Sakana AI
Sakana AI used LLMs to automatically discover new preference optimization algorithms for training other LLMs, a process they call LLM². They discovered Discovered Preference Optimization (DiscoPOP), which outperforms existing methods like DPO across multiple benchmarks. This approach reduces reliance on human researchers to manually design training algorithms and creates a self-referential feedback loop where AI improvements can accelerate future AI development.
404 Media
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4 hours ago
AI companies are bulk-buying millions of old printed books from marketplaces and ISBNdb, a book database service, specifically because pre-2022 books are guaranteed free of AI-generated text that could degrade training models. Since April, book dealers report historic spikes in sales—one seller went from 20 books weekly to hundreds—with large, seemingly random purchases of books with ISBNs, suggesting systematic bulk acquisition. AI companies destroy the physical books during scanning to save storage space, a practice courts have ruled is transformative fair use, allowing them to acquire clean training data while avoiding the contamination risk from internet-scraped text.
CSET Georgetown
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4 hours ago
AI misbehavior results from interactions among five components: training data quality, the model's training objective, neural architecture design, system-level guardrails and sampling strategies, and conversation context. Data biases account for a large portion of failures—a medical imaging model once relied on hospital watermarks rather than anatomy, and a horse-to-zebra converter added zebra stripes to rider clothing. Identifying which component causes misbehavior enables targeted fixes, though improvements in one area can sometimes create vulnerabilities elsewhere.
TLDR
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7 hours ago
BrainCo demonstrated a brain-computer interface platform that lets users control robots through neural signals decoded by AI algorithms in under 200 milliseconds. The system was showcased at the 2026 World Artificial Intelligence Conference in Shanghai, where a person wearing an EEG headset directed a robotic arm to grasp objects with precision. BrainCo also introduced a data-collection system combining brain signals, human demonstrations, and robot execution to address the shortage of high-quality training data for teaching robots complex physical tasks.
