Beyond Tokens: Why LLMs Need Reusable Chunks of Reasoning
Tokens were a brilliant engineering choice.
They were never the final answer.
Research#
Causal Advantage: Why Reusable Reasoning Will Separate the Winners from the Experiments
Most conversations about AI still orbit the same three words: bigger, faster, more.
Bigger models. Faster inference. More compute.
Causal Tiling: Stop Paying for the Same Reasoning Twice
AI has a strange habit: it is expensive because it is forgetful.
We train giant models on staggering amounts of text, logs, time series, and behavioral traces. Then we ask them to solve the same classes of problems again and again: Why did demand move? What caused the outage? Which levers drive revenue? What happens if we change this constraint, this price, this power contract, this promotion?
Causify DataFlow: A Framework For High-performance Machine Learning Stream Computing
TL;DR DataFlow is a computational framework for simulating causal models on time series data using a directed acyclic graph architecture enhanced with knowledge time semantics for temporal causal reasoning.
Beyond Accuracy: A Stability-Aware Metric for Multi-Horizon Forecasting
TL;DR Traditional forecasting models optimize only for accuracy, ignoring an important issue: predictions that fluctuate significantly from day to day undermine confidence in production. This paper introduces the AC score metric that balances accuracy and temporal stability, achieving 91% reduction in forecast volatility while improving multi-step prediction accuracy by up to 26%.