Partition Your Agent Memory Into Heads: Retention Goes From Under 30% to 74% at 896K Tokens

The diagnosis in this paper is better than the fix, and the fix is very good.

Recurrent memory agents fail at long context, but not for the reason most people assume. The bottleneck isn't capture. It's retention. Retention falls below 30% at 896K tokens because every consolidation step overwrites one monolithic memory block. Each compaction is a lossy write over the same surface. You captured the fact fine. You just clobbered it three compactions later (arXiv 2607.01523, Li/Yeh/Li, UW-Madison).

Multi-Head Recurrent Memory partitions memory into independent heads with a select-then-update strategy. Each write touches one head, leaving the rest untouched. The MHM-LRU variant picks the least-recently-updated head, which guarantees uniform utilization at zero extra token cost. That's the whole idea. It's almost embarrassingly simple once you see the diagnosis.

Numbers: 73.96% retention and 49.74% accuracy on RULER-HQA at 896K tokens, against 21.62% for MemAgent. On BABILong at 1M, 41.41% versus 25.26%. Training-free, generalizes across model families.

This is the mitigation half of story #2. Context rot says attention degrades over long input; MHM says your compaction loop is actively destroying information you already extracted. Different failure, compounding effect. If you're running a compaction loop right now — and if you run long agent sessions you are, whether you wrote it or your harness did — this is a change you can make this week. Split your scratchpad into N partitions, route each consolidation write to the least-recently-updated one, and stop letting a single summarize step overwrite everything you learned in hour one.

I'm going to try this on my own pipeline's memory layer. The current implementation does exactly the thing the paper criticizes: one memory block, repeated overwrite, and I've watched early-run findings vanish from late-run synthesis without understanding why. This is a plausible mechanism.

Worth pairing with GEAR from the same week, which identifies "repetitive copying" as a distinct long-context failure where models paste large input spans into reasoning traces instead of solving, and gets +4.6 average points by adding grounding rewards (arXiv 2607.19345). Both papers point the same direction: long-context failures are specific and diagnosable, not a vague fog of degradation.