mlsanityData to 14 Oct 2026

Countercurrent Multiplier Networks: A Renal-Inspired Iterative Operator with Provably Bounded Fixed-Point Dynamics

arXiv 2607.18829 ↗First posted 21 Jul 2026cs.LG math-ph math.MPcited by 0

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Abstract (arXiv)

The mammalian kidney concentrates urine using a mechanism with no analogue in current neural architectures: the countercurrent multiplier. Two anti-parallel flows joined at a hairpin recirculate a weak magnitude-bounded local pump into a large axial gradient achieving a four-fold concentration increase from a single-effect gradient that never exceeds 200 mOsm at any point. We formalize this mechanism as a differentiable sequence operator the Countercurrent Multiplier (CCM) layer and study it as an alternative to residual iterative refinement.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 21 Jul 2026.

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References · 9

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  2. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  3. 2019Deep Equilibrium Modelscited by 615
  4. 2016Adaptive Computation Time for Recurrent Neural Networkscited by 506
  5. 2016Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagationcited by 259
  6. 2021PonderNet: Learning to Pondercited by 168
  7. 2019AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networkscited by 112
  8. 2020Monotone operator equilibrium networkscited by 100
  9. 2024Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learningcited by 5

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