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Paper Citation Record · LEDGER

Gradients are Not All You Need

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2111.05803.

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

pith.paper-citation-record.v1
2111.05803 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:38:42.138517Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:10:08.808642Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 246f5cfb-9229-4915-bf34-d3b1493f7387 · inbound

Fitting Coarse-Grained Models to Macroscopic Experimental Data via Automatic Differentiation cites this paper.

Fitting Coarse-Grained Models to Macroscopic Experimental Data via Automatic Differentiation Gradients are Not All You Need

Reference 31

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no resolver link, observed 2026-08-12T20:59:57.528560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:59:57.528560Z digest=sha256:b52dbb61b8107761210220e37fe596650e4e8174c548f5d59729130f74ec3714

Observation cbd0547a-70e9-47d5-906c-7bfe07d721c8 · inbound

Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation cites this paper.

Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation Gradients are Not All You Need

Reference 31

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no resolver link, observed 2026-08-11T15:20:56.386945Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:20:56.386945Z digest=sha256:56af3c9c01bd1ef19780991e63ff32f9817b6cb9428a2797b8deba201c7583c4

Observation 91466f35-c3df-42c4-8297-71bae3257c60 · inbound

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials cites this paper.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Gradients are Not All You Need

Reference 86

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no resolver link, observed 2026-08-09T04:14:42.103084Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T04:14:42.103084Z digest=sha256:ae64bdcd19d36ff798a80dd1a990ae2c4e45baf25da3990013723a7a8ab7d56f

Observation 775e635b-e5c4-4758-81e3-82730cddb083 · inbound

Joint parameter and state estimation for regularized time-discrete multibody dynamics cites this paper.

Joint parameter and state estimation for regularized time-discrete multibody dynamics Gradients are Not All You Need

Reference 15

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no resolver link, observed 2026-08-08T15:01:51.853712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:01:51.853712Z digest=sha256:2634d6ef00731f684ac7c5aa534c92ef562181a497e24618488a5858ac795977

Observation 06509317-10c5-4efd-bc45-c1859502b2c1 · inbound

Accelerated Learning with Linear Temporal Logic using Differentiable Simulation cites this paper.

Accelerated Learning with Linear Temporal Logic using Differentiable Simulation Gradients are Not All You Need

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-05-19T10:52:15.112840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-19T10:50:18.047151Z digest=sha256:a6696ed01a511be5cfe91a2b1fd979a49f4118a4c2fb6654af2156fc6492182a

Observation 0610a14e-4f3a-47f2-9159-606f1641d390 · inbound

How Should We Meta-Learn Reinforcement Learning Algorithms? cites this paper.

How Should We Meta-Learn Reinforcement Learning Algorithms? Gradients are Not All You Need

Reference 53

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no resolver link, observed 2026-08-06T14:48:49.755270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:48:49.755270Z digest=sha256:0209d3ec7c834f9701a674d62e2cc1fd2ff8bd0919f2ac12e1b90b0d3d5ad695

Observation 10e7b0e1-f90f-467e-9124-2eb1428c5f1b · inbound

First Order Model-Based RL through Decoupled Backpropagation cites this paper.

First Order Model-Based RL through Decoupled Backpropagation Gradients are Not All You Need

Reference 56

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no resolver link, observed 2026-08-05T13:55:22.186464Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:55:22.186464Z digest=sha256:23af1b8ad7436ae01f814cd53175866c21f9c293337e193941a2a8d4be61f61c

Observation a42b6821-0fb2-41e6-b08f-051f0cf75fe3 · inbound

RoboSSM: Scalable In-context Imitation Learning via State-Space Models cites this paper.

RoboSSM: Scalable In-context Imitation Learning via State-Space Models Gradients are Not All You Need

Reference 37

Resolution
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no resolver link, observed 2026-08-04T15:34:52.314730Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T15:34:52.314730Z digest=sha256:03fba95281184cfe8d75e38c1024b25ed36bca546f83e44fe4579e096daad93d

Observation cb26206a-7343-4794-9673-8b4fe9c2592a · inbound

Fatigue-Aware Learning to Defer via Constrained Optimisation cites this paper.

Fatigue-Aware Learning to Defer via Constrained Optimisation Gradients are Not All You Need

Reference 43

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verified exact
arxiv_id, observed 2026-05-13T22:43:23.334588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-13T22:40:32.912158Z digest=sha256:f88eaf3d82927ea91e800f4f2a26b360bb7d6ef0f69a568b6424b9b4aee489c3

Observation 0969666b-8e22-4f20-981f-33ccf0404d3b · inbound

Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation cites this paper.

Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation Gradients are Not All You Need

Reference 15

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verified exact
arxiv_id, observed 2026-05-13T20:23:13.591829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-13T20:20:52.902947Z digest=sha256:4abcd9394f2c9a39dfa34a1093d55e6662e49ee6b95062885251619b5791139c

Observation cfa41a04-38d8-4bf7-b929-599450385edc · inbound

Differentiable hybrid force fields support scalable autonomous electrolyte discovery cites this paper.

Differentiable hybrid force fields support scalable autonomous electrolyte discovery Gradients are Not All You Need

Reference 52

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verified exact
arxiv_id, observed 2026-05-11T06:51:37.548051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T17:24:50.428517Z digest=sha256:9572b9d41e05b38402aa6f8b209d7ff746f1c6f81f8ffd27a1758bef0c70ab8a

Observation 02ed5da2-4a38-4102-bd05-b98b72b2df98 · inbound

Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient cites this paper.

Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient Gradients are Not All You Need

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-06-29T18:03:48.639435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-29T17:34:41.053725Z digest=sha256:566abd7dfd0ca5cb25d10ae874354176a9a477b304704369b1dbb8698aef312f

Observation eb58e319-ede6-437d-b221-13bffd75fd04 · inbound

MAOAM: Unified Object and Material Selection with Vision-Language Models cites this paper.

MAOAM: Unified Object and Material Selection with Vision-Language Models Gradients are Not All You Need

Reference 92

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metadata mismatch
arxiv_id, observed 2026-07-02T02:16:26.380861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-28T11:08:59.900161Z digest=sha256:65044c2a1811b9a03d24be62a1b5aab328d9a85f288e3be27ff7e9093ede154a

Observation 1bd1a990-9e08-4677-a9f1-5dee21b6ebe8 · inbound

Efficient Domain-Adaptive Policy Learning via Kernel Representation with Application to Quadrotor Control under Non-Stationary Disturbances cites this paper.

Efficient Domain-Adaptive Policy Learning via Kernel Representation with Application to Quadrotor Control under Non-Stationary Disturbances Gradients are Not All You Need

Reference 26

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no resolver link, observed 2026-08-02T11:42:11.689859Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:42:11.689859Z digest=sha256:e4adc51ee7d8f5101c5d907be92e384e1b38df021ac7554ec8c593b1eb295021

Observation db80dc10-b572-4ac9-ad3a-44f0660dcbeb · inbound

SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity cites this paper.

SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity Gradients are Not All You Need

Reference 22

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verified exact
arxiv_id, observed 2026-07-04T07:59:39.917784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T12:26:28.391451Z digest=sha256:5f02018f7d2e29ff6c2379d10014872ef1eb94e007bfb1e9bb0080f889fd506b

Observation d7f259d4-5f7e-4acf-bdf7-ef0a34f0e386 · inbound

Bridging Spherical Black-Box Optimizers cites this paper.

Bridging Spherical Black-Box Optimizers Gradients are Not All You Need

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:10:08.810055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-25T20:32:46.418304Z digest=sha256:d8a88dfcd032b86aafdb05357f709e4eab116a2de1625973e2e40dcc4c0c0121

Observation 23682b6d-7faf-43ff-b6ba-bee27bc2ec15 · inbound

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients cites this paper.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Gradients are Not All You Need

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-07-02T18:37:16.333395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-07-02T18:27:35.055003Z digest=sha256:70e2126f3277fb89e583869053956dfe96ae1186fe6a3f2c166d83704c01e36d

Observation 923ed04d-d8ab-4005-a2fb-bff299cac88b · inbound

Open-DiffLoco: Open-Source Differentiable Learning for Deployable Blind Quadruped Locomotion cites this paper.

Open-DiffLoco: Open-Source Differentiable Learning for Deployable Blind Quadruped Locomotion Gradients are Not All You Need

Reference 10

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unresolved
no resolver link, observed 2026-08-04T15:52:55.728405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T15:52:55.728405Z digest=sha256:2df2668e61baf6c509106833b56c97586b5b04c1aac5b62b9d5f5e4a13824dad

Observation 26f40575-c065-4671-912d-4a92efe58af0 · inbound

Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation cites this paper.

Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation Gradients are Not All You Need

Reference 2004

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unresolved
no resolver link, observed 2026-08-14T04:38:42.138517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:38:42.138517Z digest=sha256:d36d3b0994135da2821c5195c31edbfe3a0b60628164c35e7c04b3cbb075524d