Internet of Things (IoT) networks generate diverse and high-volume traffic that reflects both normal activity and potential threats. Deriving meaningful insight from such telemetry requires cross-layer interpretation of behaviors, protocols, and context rather than isolated detection. This work presents an LLM-powered AI agent framework that converts raw packet captures into structured and semantically enriched representations for interactive analysis. The framework integrates feature extraction, transformer-based anomaly detection, packet and flow summarization, threat intelligence enrichment, and retrieval-augmented question answering. An AI agent guided by a large language model performs reasoning over the indexed traffic artifacts, assembling evidence to produce accurate and human-readable interpretations. Experimental evaluation on multiple IoT captures and six open models shows that hybrid retrieval, which combines lexical and semantic search with reranking, substantially improves BLEU, ROUGE, METEOR, and BERTScore results compared with dense-only retrieval. System profiling further indicates low CPU, GPU, and memory overhead, demonstrating that the framework achieves holistic and efficient interpretation of IoT network traffic.

Effective storytelling relies on a delicate balance between meeting the reader's prior expectations and introducing unexpected developments. In the domain of detective fiction, this tension is known as fair play, which includes the implicit agreement between the writer and the reader as to the range of possible resolutions the mystery story may have. In this work, we present a probabilistic framework for detective fiction that allows us to define desired qualities. Using this framework, we formally define fair play and design appropriate metrics for it. Stemming from these definitions is an inherent tension between the coherence of the story, which measures how much it ``makes sense'', and the surprise it induces. We validate the framework by applying it to LLM-generated detective stories. This domain is appealing since we have an abundance of data, we can sample from the distribution generating the story, and the story-writing capabilities of LLMs are interesting in their own right. Results show that while LLM-generated stories may be unpredictable, they generally fail to balance the trade-off between surprise and fair play, which greatly contributes to their poor quality.

We prove that perfect hallucination control in large language models is mathematically impossible. No LLM inference mechanism can simultaneously achieve truthful response generation, semantic information conservation, relevant knowledge revelation, and knowledge-constrained optimality. This impossibility is fundamental, arising from the mathematical structure of information aggregation itself rather than engineering limitations. The proof spans three mathematical frameworks: auction theory, proper scoring theory for probabilistic predictions, and log-sum-exp analysis for transformer architectures. In each setting, we demonstrate that information aggregation creates unavoidable violations of conservation principles. The Jensen gap in transformer probability aggregation provides a direct measure of this impossibility. These results reframe hallucination from an engineering bug to an inevitable mathematical feature of distributed intelligence. There are fundamental trade-offs between truthfulness, knowledge utilization, and response completeness, providing principled foundations for managing rather than eliminating hallucination. This work reveals deep connections between neural network inference, philosophy of knowledge and reasoning, and classical results in game theory and information theory, opening new research directions for developing beneficial AI systems within mathematical constraints.

The article "Revelations: A Decidable Class of POMDP with Omega-Regular Objectives" won an Outstanding Paper Award at the AAAI 2025 conference, a prestigious international conference about artificial intelligence. This year, only three papers received such an award out of 3,000 accepted and 12,000 submitted! This recognition crowns the results of research initiated in Bordeaux (France) within the Synthèse team at the Bordeaux Computer Science Research Laboratory (LaBRI), where four of the authors work: Marius Belly, Nathanaël Fijalkow, Hugo Gimbert, and Pierre Vandenhove. The work also involved researchers from Paris (Florian Horn) and Antwerp (Guillermo A. Pérez). The article is freely available on arXiv, and this post outlines its main ideas.

Chilling words in the Bible's Book of Revelation seem to describe events in the early Christian world - but are they actually predicting a nuclear explosion during a future world war? 'Something like a huge mountain, all ablaze, was thrown into the sea… a third of the living creatures in the sea died,' the ancient text reads. The Book of Revelation (written towards the end of the first century AD) is packed with imagery which could describe modern weapons technologies such as helicopter or drone fleets and even robot soldiers, explained Jared Brock, author of'A Devil Named Lucifer.' Brock said that it's very easy to read Revelation and map the fall of previous empires onto it, such as the Roman Empire or the British Empire. However, the author wonders if it could be describing the future fall of the United States instead. Brock studied scripture intently as part of the research for his book - and found echoes of recent world events in the Book of Revelation and throughout the Bible.

Digging up treasures from the past is an exciting business. So exciting, in fact, it's kept players coming back to the Tomb Raider series for nearly three decades. The original trilogy was successfully remastered and rereleased last year. Now a new collection has been recovered from the attic and put on show, like a family heirloom on the Antiques Roadshow. But will this turn out to be the gaming equivalent of a priceless Ming vase?

So long as we can say'This is the worst,' " go the lines from King Lear quoted in Emily St. John Mandel's 2014 novel Station Eleven. Any stories we tell about the end of the world will have to be fictional, since once the real thing occurs, no one will be around to describe it. As the British journalist Dorian Lynskey relates in his erudite, delightfully witty, and strangely cheering new book, Everything Must Go: The Stories We Tell About the End of the World, the fact that we can only ever speculate on the subject makes us speculate all the more frantically. "There is simply no end of ends," Lynskey writes of the books, movies, TV shows, pop songs, and video games we've created to depict the apocalypse--or its near misses and the aftermaths thereof. Station Eleven is often described as "postapocalyptic," but as Lynskey points out, the more accurate term would be "postcatastrophic." That's a better label for stories in which "the world has not ended, but a world has, creating a blank ...

It's a mite soon to start grieving, but scientists now project that life on Earth will probably end in about a billion years. A Monday in February, 1,000,002,025, would be my guess. On that inhospitable day, give or take a few million years, the sun will become so hot that the oceans will boil, Earth's oxygen will disappear, and photosynthesis will cease, as will all living things. We should be so lucky. There's a pretty fair chance that life could be wiped out well before then--say, in early June, 2034, or on a cloudy Sunday in November, 3633. Plenty of people do, as it turns out, and, if you want to know who they are, Dorian Lynskey's "Everything Must Go: The Stories We Tell About the End of the World" (Pantheon) is a good place to start. Lynskey, a British journalist and podcaster, has assembled biological, geological, archeological, literary, and cinematic permutations of existential finales, leaving no stone unturned, be it meteor, comet, or asteroid. If a book, a song, a story, a film, a headline, a title, or a study has "world" and "end" in it, Lynskey has unearthed it.

Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making. We are interested in constructing algorithms with theoretical guarantees to determine whether the agent has a strategy ensuring a given specification with probability 1. This well-studied problem is known to be undecidable already for very simple omega-regular objectives, because of the difficulty of reasoning on uncertain events. We introduce a revelation mechanism which restricts information loss by requiring that almost surely the agent has eventually full information of the current state. Our main technical results are to construct exact algorithms for two classes of POMDPs called weakly and strongly revealing. Importantly, the decidable cases reduce to the analysis of a finite belief-support Markov decision process. This yields a conceptually simple and exact algorithm for a large class of POMDPs.

I'll tell you what mine is: death. I am not really built for battle – I need five cups of coffee just to function and I have terrible allergies. My body can't even handle pollen, it's not going to do well with nuclear war. Plus, even if I was hardier – who wants to live a few extra months in a completely destroyed world? As you have probably noticed bunkers have become the ultimate status symbol among the 1%.