mlsanityData to 7 Oct 2026

Differentiable Fuzzy Inference Layer: A Monotone, Compositional Ordinal Reasoning Head for Large Language Models

arXiv 2609.26113 ↗First posted 17 Aug 2026cs.CLcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

A state-of-the-art language model asked to interpret "most of most students passed" typically answers "most," though composing two instances of "most" yields a proportion closer to "some." We trace this failure to an architectural choice rather than a data deficit: standard classifier heads treat ordinal categories as independent labels, with no mechanism to respect their natural ordering or compose them algebraically. We introduce the Differentiable Fuzzy Inference Layer (DFIL), a dual-path prediction head pairing a standard classifier with a scalar-bottlenecked branch grounded in a bank of ordered membership functions. DFIL supplies two structural primitives that a label-only head cannot inherit: monotonicity in the underlying quantity, and compositional reasoning via t-norm operations without any compositional training data. The scalar branch additionally provides an interpretable interface for analyzing residual errors. We instantiate DFIL on ordinal natural-language tasks across diverse LLM families.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 27 of its 42 references that are in the map. First posted on arXiv 17 Aug 2026.

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27 of 42 references.

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  1. 2023Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models8 Nov

Other references · 26

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  8. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
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  10. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
  11. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,085
  12. 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 533
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  2. 2020Logic Tensor Networkscited by 197
  3. 2019Rank consistent ordinal regression for neural networks with application to age estimationcited by 132
  4. 2019Unconstrained Monotonic Neural Networkscited by 91
  5. 2017Deep Lattice Networks and Partial Monotonic Functionscited by 62
  6. 2021Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilitiescited by 57
  7. 2020GenericsKB: A Knowledge Base of Generic Statementscited by 51
  8. 2022Constrained Monotonic Neural Networkscited by 31
  9. 2024Not All LLM Reasoners Are Created Equalcited by 29
  10. 2017Unimodal probability distributions for deep ordinal classificationcited by 28
  11. 2024NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalizationcited by 13
  12. 2025Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activationscited by 7
  13. 2018Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifierscited by 2
  14. 2022LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learningcited by 2

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