Context

TL;DR Today’s long-context and RAG systems scale storage, not thinking. Cognitive Workspace (CW) reframes memory as an active, metacognitive process: curate, plan, reuse, and consolidate. In tests, CW reports ~55–60% memory reuse and 17–18% net efficiency gains despite a 3.3× operation overhead—precisely because it thinks about what to remember and why. The Setup: Context ≠ Cognition Over the past 18 months we’ve cheered >1M-token windows and slicker attention kernels. But piling tokens into a context is like dumping files on a desk; it’s storage without stewardship. In knowledge work, what moves the needle is not how much you can “see” but how well you organize, recall, and reuse—with intent. ...

Large language models can forecast surprisingly well when you hand them the right context. But naïve prompts leave money on the table. Today’s paper introduces four plug‑and‑play strategies—ReDP, CorDP, IC‑DP, RouteDP—that lift accuracy, interpretability, and cost‑efficiency without training new models. Here’s what that means for teams running demand, risk, or ops forecasts. Why this matters for business readers Most production forecasts are numeric workhorses (ARIMA/ETS/TS foundation models), while contextual facts—weather advisories, policy changes, promos, strikes—arrive as text. LLMs can read that text and adjust the forecast, but simply stuffing history+context into a prompt (“direct prompting”) is often fragile. The four strategies below are operational patterns you can drop into existing stacks without re‑architecting. ...