Idle/Random/whatever Thoughts of a Demented/Idle/Whatever Mind

The Indian Ministry of Health and Family Welfare’s decision to pull "Syrups" out of the exempted list under Schedule K of the Drugs Rules, 1945 , is easily one of the biggest regulatory shifts we’ve seen in years. By dropping the decades old exemption under the Drugs (Fifth Amendment) Rules, 2026 , the government has made it absolute: all liquid oral formulations now strictly require a doctor's prescription. No more walking into a neighborhood chemist to pick up a quick over-the-counter (OTC) bottle of cough formula or an antacid. While the immediate commentary focuses heavily on the raw transactional friction for urban consumers, the truest impact and why this policy is fundamentally necessary, lies across rural India. Historically, Schedule K was a practical compromise for a developing nation. Under Serial No. 13, the law allowed basic "household remedies" to be stocked and sold in remote villages (populations under 1,000) without requiring strict retail pharm...

 1. Multi-Token Prediction Reshapes LLM Training Paradigms - This research proposes that training large language models to predict multiple future tokens simultaneously, rather than just the next one, improves sample efficiency. The approach involves the model predicting the next 'n' tokens at each position in the training data using separate output heads on a shared model base. When used as an auxiliary task, multi-token prediction enhances performance in downstream tasks for both code and natural language models without increasing training time. The benefits are more significant with larger models and persist across multiple training epochs. Notably, this method excels in generative tasks like coding, where a 13B parameter model outperforms next-token models by solving 12% more problems on HumanEval and 17% more on MBPP. Additionally, it aids in developing induction heads and algorithmic reasoning on small tasks, and offers up to 3x faster inference speeds with 4-token predi...

1.  The Illusion of Thinking  - A recent paper from Apple's Machine Learning Research team challenges the idea that Large Reasoning Models (LRMs) can truly "think." The researchers argue that current methods for evaluating these models, which often use standard math and coding problems, are flawed due to potential data contamination and a focus on final answers rather than the reasoning process. Using controllable puzzles to test the models, the study found that even the most advanced LRMs experience a complete collapse in accuracy when the problem's complexity passes a certain point. Counterintuitively, as problems became more difficult, the models' "thinking" effort decreased, suggesting they "give up" rather than reason more deeply. The research identifies a significant gap between mimicking thought-like patterns and genuine, scalable reasoning, questioning the true capabilities of current AI.   My Take : Thinking is a complex p...