AI and the World's Hard Problems ─ Theoretical computer science
Science and scholarship advanced by AI, by discipline
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History’s long-secret codes are being cracked by amateurs using AI
Non-experts are increasingly using large language models to break historical ciphers, drawing on their ability to run decryption algorithms and search the internet for documents. Carter Leffen, who has no cryptography experience, asked GPT-6 Astra to decode a German army message encrypted 85 years ago, and it produced a solution by morning. Cryptography specialists welcome the interest but are overwhelmed by requests to validate AI solutions, many of which are incorrect.
Multi-level malware subfamily attribution from RGB executable images using attention-augmented deep learning
Malware analysis requires decisions at several levels, from detection to family and fine-grained subfamily attribution. The authors propose a hierarchical deep learning framework that takes RGB images of Windows PE files and performs detection, family classification and 33-subfamily attribution in one multi-output architecture. Four attention-augmented transfer learning models reached 99% binary, 96–97% family-level and 91–95% subfamily-level accuracy, with macro F1-scores up to 0.89 for fine-grained attribution.
Large language models discover complementary heuristics for combinatorial optimization
Combinatorial optimization underpins decisions in manufacturing, logistics and energy, but designing heuristics for each new variant takes expert effort over months, and single-pass LLM code generation often fails. LACE splits algorithm design into schemas, a tool library and a heuristic portfolio, then evolves complementary specialist heuristics within runtime limits. Across 36 classical CO-Bench problems it averaged 0.945, versus 0.870 for the strongest prior LLM-based method and 0.571 for direct prompting.
This game-playing AI is the new champ at Stratego
Researchers built an AI system for Stratego, a two-player board game in which the opponent's pieces are hidden and which is often used to test strategic thinking. Combining efficient training algorithms with new techniques for decisions under hidden information, the system defeated top-ranked human players by a large margin and outperformed the next best models while being cheaper to train. It also beat top human players in other strategy games with different rules.
D4RT: Unified, Fast 4D Scene Reconstruction & Tracking
MIT’s tiny flying robot gets 450% faster with AI
Insect-scale flying robots have typically been slow and limited to simple flight paths. MIT researchers developed an AI-based controller with a two-part control system that enables repeated body flips. Compared with the team's previous best results, the robot's speed rose about 450% and its acceleration about 250%, and it completed 10 somersaults in 11 seconds even under wind disturbances.