BeNice! trains looped models to share one KV-cache across recurrences
In simulated low-memory long-context inference, cache hits rose up to 31.4 percent and throughput up to 23.6 percent.
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In simulated low-memory long-context inference, cache hits rose up to 31.4 percent and throughput up to 23.6 percent.
On swap-S5, a Qwen2.5-0.5B model trained on at most 32 moves reaches 95.5% full-state accuracy at 512 moves.
A Huginn-style MoE uses 75% fewer resident parameters than a 112-layer feedforward baseline under matched FLOPs.
Learned depth priors and orthogonal injection improve looped language models while enabling cheaper KV sharing.
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