Paper Citation Record · LEDGER

On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 39 inbound Pith citation observations for arXiv:1704.00805.

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

pith.paper-citation-record.v1
1704.00805 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 39 of 39 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:39:55.024520Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-29T00:02:50.326473Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation d3b8ff35-d6e1-4d08-9e2e-8cdb4a71fc40 · inbound

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Nets cites this paper.

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Nets On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-25T15:45:59.315985Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 37ac66c6-a61f-45bc-8b9d-3fe618b89c4b · inbound

Implicit Deep Learning cites this paper.

Implicit Deep Learning On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 21

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Observation e7867490-3ee2-4be7-bba1-08f01f5863cf · inbound

Toward a Unified Lyapunov-Certified ODE Convergence Analysis of Smooth Q-Learning with p-Norms cites this paper.

Toward a Unified Lyapunov-Certified ODE Convergence Analysis of Smooth Q-Learning with p-Norms On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-24T02:18:44.871063Z

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Observation 13375581-ad32-45d0-8947-aeb05264c98e · inbound

Iterative variational learning of committor-consistent transition pathways using artificial neural networks cites this paper.

Iterative variational learning of committor-consistent transition pathways using artificial neural networks On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 8

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Observation 844e1b39-a54c-4e9f-a16f-f8d71f905103 · inbound

Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models cites this paper.

Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T16:15:39.346407Z

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Observation 5ba6dde9-f57a-41a9-b7d4-3f57506e9e8d · inbound

Biologically Plausible Brain Graph Transformer cites this paper.

Biologically Plausible Brain Graph Transformer On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T23:09:48.772739Z

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Observation 3232a6ae-4d9f-43c1-aee1-9f9a9fff8c40 · inbound

How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias cites this paper.

How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T04:39:55.024520Z

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Observation ba688fc5-0591-4953-bd21-55bd8eec26b2 · inbound

Attractor-Based Coevolving Dot Product Random Graph Model cites this paper.

Attractor-Based Coevolving Dot Product Random Graph Model On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 10

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Observation 7e85df45-3efa-4b8e-963b-cad9fed7d59c · inbound

Fairness Perceptions in Regression-based Predictive Models cites this paper.

Fairness Perceptions in Regression-based Predictive Models On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 14

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Observation 1dc4b78b-d178-4f03-a981-1ebd89d73033 · inbound

Emergence of Structure in Ensembles of Random Neural Networks cites this paper.

Emergence of Structure in Ensembles of Random Neural Networks On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 27

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Observation 4d784979-6f53-485f-a65c-a8f2008c0cd0 · inbound

Deep greedy unfolding: Sorting out argsorting in greedy sparse recovery algorithms cites this paper.

Deep greedy unfolding: Sorting out argsorting in greedy sparse recovery algorithms On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:18:12.610956Z

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Observation e411b4ea-ac04-4d62-a68e-c03bce770ccf · inbound

Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate cites this paper.

Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-19T12:52:17.993384Z

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Observation f7e1358f-e342-4e02-a312-9720a69bb5b2 · inbound

Differentiable neural network representation of multi-well, locally-convex potentials cites this paper.

Differentiable neural network representation of multi-well, locally-convex potentials On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 20

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Observation ca8ca0de-7a68-4dc9-86a9-d4f7bff1521e · inbound

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers cites this paper.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T18:46:51.575245Z

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Observation 45f548ce-8e1f-4ee9-8e50-e8e145103771 · inbound

Scaling Attention to Very Long Sequences in Linear Time with Wavelet-Enhanced Random Spectral Attention (WERSA) cites this paper.

Scaling Attention to Very Long Sequences in Linear Time with Wavelet-Enhanced Random Spectral Attention (WERSA) On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T18:19:59.044342Z

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Observation 4c9102b8-9a48-4177-8411-dd139a7d2eca · inbound

Efficient Decentralized Learning of Generalized Quantal Response Equilibrium cites this paper.

Efficient Decentralized Learning of Generalized Quantal Response Equilibrium On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:51:37.182966Z

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Observation 1565c379-91d9-4a37-a09a-f2f89f49caa1 · inbound

Learning with Episodic Hypothesis Testing in General Games: A Framework for Equilibrium Selection cites this paper.

Learning with Episodic Hypothesis Testing in General Games: A Framework for Equilibrium Selection On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T11:11:30.423531Z

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Observation 1b4409d4-49d8-4fd8-b8ba-36b530e55e52 · inbound

A Minimal-Assumption Analysis of Q-Learning with Time-Varying Policies cites this paper.

A Minimal-Assumption Analysis of Q-Learning with Time-Varying Policies On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:50:57.158526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f4509a2b-db27-493f-9081-6cb2077a5473 · inbound

Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation cites this paper.

Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 17

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Observation f9859680-dbbb-4ad3-8f15-8a74112ae50f · inbound

Neural Policy Composition from Free Energy Minimization cites this paper.

Neural Policy Composition from Free Energy Minimization On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-21T17:20:25.415462Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d9b2d8e1-4f8a-4c1e-9ef1-f4affe26969a · inbound

Structure Preserving Approximation of Semiconcave Functions cites this paper.

Structure Preserving Approximation of Semiconcave Functions On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T03:38:16.571932Z

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Observation 85ead63b-f6d1-4e19-ad76-e4f633411b7b · inbound

Path-conditioned training: a principled way to rescale ReLU neural networks cites this paper.

Path-conditioned training: a principled way to rescale ReLU neural networks On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T21:38:23.768842Z

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Observation 2c5d411a-c006-4757-9b97-46ca58208e76 · inbound

Learning Cut Distributions with Quantum Optimization cites this paper.

Learning Cut Distributions with Quantum Optimization On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:50:25.011257Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 62148a31-27e2-417d-a11d-f8e655a9770c · inbound

Rethinking Intrinsic Dimension Estimation in Neural Representations cites this paper.

Rethinking Intrinsic Dimension Estimation in Neural Representations On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 48

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Observation da9b6f4b-e7de-41bb-b522-7f99e62f5580 · inbound

On Bayesian Softmax-Gated Mixture-of-Experts Models cites this paper.

On Bayesian Softmax-Gated Mixture-of-Experts Models On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 112

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Observation 9ad46d55-3706-4298-8c0a-3ab138380f03 · inbound

Structure-Centric Graph Foundation Model via Geometric Bases cites this paper.

Structure-Centric Graph Foundation Model via Geometric Bases On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 43

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Observation 4e647bb0-0fff-4ca0-8510-b812c2226413 · inbound

Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks cites this paper.

Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:46:19.344104Z

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Observation 5fecdb2d-2c34-42ac-bd6f-0bb976b2f2e3 · inbound

Sharp Spectral Thresholds for Logit Fixed Points cites this paper.

Sharp Spectral Thresholds for Logit Fixed Points On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-20T21:03:46.720664Z

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Observation 4d39b31f-1123-4e3c-bb6b-cd956744e3ad · inbound

Informative Graph Structure Learning cites this paper.

Informative Graph Structure Learning On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-05-19T21:12:47.211160Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation efd63183-cc72-46b6-b88a-92916de72f05 · inbound

Learning Empirical Evidence Equilibria under Weak Environmental Coupling cites this paper.

Learning Empirical Evidence Equilibria under Weak Environmental Coupling On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-20T00:52:54.751303Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0e8a3d1d-e991-4ea2-bf92-d3368e201201 · inbound

Learning Empirical Evidence Equilibria under Weak Environmental Coupling cites this paper.

Learning Empirical Evidence Equilibria under Weak Environmental Coupling On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-21T08:44:04.671756Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53379deb-c475-48a0-90ae-ee46bbadca3c · inbound

Functional Attention: From Pairwise Affinities to Functional Correspondences cites this paper.

Functional Attention: From Pairwise Affinities to Functional Correspondences On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-06-29T00:02:50.327751Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2e938b54-b37f-4832-917e-dada79c4165f · inbound

SiamJEPA: On the Role of Siamese Student Encoders in JEPA cites this paper.

SiamJEPA: On the Role of Siamese Student Encoders in JEPA On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 80

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Observation 0c86c78b-02de-47d2-b4d3-8df7691b36a0 · inbound

SiamJEPA: On the Role of Siamese Student Encoders in JEPA cites this paper.

SiamJEPA: On the Role of Siamese Student Encoders in JEPA On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 80

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Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex cites this paper.

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 191

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SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation cites this paper.

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 39

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Observation 6330d395-4451-4d42-9695-266afb97123c · inbound

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation cites this paper.

Reference 39

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Observation c7a97561-1927-4eae-994c-3b2b5b9b3412 · inbound

Spend Bits Where Queries Look: KV Cache Vector Quantization with Attention-Preserving Transforms cites this paper.

Spend Bits Where Queries Look: KV Cache Vector Quantization with Attention-Preserving Transforms On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 1949

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Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference cites this paper.

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 9

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Unavailable: canonical work link unavailable.