Paper Citation Record · LEDGER

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions

As of 20 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2505.19531.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.19531 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:21:02.060803Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T20:57:10.558525Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact4
verified fuzzy2
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dfeadc09-5c90-4514-a781-34d65330052a · outbound

This paper cites Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.129826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3613ede7-6236-4cd7-b96b-3bef5ecbc367 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.303448Z

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Observation 048926cc-308b-46de-867a-170a7ef15673 · outbound

This paper cites Circuit Complexity Bounds for RoPE-based Transformer Architecture.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Circuit Complexity Bounds for RoPE-based Transformer Architecture

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:21:03.091343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b244969a-2e56-4356-b7ec-223e8ae722a6 · outbound

This paper cites Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:21:02.912542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d2393cb8-fdb7-4118-bac0-38c4439db750 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.641603Z

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Observation 98c36a3c-2de1-4115-adcb-e61fcb878086 · outbound

This paper cites T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.809131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 063b1293-4e99-4560-8953-4c1d2dd07be4 · outbound

This paper cites Subquadratic Algorithms and Hardness for Attention with Any Temperature.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Subquadratic Algorithms and Hardness for Attention with Any Temperature

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:21:02.673064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 56df5c5a-39b4-4178-b882-02195e1018e8 · outbound

This paper cites In-Context Convergence of Transformers.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions In-Context Convergence of Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.978836Z

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Observation f226550d-9ed6-48ea-858b-4d98beffd732 · outbound

This paper cites Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.293029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4caa9f6-825f-4547-9a72-b008be475c2d · outbound

This paper cites Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:21:02.435527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 15190e46-7116-43bb-bada-dbb6eb711a62 · outbound

This paper cites On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.419305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1027cf28-3136-40e0-9c9b-44a8bdd5b271 · outbound

This paper cites One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.467347Z

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Unavailable: canonical work link unavailable.

Observation 9cadd4b5-c628-4d76-9b40-56a422be704e · outbound

This paper cites Evalu- ating numeracy of language models as a natural language inference task.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Evalu- ating numeracy of language models as a natural language inference task

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:03.534648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a5275ba0-4a72-438a-b059-5c7f92ff82ec · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.699323Z

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Observation 595b73b5-7fae-49f6-895d-3b764859bc8a · outbound

This paper cites Transformers Learn Low Sensitivity Functions: Investigations and Implications.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Transformers Learn Low Sensitivity Functions: Investigations and Implications

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.743928Z

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Observation e3256b49-7b24-4451-9d9e-d7816e3bed41 · outbound

This paper cites Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.786208Z

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Unavailable: canonical work link unavailable.

Observation 939fe7ff-cdb3-4f8f-88fc-e807cdd348f8 · outbound

This paper cites From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.863974Z

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Unavailable: canonical work link unavailable.

Observation 700e4e8a-caf4-45ef-bfb2-347f4c249a7d · outbound

This paper cites DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.955998Z

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Observation 64b3c3ce-f5d0-49a0-bfd9-f86abd0ff6ae · outbound

This paper cites Genomeocean: An efficient genome foundation model trained on large-scale metagenomic assemblies.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Genomeocean: An efficient genome foundation model trained on large-scale metagenomic assemblies

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:21:03.344204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd78cc70-0225-422b-85bf-579607168ac3 · outbound

This paper cites DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA Embeddings.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA Embeddings

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:02.060803Z

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Observation f0e4350d-26ba-4df3-b90e-ba5b1d39be14 · outbound

This paper cites Transformers in Uniform TC$^0$.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Transformers in Uniform TC$^0$

Reference 1867

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.402417Z

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Observation 206e2655-941d-4e3c-a85b-05d3558c2bba · outbound

This paper cites Why are Sensitive Functions Hard for Transformers?.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Why are Sensitive Functions Hard for Transformers?

Reference 1963

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.050664Z

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Unavailable: canonical work link unavailable.

Observation 23287124-bca5-4cf0-8d95-87a8944e8a85 · outbound

This paper cites Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models

Reference 1993

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.742146Z

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Unavailable: canonical work link unavailable.

Observation 826301cb-e573-4121-bba2-e1db4ad29938 · outbound

This paper cites Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.122910Z

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Unavailable: canonical work link unavailable.

Observation ba15c0a1-cce9-4037-83c4-9e7db19ab1dc · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions LLaMA: Open and Efficient Foundation Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.566777Z

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Observation 2db79da7-9150-4c5c-b483-19a604299ca4 · outbound

This paper cites On the Optimal Memorization Capacity of Transformers.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions On the Optimal Memorization Capacity of Transformers

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.197868Z

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Observation 83bfdf52-d4cc-4c50-bed9-d0b40e8dba71 · outbound

This paper cites How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.090765Z

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Observation 51d5d997-6339-4246-adba-6ef2c4f6b0e1 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:00.215985Z

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Pith citing papers

Observation 11a05383-c11a-49d3-8e5a-321ce04f849a · inbound

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently cites this paper.

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently Minimalist Softmax Attention Provably Learns Constrained Boolean Functions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T20:57:10.558525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.