great-grandparents' house. As she scrapes the brown earth with a trowel, she soon spots a black ceramic shard. It is only about the size of her palm, and this is her first day ever on an archaeological excavation. But she immediately recognizes what the object once was. "It's an alato," she says, showing the piece to a group of archaeologists and other Kuikuro who have gathered to watch the excavation in the village of Anitahagu. An alato, Yamána explains, is a large pan used to cook beiju, a white flatbread made with yucca flour that's eaten almost every day in her village. Her grandmother still has one in the backyard fire pit where she prepares most meals, just as countless Kuikuro women did before her. This alato likely belonged to her great-grandmother on her mother's side.

However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques.

In this work, we present a retrieval-augmented generation (RAG)-based system for provenance analysis of archaeological artifacts, designed to support expert reasoning by integrating multimodal retrieval and large vision-language models (VLMs). The system constructs a dual-modal knowledge base from reference texts and images, enabling raw visual, edge-enhanced, and semantic retrieval to identify stylistically similar objects. Retrieved candidates are synthesized by the VLM to generate structured inferences, including chronological, geographical, and cultural attributions, alongside interpretive justifications. W e evaluate the system on a set of Eastern Eurasian Bronze Age artifacts from the British Museum. Expert evaluation demonstrates that the system produces meaningful and interpretable outputs, offering scholars concrete starting points for analysis and significantly alleviating the cognitive burden of navigating vast comparative corpora.

The game I'm most looking forward to right now is Big Walk, the latest title from House House, creators of the brilliant Untitled Goose Game. A cooperative multiplayer adventure where players are let loose to explore an open world, I'm interested to see what emergent gameplay comes out of it. Could Big Walk allow for a kind of community archaeology with friends? When games use environmental storytelling in their design – from the positioning of objects to audio recordings or graffiti – they invite players to role play as archaeologists. Game designer Ben Esposito infamously joked back in 2016 that environmental storytelling is the "art of placing skulls near a toilet" – which might have been a jab at the tropes of games like the Fallout series, but his quip demonstrates how archaeological gaming narratives can be.

Archaeological ceramics are a valuable source of information for reconstructing the customs, exchanges and social relationships of ancient populations, as well as for dating archaeological contexts (Sinopoli 1991; Peroni 1994; Steiner and Allason-Jones 2005; Vidale 2007; Orton and Hughes 2013; Hunt 2016). However, in order to turn a ceramic fragment into a rich source of scientific information, a long process of study and elaboration is required: once recovered in an excavation, the ceramic fragment is washed, catalogued, drawn and made ready for publication through the preparation of tables and figures that allow its correct interpretation and comparison with other archaeological contexts. Archaeological drawing is a fundamental and well-established tool in archaeological practice, and new technologies and methods are emerging to automate, standardise and speed up this process as much as possible. An example of this is the LAD (Laser Aided Profiler - Demján, Pavúk, and Roosevelt 2023), a tool that allows ceramic fragments to be'drawn' quickly and accurately using a laser beam. Over time, however, many drawings were made by hand using traditional tools such as pencils and then had to be'inked' and made ready for publication. Traditionally, this post-process was done by hand with Indian ink, and nowadays digital drawing programmes are used. This process is however extremely time-consuming and can often discourage the publication of new contexts due to the difficulties in terms of time and resources needed for inking. Generative AI can help to achieve this task, using complex image translation operation. Today, AI is permeating business, creativity and everyday life (Elliott 2019; Le et al. 2020; Varghese, Raj, and Venkatesh 2022; Azatbekova

Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only focused only on specific subfields of archaeology. Our review examined an exhaustive corpus of 135 articles published between 1997 and 2022. We observed a significant increase in the number of relevant publications from 2019 onwards. Automatic structure detection and artefact classification were the most represented tasks in the articles reviewed, followed by taphonomy, and archaeological predictive modelling. From the review, clustering and unsupervised methods were underrepresented compared to supervised models. Artificial neural networks and ensemble learning account for two thirds of the total number of models used. However, if machine learning is gaining in popularity it remains subject to misunderstanding. We observed, in some cases, poorly defined requirements and caveats of the machine learning methods used. Furthermore, the goals and the needs of machine learning applications for archaeological purposes are in some cases unclear or poorly expressed. To address this, we proposed a workflow guide for archaeologists to develop coherent and consistent methodologies adapted to their research questions, project scale and data. As in many other areas, machine learning is rapidly becoming an important tool in archaeological research and practice, useful for the analyses of large and multivariate data, although not without limitations. This review highlights the importance of well-defined and well-reported structured methodologies and collaborative practices to maximise the potential of applications of machine learning methods in archaeology.