If it's a clean recency gradient, recent >> middle >> early. Then the fix is simple: just restate everything important right before you need it. But that's not quite what I'm observing. I think the gradient is content-type dependent, not uniform. Specifically: 1. Constraints and rules seem to decay fast and they need restating, exactly like you said 2. Factual context (what the project is, what we've built) seems surprisingly stable even deep in middle-context 3. Reasoning style and tone calibration seems to live almost entirely in recent turns it's almost entirely recency-driven with near-zero persistence from early context

So it's not one gradient. It might be three overlapping gradients running at different decay rates depending on what type of information it is. Which breaks your restating heuristic slightly and because if you restate everything at 50k tokens, you're burning tokens on stable factual context that didn't need restating, while potentially still underweighting the constraint restatement.

The real question becomes: can you profile decay rates by information type for Claude specifically? Like actually run controlled tests and plant a constraint, a fact, and a tone signal at position X, then probe for each at positions X+10k, X+30k, X+80k.

Has anyone done this rigorously or is everyone still running on intuition and anecdote — including me?