Lin Hsin Hsin Artificial Intelligence Center
FRONTIER MODELS
Where Do We Go From Here
🗣
Addressing the Wastage
The industry just woke up, recognizing this zero optimization flaw and moving towards architectures that do leverage frequency or sparsity explicitly
📍 Sparse Attention
New mechanisms such as Sparse Attention select only a constant number of key-value pairs to attend to, effectively ignoring low-probability (low-frequency) connections to reduce complexity from quadratic to linear.
📍 Textual Frequency Law (TFL)
Recent 2026 research proposes that models should explicitly prefer high-frequency textual data for fine-tuning and prompting. Methods like Curriculum Textual Frequency Training (CTFT) order training data by frequency to optimize learning, acknowledging that current random-mixture training is suboptimal
📍 Recursive/Agentic Approaches
Instead of feeding 300k words directly, systems like RLMs use the model to write code that searches the text. This bypasses the combinatorial explosion by only loading high-frequency/relevant chunks into the context window, effectively implementing the optimization current transformers lack natively.
Conclusion
Current Transformer-based Frontier Models
LACK
An explicit frequency-of-occurrence optimization layer
They brute-force the combinatorial space of the context window
Leading to massive computational wastage on low-probability sequences
True optimization requires shifting
from process everything
to attend only to high-frequency/high-signal patterns
Acapability next-generation sparse and agentic architectures are beginning to realize
Textual Frequency Law LLM Optimization
sparse attention mechanisms efficiency long context