Pith. sign in

Paper Citation Record · LEDGER

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies

As of 8 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2507.02953.

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

pith.paper-citation-record.v1
2507.02953 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:54:43.733181Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

16 of 16 outbound references displayed

  • verified exact0
  • verified fuzzy6
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 68031814-6257-4cd0-b8f0-abf4b0f6c546 · outbound

This paper cites Barbara, Ruigang Wang, and Ian R.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Barbara, Ruigang Wang, and Ian R

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:54:44.110245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T21:54:43.259793Z digest=sha256:3f9679f5b0c110de304427fd416cda79fec3e4294299105a86c4cb90f69b58e6

Observation ac32c450-c7d8-4fa1-8910-10aee3d22b17 · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.304291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.304291Z digest=sha256:cac26c37141c675e92854e212394b6113942f2d2cc574b724a8c9433ffd85ee5

Observation b5cadf90-4cdd-4e55-a4e8-704910adf056 · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.374298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.374298Z digest=sha256:a42360be7d0b2ae3f7207d9a4d315ae7cc06952e6e5e8c324a5230bcad3e52c6

Observation cb30de8c-9d57-42ee-b7da-127e8aef4638 · outbound

This paper cites Measure theory and fine properties of functions.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Measure theory and fine properties of functions

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:54:44.084494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T21:54:43.450200Z digest=sha256:9227319b4f710f2cb0bebba01d1f779a551b03b903b52198602ce65a5fbc2cf6

Observation 8939fc97-3c1d-4aa8-8338-73c370226091 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.540799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.540799Z digest=sha256:2e86f8f52f20530e25ad229186e289b2fc353cfc83d3f96d6bdaf82a9ac6dbed

Observation 11fa7e59-32c5-40c5-ba11-5618b2dcd1be · outbound

This paper cites A Review of Safe Reinforcement Learning: Methods, Theory and Applications.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies A Review of Safe Reinforcement Learning: Methods, Theory and Applications

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.614688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.614688Z digest=sha256:6f1b38dc60416b3d4c33f96875496d7716f4a4df7041c44f09ba6302245c2dc4

Observation ad9ef977-e278-477b-8f12-8e98788816a8 · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Second order derivatives for network pruning: Optimal brain surgeon

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.636450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.636450Z digest=sha256:e90a8e9cbea33da550ce60d48523e9dc96bec006e289913300ca5acfc03467a6

Observation acc74348-4943-4f50-8ba2-8de514599c98 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.646080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.646080Z digest=sha256:20df9221d4663cc3b8e3cd144790a0c3613da88cb40d7b0406b51e7e486fb64c

Observation fe2b7153-c516-482d-ab47-65bd43170578 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Gaussian Error Linear Units (GELUs)

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.653791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.653791Z digest=sha256:64894c6af476ad5489de4286204c2e871dfb9d5e0265bfdde6fbcc4b93be252a

Observation 1a2624cb-419a-43aa-b36d-e2d5b0f8ba39 · outbound

This paper cites Optimal brain damage.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Optimal brain damage

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.666564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.666564Z digest=sha256:c1c456f1303cafbde8d9686702249d38a3959875d1c2a339bcac8ac273c3d78a

Observation 621c0a69-4451-4555-82f8-7b6d1f3ac046 · outbound

This paper cites Rectifier nonlinearities improve neural network acoustic models.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Rectifier nonlinearities improve neural network acoustic models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.692854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.692854Z digest=sha256:ea3831d1bafa4295cdddb77a723c47ba2cb8d7f0c087537e2757bcdb1598a6e7

Observation c052c6bb-254c-4bb8-8b2a-25de66eb08ff · outbound

This paper cites Human-level control through deep reinforcement learning.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Human-level control through deep reinforcement learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:43.700268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:43.700268Z digest=sha256:88748876f80c7c654bfc96df75e275ddc6afcdf9c11583916de6f9afee64c53f

Observation 04bd743e-85f8-4a15-8f42-b33d1faaa8e6 · outbound

This paper cites On the effects of pruning on evolved neural controllers for soft robots.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies On the effects of pruning on evolved neural controllers for soft robots

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:54:43.986890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T21:54:43.711636Z digest=sha256:c526ef91df71efbe91580ff491e1fca03f2313cf07203f6a7708154cf09f97b9

Observation e25dfab3-e6f5-4fd7-ab97-8275632d26e3 · outbound

This paper cites Rectified linear units improve restricted boltzmann machines.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Rectified linear units improve restricted boltzmann machines

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:54:43.958730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T21:54:43.719065Z digest=sha256:55a857c2cc01a49a0126768bb2485834fe4af036170ca4436cd0fe036ab6e5c3

Observation 061ac16d-3a26-4342-9616-28399e032014 · outbound

This paper cites Lipschitz regularity of deep neural networks: Analysis and efficient estimation.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Lipschitz regularity of deep neural networks: Analysis and efficient estimation

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:54:43.934786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T21:54:43.726294Z digest=sha256:0d4890b16622fdc28458b54fd8389916d95eb342e1742c1ab7d48c19567d1f03

Observation 779f8b3e-3901-4446-8aba-d512216052b9 · outbound

This paper cites Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective, October 2022.

Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective, October 2022

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:54:43.911517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T21:54:43.733181Z digest=sha256:dbeab43104cab1fff9932ddee7c36dc29f695a2b49d53b781958d7adb3fbcdbe

Pith citing papers

No inbound Pith citation observations are available.