Penelope: Localized Latent Recurrence for Efficient Structured Reasoning

• URL: http://arxiv.org/abs/2607.25915v1
• Date: Tue, 28 Jul 2026 16:06:46 GMT
• Title: Penelope: Localized Latent Recurrence for Efficient Structured Reasoning
• Abstract summary: Penelope is an efficient latent-reasoning framework for pretrained decoder-only Transformers.<n>It localizes recurrent computation to a selected decoder interval.<n>It attains competitive accuracy relative to established latent-reasoning models.
• Score: 26.549641831847982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complex structured reasoning tasks often require additional computation, yet current language models obtain it mainly by increasing parameter scale or by serializing intermediate steps as chain-of-thought (CoT) tokens. The former raises training and deployment costs, while the latter ties reasoning computation to autoregressive output length. We introduce Penelope, an efficient latent-reasoning framework for pretrained decoder-only Transformers that localizes recurrent computation to a selected decoder interval. The lower decoder prefix is evaluated once to construct a problem-conditioned boundary memory, which is then iteratively refined through time-modulated GRU dynamics and recurrent readout states before answer generation. A progressive CoT-to-latent curriculum transfers visible reasoning into this internal recurrent path, allowing additional computation to be allocated in latent space without repeatedly executing the complete decoder or generating a long intermediate trace. Experiments on open-source structured-reasoning benchmarks show that, at validation-selected latent budgets, Penelope attains competitive accuracy relative to established latent-reasoning models while reducing measured inference latency. These results show that latent refinement can be localized to a narrow decoder interval, reducing repeated full-decoder execution without generating a long visible reasoning trace and providing a practical accuracy-efficiency tradeoff for decoder-only Transformer models.

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