mlsanityData to 7 Oct 2026

A Miniature Brain Transformer: Thalamic Gating, Hippocampal Lateralization, Amygdaloid Salience, and Prefrontal Working Memory in Attention-Coupled Latent Memory

arXiv 2603.07217 ↗First posted 7 Mar 2026q-bio.NC cs.AIcited by 2

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Abstract (arXiv)

We present a miniature brain transformer architecture that extends the attention-coupled latent memory framework with four additional brain-region analogues: a thalamic relay, an amygdaloid salience module, a prefrontal working-memory (PFC) buffer, and a cerebellar fast-path, all coupled by inhibitory callosal cross-talk between lateralized hippocampal banks. We evaluate on a two-domain benchmark -- MQAR (Multi-Query Associative Recall; episodic domain) and modular arithmetic (+1 mod 10; rule-based domain) -- using a seven-variant additive ablation. The central empirical finding is a surprise: inhibitory callosal coupling alone never lateralizes the banks (variants 1-5 maintain D_sep ~ 0.25 and P_ct ~ 0.25 for all 30 epochs). Functional lateralization requires the synergy of PFC and inhibition: only when the PFC buffer is added (variant 6) does a sharp, discontinuous phase transition fire -- at epoch 11 for the PFC-only variant and epoch 10 for the full model -- collapsing P_ct from 0.25 to ~0.002 and more than doubling D_sep from 0.251 to 0.501 in a single gradient step. The PFC buffer acts as a symmetry-breaker: its slowly drifting domain context creates the initial asymmetry that the inhibitory feedback loop then amplifies irreversibly. The cerebellar fast-path accelerates the transition by one epoch (epoch 10 vs. epoch 11) with no asymptotic change, confirming its convergence-acceleration role. The result constitutes a novel, falsifiable prediction -- no lateralization without working memory context -- and a principled, neurobiologically motivated blueprint for hierarchical persistent memory in sequence models.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 7 Mar 2026.

Builds on

11 of 29 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  4. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  5. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  6. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 800
  7. 2023Zoology: Measuring and Improving Recall in Efficient Language Modelscited by 188
  8. 2018Differentiable plasticity: training plastic neural networks with backpropagationcited by 83
  9. 2020Deep Reinforcement Learning and its Neuroscientific Implicationscited by 33
  10. 2018Been There, Done That: Meta-Learning with Episodic Recallcited by 25
  11. 2026Inhibitory Cross-Talk Enables Functional Lateralization in Attention-Coupled Latent Memorycited by 3

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Citing papers per year: 2026: 2.

2 papers.