Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory
  and Test-Time Compute Scaling
Reasoning is a core capability of large language models, yet understanding
how they learn and perform multi-step reasoning remains an open problem. In
this study, we explore how different architectures and training methods affect
model multi-step reasoning capabilities within a cellular automata framework.
By training on state sequences generated with random Boolean functions for
random initial conditions to exclude memorization, we demonstrate that most
neural architectures learn to abstract the underlying rules. While models
achieve high accuracy in next-state prediction, their performance declines
sharply if multi-step reasoning is required. We confirm that increasing model
depth plays a crucial role for sequential computations. We demonstrate that an
extension of the effective model depth with recurrence, memory, and test-time
compute scaling substantially enhances reasoning capabilities.
68ad769486b21a0e2e358f20
Ivan Rodkin
claimed_verified
2025-08-26T09:49:10.644Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63c1ac8cc58fcfeac186bda2/MajMhLgsvCBN81tmO9PZc.jpeg
Ivan Rodkin
user
irodkin
68ad769486b21a0e2e358f21
Daniil Orel
68ad769486b21a0e2e358f22
Konstantin Smirnov
68ad769486b21a0e2e358f23
Arman Bolatov
68ad769486b21a0e2e358f24
Bilal Elbouardi
claimed_verified
2025-08-26T11:37:55.584Z
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/avatars/8edc9e4f889832f4b25e296b2454ce8c.svg
BIlal Elbouardi
user
b1l4lx1
68ad769486b21a0e2e358f25
Besher Hassan
claimed_verified
2025-08-27T10:02:18.097Z
66e05123e6f5f72105af69e8
/avatars/47dff0524a6dcd26cd12e601909987f8.svg
besher hassan
user
besher2002
68ad769486b21a0e2e358f26
Yuri Kuratov
claimed_verified
2025-08-26T09:49:12.960Z
618b9540682ec1c38327e586
https://cdn-avatars.huggingface.co/v1/production/uploads/618b9540682ec1c38327e586/v_ZBkfh8O9Zh6C2YQpuBX.jpeg
Yury Kuratov
user
yurakuratov
68ad769486b21a0e2e358f27
Aydar Bulatov
68ad769486b21a0e2e358f28
Preslav Nakov
68ad769486b21a0e2e358f29
Timothy Baldwin
68ad769486b21a0e2e358f2a
Artem Shelmanov
68ad769486b21a0e2e358f2b
Mikhail Burtsev
claimed_verified
2025-08-26T09:49:16.139Z
639c6e978a34ed9a404c6a7b
/avatars/c98ca8c9f9ed8509c2f1bb6aa994fd57.svg
MIKHAIL BURTSEV
user
mbur
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory
  and Test-Time Compute Scaling
Reasoning is a core capability of large language models, yet understanding
how they learn and perform multi-step reasoning remains an open problem. In
this study, we explore how different architectures and training methods affect
model multi-step reasoning capabilities within a cellular automata framework.
By training on state sequences generated with random Boolean functions for
random initial conditions to exclude memorization, we demonstrate that most
neural architectures learn to abstract the underlying rules. While models
achieve high accuracy in next-state prediction, their performance declines
sharply if multi-step reasoning is required. We confirm that increasing model
depth plays a crucial role for sequential computations. We demonstrate that an
extension of the effective model depth with recurrence, memory, and test-time
compute scaling substantially enhances reasoning capabilities.
Models trained on random Boolean functions in a cellular automata framework show that increasing depth, recurrence, memory, and test-time compute scaling enhances multi-step reasoning capabilities.
cellular automata Boolean functions multi-step reasoning model depth recurrence memory test-time compute scaling