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Paper Citation Record · LEDGER

Verification of Visual Controllers via Compositional Geometric Transformations

As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2507.04523.

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

pith.paper-citation-record.v1
2507.04523 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:50:43.347369Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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  • verified fuzzy34
  • unresolved8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 51befdcb-bbf6-431d-a31a-eef17a8e306c · outbound

This paper cites Efficient neural network robustness certification with general activa- tion functions,.

Verification of Visual Controllers via Compositional Geometric Transformations Efficient neural network robustness certification with general activa- tion functions,

Reference 1

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This paper cites Towards fast computation of certified robustness for relu networks,.

Verification of Visual Controllers via Compositional Geometric Transformations Towards fast computation of certified robustness for relu networks,

Reference 2

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This paper cites Automatic perturbation analy- sis for scalable certified robustness and beyond,.

Verification of Visual Controllers via Compositional Geometric Transformations Automatic perturbation analy- sis for scalable certified robustness and beyond,

Reference 3

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This paper cites Semidefinite relax- ations for certifying robustness to adversarial examples,.

Verification of Visual Controllers via Compositional Geometric Transformations Semidefinite relax- ations for certifying robustness to adversarial examples,

Reference 4

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Observation a253235f-ef5a-4140-8256-b942710d8712 · outbound

This paper cites Evaluating robustness of neural networks with mixed integer programming,.

Verification of Visual Controllers via Compositional Geometric Transformations Evaluating robustness of neural networks with mixed integer programming,

Reference 5

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Observation aa0dcd4f-3710-4706-8162-48e496c3c382 · outbound

This paper cites The marabou framework for verification and analysis of deep neural networks,.

Verification of Visual Controllers via Compositional Geometric Transformations The marabou framework for verification and analysis of deep neural networks,

Reference 6

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Observation 9fdcef05-8f6f-4c2b-bf7b-2f14f4f15cb3 · outbound

This paper cites Re- luplex: An efficient SMT solver for verifying deep neural networks,.

Verification of Visual Controllers via Compositional Geometric Transformations Re- luplex: An efficient SMT solver for verifying deep neural networks,

Reference 7

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Observation 1123e30a-a558-4091-8846-82dcb0ce0ee5 · outbound

This paper cites Verifying low-dimensional input neural networks via input quantization,.

Verification of Visual Controllers via Compositional Geometric Transformations Verifying low-dimensional input neural networks via input quantization,

Reference 8

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Observation c94b94b2-f74b-44fe-ac3e-59c332ce4099 · outbound

This paper cites Reachable polyhedral marching (RPM): A safety verification algorithm for robotic systems with deep neural network components,.

Verification of Visual Controllers via Compositional Geometric Transformations Reachable polyhedral marching (RPM): A safety verification algorithm for robotic systems with deep neural network components,

Reference 9

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Observation 8d675292-a289-41fc-8fdd-25470d8f36ea · outbound

This paper cites An abstract domain for certifying neural networks,.

Verification of Visual Controllers via Compositional Geometric Transformations An abstract domain for certifying neural networks,

Reference 10

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Source-reported events for the cited work

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Observation 94264b37-1387-4caf-81b8-ac643b2aace1 · outbound

This paper cites On the effectiveness of interval bound propagation for training verifiably robust models,.

Verification of Visual Controllers via Compositional Geometric Transformations On the effectiveness of interval bound propagation for training verifiably robust models,

Reference 11

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Observation c2699b50-fc78-48a6-be01-7d936fd63261 · outbound

This paper cites Certi- fying geometric robustness of neural networks,.

Verification of Visual Controllers via Compositional Geometric Transformations Certi- fying geometric robustness of neural networks,

Reference 12

Resolution
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Observation 8a585cb5-a747-4807-9236-4a45ba8091fc · outbound

This paper cites Towards verifying robustness of neural networks against a family of semantic perturbations,.

Verification of Visual Controllers via Compositional Geometric Transformations Towards verifying robustness of neural networks against a family of semantic perturbations,

Reference 13

Resolution
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Source-reported events for the cited work

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Observation bd58e50c-8529-43af-af1e-654449d522bf · outbound

This paper cites Tss: Transformation-specific smoothing for robustness certification,.

Verification of Visual Controllers via Compositional Geometric Transformations Tss: Transformation-specific smoothing for robustness certification,

Reference 14

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Observation 5c8cd7c7-7aa0-46bd-840f-178caf1b8470 · outbound

This paper cites Ver- ification of image-based neural network controllers using generative models,.

Verification of Visual Controllers via Compositional Geometric Transformations Ver- ification of image-based neural network controllers using generative models,

Reference 15

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Source-reported events for the cited work

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Observation e8285409-e41f-4acf-8bb8-09bf736323e1 · outbound

This paper cites Scalable and interpretable verifica- tion of image-based neural network controllers for autonomous vehi- cles,.

Verification of Visual Controllers via Compositional Geometric Transformations Scalable and interpretable verifica- tion of image-based neural network controllers for autonomous vehi- cles,

Reference 16

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Observation 2e03d769-efda-4b08-b80b-0b94684846bd · outbound

This paper cites Bunel, J.

Verification of Visual Controllers via Compositional Geometric Transformations Bunel, J

Reference 17

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Observation 01231d70-0e91-43c0-8d9f-0fa3f3449c72 · outbound

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Verification of Visual Controllers via Compositional Geometric Transformations Unresolved cited work

Reference 18

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Observation d5faa734-ec1c-442f-bab0-215f34a31417 · outbound

This paper cites Overt: An algorithm for safety verification of neural network control policies for nonlinear systems,.

Verification of Visual Controllers via Compositional Geometric Transformations Overt: An algorithm for safety verification of neural network control policies for nonlinear systems,

Reference 19

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Source-reported events for the cited work

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Observation a943515d-dad9-4dc7-b2bb-c56af9137ad1 · outbound

This paper cites One-shot reachability anal- ysis of neural network dynamical systems,.

Verification of Visual Controllers via Compositional Geometric Transformations One-shot reachability anal- ysis of neural network dynamical systems,

Reference 20

Resolution
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Source-reported events for the cited work

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Observation 91a2043b-e42b-4739-bd3e-4e0506bbac9a · outbound

This paper cites Reachability analysis of neural feedback loops,.

Verification of Visual Controllers via Compositional Geometric Transformations Reachability analysis of neural feedback loops,

Reference 21

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Source-reported events for the cited work

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Observation 28aa688c-bbbc-44de-b122-e253a7056bba · outbound

This paper cites A Reachability Method for Verifying Dynamical Systems with Deep Neural Network Controllers.

Verification of Visual Controllers via Compositional Geometric Transformations A Reachability Method for Verifying Dynamical Systems with Deep Neural Network Controllers

Reference 22

Resolution
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Source-reported events for the cited work

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Observation 511803fb-54f8-42da-b815-c75093abe669 · outbound

This paper cites Reach-SDP: Reachability analysis of closed-loop systems with neural network controllers via semidefinite programming,.

Verification of Visual Controllers via Compositional Geometric Transformations Reach-SDP: Reachability analysis of closed-loop systems with neural network controllers via semidefinite programming,

Reference 23

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Observation ac4bdcae-671d-4eb6-b5aa-e5fe51c035d7 · outbound

This paper cites Polar-express: Efficient and precise formal reachability analysis of neural-network controlled systems,.

Verification of Visual Controllers via Compositional Geometric Transformations Polar-express: Efficient and precise formal reachability analysis of neural-network controlled systems,

Reference 24

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Source-reported events for the cited work

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Observation cc247a7e-d87f-43bd-a3c6-3f58b7369937 · outbound

This paper cites Verisig: verifying safety properties of hybrid systems with neural network con- trollers,.

Verification of Visual Controllers via Compositional Geometric Transformations Verisig: verifying safety properties of hybrid systems with neural network con- trollers,

Reference 25

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 30422cbf-e5f4-4c86-89a9-f0299f0c97ab · outbound

This paper cites Reachability analysis for neural feedback systems using regressive polynomial rule inference,.

Verification of Visual Controllers via Compositional Geometric Transformations Reachability analysis for neural feedback systems using regressive polynomial rule inference,

Reference 26

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 7b4e386b-b729-4037-8042-18b27f51215f · outbound

This paper cites Reachnn: Reachability analysis of neural-network controlled systems,.

Verification of Visual Controllers via Compositional Geometric Transformations Reachnn: Reachability analysis of neural-network controlled systems,

Reference 27

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 9629dbea-7d10-4cc6-8597-b202a0cb77a7 · outbound

This paper cites Reachnn*: A tool for reachability analysis of neural-network controlled systems,.

Verification of Visual Controllers via Compositional Geometric Transformations Reachnn*: A tool for reachability analysis of neural-network controlled systems,

Reference 28

Resolution
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Source-reported events for the cited work

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Observation ef099629-a054-4a79-903b-2d1b0dfb4371 · outbound

This paper cites Reachable set estimation for neural network control systems: A simulation-guided approach,.

Verification of Visual Controllers via Compositional Geometric Transformations Reachable set estimation for neural network control systems: A simulation-guided approach,

Reference 29

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation a697411c-bef1-428e-9e1a-dcd95ba03f8e · outbound

This paper cites Neural network compression of ACAS Xu early prototype is unsafe: Closed-loop verification through quantized state backreachability,.

Verification of Visual Controllers via Compositional Geometric Transformations Neural network compression of ACAS Xu early prototype is unsafe: Closed-loop verification through quantized state backreachability,

Reference 30

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 82030749-3dd0-4807-b089-8679cc4eb362 · outbound

This paper cites Probably approximately correct vision- based planning using motion primitives,.

Verification of Visual Controllers via Compositional Geometric Transformations Probably approximately correct vision- based planning using motion primitives,

Reference 31

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation ba3a21af-d270-461b-a14d-03fea307b141 · outbound

This paper cites Safe output feedback motion planning from images via learned perception modules and contraction theory,.

Verification of Visual Controllers via Compositional Geometric Transformations Safe output feedback motion planning from images via learned perception modules and contraction theory,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:50:43.522915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation cd911dbc-2e2d-47b0-acaa-3b90c37f2249 · outbound

This paper cites Scenario-based Compositional Verification of Autonomous Systems with Neural Perception.

Verification of Visual Controllers via Compositional Geometric Transformations Scenario-based Compositional Verification of Autonomous Systems with Neural Perception

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:50:43.438921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 567931a5-643c-470d-b460-2da64efad802 · outbound

This paper cites Verifying controllers with vision-based perception using safe approx- imate abstractions,.

Verification of Visual Controllers via Compositional Geometric Transformations Verifying controllers with vision-based perception using safe approx- imate abstractions,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:50:43.514728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.307114Z digest=sha256:35a468e3ef65b52a374d29fbbf52a749b58966c297ca2e9c061bda993b0dfe31

Observation 9d2846cb-9d48-4b9f-b14f-026755819562 · outbound

This paper cites System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles.

Verification of Visual Controllers via Compositional Geometric Transformations System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T19:50:43.310457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:50:43.310457Z digest=sha256:f8ee994244c633fe1700d0881c92de848b05429180e0db08333f58640fac846c

Observation 65bc631a-d7fd-49ca-ac9d-f77572e2dd33 · outbound

This paper cites Enhancing Safety and Robustness of Vision-Based Controllers via Reachability Analysis.

Verification of Visual Controllers via Compositional Geometric Transformations Enhancing Safety and Robustness of Vision-Based Controllers via Reachability Analysis

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:50:43.416012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.314316Z digest=sha256:b4ee7ea543ce51f556312a36e233f1a8140584644a8eff12975ce4b3cfce22e7

Observation 26742cc4-4709-42d8-9fe9-dbe83d5848a8 · outbound

This paper cites Discovering closed-loop failures of vision-based controllers via reachability analysis,.

Verification of Visual Controllers via Compositional Geometric Transformations Discovering closed-loop failures of vision-based controllers via reachability analysis,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:50:43.506643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.317576Z digest=sha256:45a04aa773461ebf1ea65ce45110b66b75c5a7c9174b7ca0f51c48668bf4be46

Observation 419f8337-8d5a-45a6-bba3-9206fa13e107 · outbound

This paper cites Toward certified robustness against real-world distribution shifts,.

Verification of Visual Controllers via Compositional Geometric Transformations Toward certified robustness against real-world distribution shifts,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:50:43.498489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.320545Z digest=sha256:4928bb8b9a19f5ccb6aaedbb57ca7965591e769302d6b14c8f172547290f4719

Observation 437e5d93-38e7-4330-97bc-990483ada951 · outbound

This paper cites Data-Driven Modeling and Verification of Perception-Based Autonomous Systems.

Verification of Visual Controllers via Compositional Geometric Transformations Data-Driven Modeling and Verification of Perception-Based Autonomous Systems

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:50:43.403342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.324281Z digest=sha256:496a7f744949e5f3bc584748381f6e5c54d5830d6a77ee52509b23723543b2ab

Observation c8e15f4b-7672-4d12-8a61-d5e3cfe5fee7 · outbound

This paper cites Enforcing safety for vision-based controllers via control barrier functions and neural radiance fields,.

Verification of Visual Controllers via Compositional Geometric Transformations Enforcing safety for vision-based controllers via control barrier functions and neural radiance fields,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:50:43.490737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.328091Z digest=sha256:1316ae6b888c2f6099cd895681b76d93eab565de83e24a711668e8a60d584c75

Observation e7e82b67-56e5-4ba0-8359-f8014341a517 · outbound

This paper cites Nnlander-verif: A neural network formal verification framework for vision-based autonomous aircraft landing,.

Verification of Visual Controllers via Compositional Geometric Transformations Nnlander-verif: A neural network formal verification framework for vision-based autonomous aircraft landing,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:50:43.482357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.331463Z digest=sha256:224ff5919ae641a8c872290f7ec1c6249707cdfee5aff08e1c586e6f010e9f4d

Observation 8153510d-b55f-4c21-9852-1bb93420cd6d · outbound

This paper cites One-Shot Reachability Analysis of Neural Network Dynamical Systems.

Verification of Visual Controllers via Compositional Geometric Transformations One-Shot Reachability Analysis of Neural Network Dynamical Systems

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:50:43.389140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.334391Z digest=sha256:35eaf60a06813953b8b674b334239a94b1340e5815be3c0e5703837f00f44248

Observation 25b6d545-c84d-4cfd-8c46-871427d3c32e · outbound

This paper cites Branch and bound for piecewise linear neural network verification,.

Verification of Visual Controllers via Compositional Geometric Transformations Branch and bound for piecewise linear neural network verification,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:50:43.473224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.337325Z digest=sha256:9460f7af0577c4e14fa5c9bb41048f9166b9b715bed0be7f016e2daaa2739c11

Observation 468bde36-858d-4885-816b-5268355cbee3 · outbound

This paper cites Neural network verification with branch-and-bound for general nonlineari- ties,.

Verification of Visual Controllers via Compositional Geometric Transformations Neural network verification with branch-and-bound for general nonlineari- ties,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:50:43.464326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T19:50:43.340816Z digest=sha256:575dcac96704beb2f1a3c7b91c68971d64d5c6c98b13b9772ed7fbff07d07bc5

Observation 7d52518b-ed46-42db-a5c1-5c09b74a8336 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Verification of Visual Controllers via Compositional Geometric Transformations Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T19:50:43.343745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:50:43.343745Z digest=sha256:6b0a2004965620699d9005db901212d1e4fcf60a3f3f8dd28bd13d1df19b3514

Observation 98ea7f20-979f-4e68-93ab-dc9806be6f2a · outbound

This paper cites Proximal Policy Optimization Algorithms.

Verification of Visual Controllers via Compositional Geometric Transformations Proximal Policy Optimization Algorithms

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T19:50:43.347369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:50:43.347369Z digest=sha256:af62bc32c6c1af45bd56f67ec608e0c5d3a8be80ae2f81a556bec583029db64a

Observation 7a5a5c7d-4972-4e48-8b18-b299a7325280 · outbound

This paper cites On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.

Verification of Visual Controllers via Compositional Geometric Transformations On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-06T19:50:43.236620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:50:43.236620Z digest=sha256:db01af0be59e8530077ec01f2784f012c06e000c32dc6b04b3e5b5e6b0cd3954

Pith citing papers

No inbound Pith citation observations are available.