A 14th century trickster tale was misread as fact. Breakthroughs, discoveries, and DIY tips sent every weekday. Misinformation surrounding COVID-19 is still a major problem more than five years after its emergence. Even after hundreds of years, our understanding of the Black Death () remains clouded by false narratives. In a study recently published in the, historians at the UK's University of Exeter argue the infamous plague likely didn't move across the continent as quickly as many experts thought.

Camera movement classification (CMC) models trained on contemporary, high-quality footage often degrade when applied to archival film, where noise, missing frames, and low contrast obscure motion cues. We bridge this gap by assembling a unified benchmark that consolidates two modern corpora into four canonical classes and restructures the HISTORIAN collection into five balanced categories. Building on this benchmark, we introduce DGME-T, a lightweight extension to the Video Swin Transformer that injects directional grid motion encoding, derived from optical flow, via a learnable and normalised late-fusion layer. DGME-T raises the backbone's top-1 accuracy from 81.78% to 86.14% and its macro F1 from 82.08% to 87.81% on modern clips, while still improving the demanding World-War-II footage from 83.43% to 84.62% accuracy and from 81.72% to 82.63% macro F1. A cross-domain study further shows that an intermediate fine-tuning stage on modern data increases historical performance by more than five percentage points. These results demonstrate that structured motion priors and transformer representations are complementary and that even a small, carefully calibrated motion head can substantially enhance robustness in degraded film analysis. Related resources are available at https://github.com/linty5/DGME-T.

The radioactive'miracle water' that killed its believers In the 1920s, Radithor promised to cure everything from wrinkles to leukemia, but its unintended results were deadly. While 1920s soda shops offered a plethora of sweet treats, nearby pharmacies served their own tinctures--like Radithor, certified radioactive water. Breakthroughs, discoveries, and DIY tips sent every weekday. William Bailey promised to cure anything that ailed you. " Just a tiny bottle of apparently lifeless, colorless, and tasteless water " was, he advertised in a 1929 pamphlet for his product, Radithor, "the greatest therapeutic force known to mankind."

A fragment of a bronze military diploma from Sardinia, issued by the emperor Trajan to a sailor on a warship, as restored by Aeneas. If you believe the hype, generative artificial intelligence (AI) is the future. However, new research suggests the technology may also improve our understanding of the past. A team of computer scientists from Google DeepMind, working with classicists and archaeologists from universities in the United Kingdom and Greece, described a new machine-learning system designed to help experts to understand ancient Latin inscriptions. Named Aeneas (after the mythical hero of Rome's foundation epic), the system is a generative neural network designed to provide context for Latin inscriptions written between the 7th century BCE and the 8th century CE.

Latin inscriptions from the ancient world can tell us about Roman emperors' decrees and enslaved people's thoughts – if we can read them. Now an artificial intelligence tool is helping historians reconstruct the often fragmentary texts. It can even accurately predict when and where in the Roman Empire a given inscription came from. "Studying history through inscriptions is like solving a gigantic jigsaw puzzle, only this is tens of thousands of pieces more than normal," said Thea Sommerschield at the University of Nottingham in the UK, during a press event. "And 90 per cent of them are missing because that's all that survived for us over the centuries."

It's not the first time AI has been used to join up the missing dots in Roman history. Dr Sommerschield developed Aeneas along with her co-research leader Dr Yannis Assael, an AI specialist at Google DeepMind. It automates the process of contextualising based on parallels, in the blink of an eye. Aeneas draws on a vast database of of 176,000 Roman inscriptions including images and uses a carefully designed AI system to pull up a range of relevant historical parallels, to support the work of historians, according to Dr Assael. "What the historian can't do is assess these parallels in a matter of seconds across tens of thousands of inscriptions, and that is where AI can come in as an assistant."

To do this, Aeneas takes in partial transcriptions of an inscription alongside a scanned image of it. Using these, it gives possible dates and places of origins for the engraving, along with potential fill-ins for any missing text. For example, a slab damaged at the start and continuing with ... us populusque Romanus would likely prompt Aeneas to guess that Senat comes before us to create the phrase Senatus populusque Romanus, "The Senate and the people of Rome." This is similar to how Ithaca works. But Aeneas also cross-references the text with a stored database of almost 150,000 inscriptions, which originated everywhere from modern-day Britain to modern-day Iraq, to give possible parallels--other catalogued Latin engravings that feature similar words, phrases, and analogies.

Historical documents represent an invaluable cultural heritage, yet have undergone significant degradation over time through tears, water erosion, and oxidation. Existing Historical Document Restoration (HDR) methods primarily focus on single modality or limited-size restoration, failing to meet practical needs. To fill this gap, we present a full-page HDR dataset (FPHDR) and a novel automated HDR solution (AutoHDR). Specifically, FPHDR comprises 1,633 real and 6,543 synthetic images with character-level and line-level locations, as well as character annotations in different damage grades. AutoHDR mimics historians' restoration workflows through a three-stage approach: OCR-assisted damage localization, vision-language context text prediction, and patch autoregressive appearance restoration. The modular architecture of AutoHDR enables seamless human-machine collaboration, allowing for flexible intervention and optimization at each restoration stage. Experiments demonstrate AutoHDR's remarkable performance in HDR. When processing severely damaged documents, our method improves OCR accuracy from 46.83% to 84.05%, with further enhancement to 94.25% through human-machine collaboration. We believe this work represents a significant advancement in automated historical document restoration and contributes substantially to cultural heritage preservation. The model and dataset are available at https://github.com/SCUT-DLVCLab/AutoHDR.

The arrival of AI chatbots marks a historical dividing line after which online material can't be completely trusted to be human-created, but how will people look back on this change? While some are urgently working to archive "uncontaminated" data from the pre-AI era, others say it is the AI outputs themselves that we need to record, so future historians can study how chatbots have evolved. Rajiv Pant, an entrepreneur and former chief technology officer at both The New York Times and The Wall Street Journal, says he sees AI as a risk to information such as news stories that form part of the historical record. "I've been thinking about this'digital archaeology' problem since ChatGPT launched, and it's becoming more urgent every month," says Pant. "Right now, there's no reliable way to distinguish human-authored content from AI-generated material at scale. For John Graham-Cumming at cybersecurity firm Cloudflare, information produced before the end of 2022, when ChatGPT launched, is akin to low-background steel. This metal, smelted before the Trinity nuclear bomb test on 16 July 1945, is prized for use in delicate scientific and medical instruments because it doesn't contain faint radioactive contamination from the atomic weapon era that creates noise in readings. Graham-Cumming has created a website called lowbackgroundsteel.ai to archive sources of data that haven't been contaminated by AI, such as a full download of Wikipedia from August 2022. Studies have already shown that Wikipedia today shows signs of huge AI input. "There's a point at which we we did everything ourselves, and then at some point we started to get augmented significantly by these chat systems," he says. "So the idea was to say – you can see it as contamination, or you can see it as a sort of a vault – you know, humans, we got to here.

D4R is a digital platform designed to assist non-technical users, particularly historians, in exploring textual documents through advanced graphical tools for text analysis and knowledge extraction. By leveraging a large language model, D4R translates natural language questions into Cypher queries, enabling the retrieval of data from a Neo4J database. A user-friendly graphical interface allows for intuitive interaction, enabling users to navigate and analyse complex relational data extracted from unstructured textual documents. Originally designed to bridge the gap between AI technologies and historical research, D4R's capabilities extend to various other domains. A demonstration video and a live software demo are available.