Paper Citation Record · LEDGER

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric

As of 20 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2509.03594.

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

pith.paper-citation-record.v1
2509.03594 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:56:00.593707Z

measured 33 of 33 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 2026-05-10T19:43:18.349015Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T22:35:49.573675Z

Reference resolution

32 of 32 outbound references displayed

verified exact0
verified fuzzy20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f061259c-ee3b-47de-9fff-f6f35cc6f1f2 · outbound

This paper cites Visualizing the loss landscape of neural nets,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Visualizing the loss landscape of neural nets,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:04.440314Z

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 1777220b-2060-4ef4-b773-6d61d139956c · outbound

This paper cites On the difficulty of training recurrent neural networks,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric On the difficulty of training recurrent neural networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:04.256017Z

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Observation 52a71689-9fdc-4a56-90af-6e901bde2fc7 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 3

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Observation 068c7a07-49dd-4e02-ad8a-14ee753feb3e · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T10:55:58.476462Z

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Observation 817b0f69-58ad-431a-8fc3-39c738cd15d8 · outbound

This paper cites Decoupled Weight Decay Regularization.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Decoupled Weight Decay Regularization

Reference 5

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Observation e235e47e-98b0-4eb2-b56a-7da0eb299cb5 · outbound

This paper cites Muon: An optimizer for hidden layers in neural networks,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Muon: An optimizer for hidden layers in neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:04.105022Z

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 97c8d094-e8ae-4dc9-9bef-5743b34b6c15 · outbound

This paper cites Muon is Scalable for LLM Training.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Muon is Scalable for LLM Training

Reference 7

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Observation aa6921de-99a6-4d0f-8156-9fff6e17b0e0 · outbound

This paper cites Gradient-based learning applied to document recognition,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Gradient-based learning applied to document recognition,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:03.919227Z

Source-reported events for the cited work

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Observation e1d9f955-e462-4d10-8238-388b08c0aa4d · outbound

This paper cites Deep residual learning for image recognition,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Deep residual learning for image recognition,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:03.719745Z

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 3d88dc33-5a7f-45b4-9c56-dc21137bee6e · outbound

This paper cites Learning multiple layers of features from tiny images.(2009),.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Learning multiple layers of features from tiny images.(2009),

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T10:55:58.861762Z

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Observation d2cfdfa2-5326-4cc9-82f1-43e76a9bbd79 · outbound

This paper cites Attention Is All You Need.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Attention Is All You Need

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T10:55:58.932457Z

Source-reported events for the cited work

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Observation 7ee3ccc1-0ab4-4026-b706-85256ba91849 · outbound

This paper cites Optimization by simulated annealing,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Optimization by simulated annealing,

Reference 12

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Observation 28b39020-45a7-4cba-b807-5b5959616f9d · outbound

This paper cites Stochastic gradient hamiltonian monte carlo,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Stochastic gradient hamiltonian monte carlo,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:03.590626Z

Source-reported events for the cited work

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Observation f4d23ffa-d4f6-41be-935e-581b10c2a09e · outbound

This paper cites Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization

Reference 14

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Observation b5083e43-3444-4eb0-8697-338f763d5c99 · outbound

This paper cites Bayesian learning via stochastic gradient langevin dynamics,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Bayesian learning via stochastic gradient langevin dynamics,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:03.362275Z

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Observation 15434504-4dbf-4e6f-9878-508aee379c22 · outbound

This paper cites Absil, R.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Absil, R

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:03.109355Z

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Observation be0c59bb-dac4-4611-bd5b-1ed79199392f · outbound

This paper cites A survey of geometric optimization for deep learning: from euclidean space to riemannian manifold,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric A survey of geometric optimization for deep learning: from euclidean space to riemannian manifold,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:02.935210Z

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Observation c7574e02-5237-488f-bcce-b7480a14f108 · outbound

This paper cites The unreasonable effectiveness of recurrent neural networks.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric The unreasonable effectiveness of recurrent neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:02.734837Z

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 721d0aac-b2f6-4baa-9ed1-3c7670134ad0 · outbound

This paper cites Induced metric repository.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Induced metric repository

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:02.581699Z

Source-reported events for the cited work

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Observation eadbb9f6-a804-4743-8fcd-8a55f2f4bd63 · outbound

This paper cites JAX: composable transformations of Python+NumPy programs,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric JAX: composable transformations of Python+NumPy programs,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:02.412839Z

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 04359872-bead-41a2-a75f-8f3ed9ff5620 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Pytorch: An imperative style, high-performance deep learning library,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:02.222963Z

Source-reported events for the cited work

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Observation a7e22d77-8b98-4b1c-9f13-5e488890f826 · outbound

This paper cites Natural gradient works efficiently in learning,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Natural gradient works efficiently in learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:02.027742Z

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 4a7cb814-b52a-43d5-b09e-7d447b75148d · outbound

This paper cites Natural Gradient Methods: Perspectives, Efficient-Scalable Approximations, and Analysis.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Natural Gradient Methods: Perspectives, Efficient-Scalable Approximations, and Analysis

Reference 23

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Observation c053faa7-3ac6-4cf3-8273-49bb553124ab · outbound

This paper cites Neural networks for machine learning.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Neural networks for machine learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:01.765014Z

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 a6e6c778-4550-4027-97d4-513d5ce435cc · outbound

This paper cites Adjustment of an inverse matrix corresponding to a change in one element of a given matrix,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Adjustment of an inverse matrix corresponding to a change in one element of a given matrix,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:01.609982Z

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 cdc46e4a-70d2-4ce8-967a-b71fecb59fd6 · outbound

This paper cites Scaling Laws for Neural Language Models.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Scaling Laws for Neural Language Models

Reference 26

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Source-reported events for the cited work

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Observation 7fe162c2-7476-4f3e-9672-1feb2ddda2e2 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Deep Learning Scaling is Predictable, Empirically

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T10:56:00.077527Z

Source-reported events for the cited work

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Observation f7557292-6285-4434-aa3e-936fbbd5cf75 · outbound

This paper cites Some methods of speeding up the convergence of iteration methods,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Some methods of speeding up the convergence of iteration methods,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:01.387271Z

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 3efe0be9-91d3-41c9-8ed3-9e6a32652b50 · outbound

This paper cites A literature survey of benchmark functions for global optimisation problems,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric A literature survey of benchmark functions for global optimisation problems,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:01.226726Z

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 f61b9bad-10a1-488a-aaff-4d943e0fd694 · outbound

This paper cites An automatic method for finding the great- est or least value of a function,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric An automatic method for finding the great- est or least value of a function,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:01.056733Z

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

Observation 9b065e5b-89b6-4dd2-bcda-b0e1d92ea893 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Gaussian Error Linear Units (GELUs)

Reference 31

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Observation 602e2ac2-460a-4524-946f-d1d9cd0be655 · outbound

This paper cites Taking the human out of the loop: A review of bayesian optimization,.

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric Taking the human out of the loop: A review of bayesian optimization,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:56:00.915060Z

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

Pith citing papers

Observation 6908a2c0-63f3-4e95-9a0b-5df2d266a48b · inbound

Loss-aware state space geometry for quantum variational algorithms cites this paper.

Loss-aware state space geometry for quantum variational algorithms The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:35:49.576720Z

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.