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Causify Blog#

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?

The astonishing part is not that our models can answer these questions. The astonishing part is that they often answer them by recomputing similar reasoning from scratch every time.

That is wasteful. It is wasteful in tokens, wasteful in compute, and wasteful in latency. More importantly, it is wasteful in the deepest sense: we keep paying for intelligence as though intelligence has no memory.

Why trust is becoming critical for enterprise AI systems

Most AI platforms focus on improving model performance, and better models do lead to better outputs. But for enterprise adoption, performance alone is not enough.

Before adopting any AI system, organizations ask a fundamental question: Can this system be trusted with our data and decisions? The answer often determines whether evaluation moves forward at all.

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%.