As users increasingly interact with large language models (LLMs) using private information, secure and encrypted communication becomes essential. Homomorphic encryption (HE) provides a principled solution by enabling computation directly on encrypted data. Although prior work has explored aspects of running LLMs under HE, the challenge of text generation, particularly next-token prediction, has received limited attention and remains a key obstacle to practical encrypted interaction. In this work, we propose a TSP-based token reordering strategy to address the difficulties of encrypted text generation, together with a post-processing step that further reduces approximation error. Theoretical analysis and experimental results demonstrate that our method prevents collapse, improves coherence in generated text, and preserves data privacy throughout. Overall, our contributions advance the feasibility of practical and privacy-preserving LLM inference.

Science fiction legend Ursula K. Le Guin is honoured with a new collection out this month, and sci-fi fans can also look forward to fiction from astronaut Chris Hadfield and award-winning authors Ken Liu and Mary Robinette Kowal Like many of you, no doubt, Ursula K. Le Guin is one of my favourite sci-fi writers. So I am really excited about a collection out this month that brings together the maps she would draw when starting a story, and also celebrates her brilliant and wise writing. Not least because we've just read with the New Scientist Book Club: do come and join us and share your thoughts on this classic novel with fellow fans! The sci-fi out this month looks forward as well as back, though. Ken Liu brings us a thriller set in the near future, and I'm keen to read Megha Majumdar's tale of a flooded Kolkata and a desperate mother.

Sir Lucian Grainge, the chairman and C.E.O. of Universal Music Group, the largest music company in the world, is curious, empathetic, and, if not exactly humble, a master of the humblebrag. His superpower is his humanity. A sixty-three-year-old Englishman, who was knighted in 2016 for his contributions to the music industry and has topped Billboard's Power 100 list of music-industry players several times in the past decade, Grainge is compact and a bit chubby, with alert eyes behind owlish glasses. He isn't trying to be noticed. He presides over a public company worth more than fifty billion dollars, but he could be a small-business owner who sells music in a London shop, as did his father, Cecil.

Solving this problem can improve precision by removing duplicates, and can enrich detail by (for example) merging a phone Location matters in many businesses and services today, number from one record with the hours of operation particularly for transportation and delivery, scenarios from another, once these records are known to refer in which it is important to find the correct pickup to the same thing. This problem is referred to as entity and drop-off locations very quickly. User experience resolution (see (Talburt, 2011)), and it occurs with can be negatively affected if the location information various datasets, including those representing people, is inaccurate or insufficient. Inaccuracies products, works of literature, etc. can originate from imprecise GPS data, manual error happening in the process of data entry, or the lack of For Grab, one entity resolution problem that arises effective data quality control. Insufficiencies can also for spatial data is the alignment of transportation destinations take many forms, including lack of coverage, and lack and restaurants. Currently Grab maintains of detail -- for example, we may know the latitude two tables separately for transportation and food delivery, and longitude of a restaurant location in a mall, but because each use case requires some specific this might not include information about where passengers features, i.e., food delivery needs information about should be dropped off, or where a delivery the estimated delivery time, cuisine types, and opening courier should park to collect food for delivery. Or hours which are absent in the POI table. However, the location of a business may be known, but not its it is highly likely that some entities from both tables contact details or opening hours.