CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention

• URL: http://arxiv.org/abs/2606.27229v2
• Date: Sun, 28 Jun 2026 10:17:59 GMT
• Title: CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention
• Abstract summary: Recurrent models must forget in order to remember, yet the state of the art decides what to erase without consulting what is stored.<n>This memory-blind gating is one of three coupled defects in the leading delta-rule architecture (GDN-2)<n>We introduce CARVE, which resolves all three problems through one principle: erase only on the key axis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recurrent models must forget in order to remember, yet the state of the art decides what to erase without consulting what is stored -- the gate sees only the arriving token, not the memory it is about to modify. This memory-blind gating is one of three coupled defects in the leading delta-rule architecture (GDN-2): the value-axis erase mask wastes parameters at the scale of the value projection, and -- as we prove -- mathematically prevents the WY-form triangular chunk solver that makes recurrent training competitive with Transformers. We introduce CARVE (Content-Aware Recurrent with Value Efficiency), which resolves all three problems through one principle: erase only on the key axis. This is provably necessary and sufficient for the WY-form solver to remain valid. Within it, CARVE reuses the recurrent output tensor -- already written to GPU memory -- as a free content signal for the erase gate, and replaces the per-value write-gate projection with a single scalar per head. At initialisation CARVE is bit-identical to GDN-2; any quality difference emerges from what the content gate learns. At 1.3B parameters trained on 100B tokens, CARVE achieves WikiText perplexity 15.72 (minus 0.18 vs. GDN-2, a 4.5-sigma effect), leads every recurrent baseline on nine common-sense reasoning benchmarks, and sets state of the art on every RULER retrieval probe -- at 0.4% throughput overhead, 13% lower peak memory, and 19% fewer parameters. Six formal theorems cover memory capacity, Lyapunov stability, gradient flow, expressivity separation, Pareto-optimal chunk size, and hybrid optimality.

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