Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-01T06:11:33.633679Z
Paper Citation Record · LEDGER
Improving Certified Robustness via Adversarial Distillation
As of 19 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2606.31653.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
49 of 49 outbound references displayed
- verified exact1
- verified fuzzy44
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ea5cbbcc-2439-4bd9-8109-fd8e3e63a208 · outbound
This paper cites Adversarial training and provable defenses: Bridging the gap.
Improving Certified Robustness via Adversarial Distillation Adversarial training and provable defenses: Bridging the gap
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.965436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e46d43a9-2af1-4552-bd1d-abf277c14c28 · outbound
This paper cites Evasion attacks against machine learning at test time.
Improving Certified Robustness via Adversarial Distillation Evasion attacks against machine learning at test time
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.055117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 49654b21-983a-48c7-bd02-d271b0f0f45f · outbound
This paper cites an unresolved cited work.
Improving Certified Robustness via Adversarial Distillation Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-07-06T19:12:51.056894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 805275a2-c5bb-4797-b810-7b8e3c6889e2 · outbound
This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.
Improving Certified Robustness via Adversarial Distillation Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.050868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 17681aff-89d9-40c6-afb6-149d065cb25f · outbound
This paper cites Decoupled kullback-leibler divergence loss.
Improving Certified Robustness via Adversarial Distillation Decoupled kullback-leibler divergence loss
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.014933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4269974d-f3e0-4a7c-b447-a07d23ac9716 · outbound
This paper cites Learning better certified models from empirically-robust teachers, 2026.
Improving Certified Robustness via Adversarial Distillation Learning better certified models from empirically-robust teachers, 2026
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T09:45:40.529469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 222c93a6-5df9-4cb2-bf90-944efe5d71ca · outbound
This paper cites Pawan Kumar, and Robert Stanforth.
Improving Certified Robustness via Adversarial Distillation Pawan Kumar, and Robert Stanforth
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.058743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 13384826-4b3c-42dd-ad28-f9a2108785dc · outbound
This paper cites Pawan Kumar, Robert Stan- forth, and Alessio Lomuscio.
Improving Certified Robustness via Adversarial Distillation Pawan Kumar, Robert Stan- forth, and Alessio Lomuscio
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.034653Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7ae73078-5b88-4b00-823e-4714bdb28ae1 · outbound
This paper cites Formal verification of piece-wise linear feed-forward neural networks.Auto- mated Technology for Verification and Analysis, 2017.
Improving Certified Robustness via Adversarial Distillation Formal verification of piece-wise linear feed-forward neural networks.Auto- mated Technology for Verification and Analysis, 2017
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.035799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f7f397a1-8b65-4698-ae23-f60bf8c77bd8 · outbound
This paper cites Complete verification via multi-neuron relaxation guided branch-and-bound.
Improving Certified Robustness via Adversarial Distillation Complete verification via multi-neuron relaxation guided branch-and-bound
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.065837Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1644621c-b95c-41e5-84e6-a2b9989beaf5 · outbound
This paper cites Adversarially robust distillation.
Improving Certified Robustness via Adversarial Distillation Adversarially robust distillation
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.969569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3c90a58c-d210-4be8-9100-ed9e3212789c · outbound
This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.
Improving Certified Robustness via Adversarial Distillation Goodfellow, Jonathon Shlens, and Christian Szegedy
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.061380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7583bbdf-ed30-4286-877f-5c681e702228 · outbound
This paper cites On the effectiveness of interval bound propagation for training verifiably robust models.
Improving Certified Robustness via Adversarial Distillation On the effectiveness of interval bound propagation for training verifiably robust models
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.992338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 20da01fe-4d88-45d0-be48-9d605b3ee6b1 · outbound
This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.
Improving Certified Robustness via Adversarial Distillation Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.990482Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 919976b0-007a-4ec5-a298-f4cb58b9503c · outbound
This paper cites Distilling the knowledge in a neural network.
Improving Certified Robustness via Adversarial Distillation Distilling the knowledge in a neural network
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.043943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c9fee689-d145-4fd2-afdd-d0da9dae958c · outbound
This paper cites On the paradox of certified training.Transactions on Machine Learning Research, 2022.
Improving Certified Robustness via Adversarial Distillation On the paradox of certified training.Transactions on Machine Learning Research, 2022
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.017040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2dffc7db-d077-48e2-a53d-a9015f869aba · outbound
This paper cites Reluplex: An efficient SMT solver for verifying deep neural networks.
Improving Certified Robustness via Adversarial Distillation Reluplex: An efficient SMT solver for verifying deep neural networks
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.998519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a90f809f-a325-4c88-bf62-e999bf072a51 · outbound
This paper cites Kingma and Jimmy Ba.
Improving Certified Robustness via Adversarial Distillation Kingma and Jimmy Ba
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.973417Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 34b79291-dd37-485d-9c2a-252e9674daf2 · outbound
This paper cites Learning multiple layers of features from tiny images.
Improving Certified Robustness via Adversarial Distillation Learning multiple layers of features from tiny images
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.000590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 287f2bce-3d51-4163-a635-9831cc64435c · outbound
This paper cites Tiny imagenet visual recognition challenge.CS 231N, 7(7):3.
Improving Certified Robustness via Adversarial Distillation Tiny imagenet visual recognition challenge.CS 231N, 7(7):3
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.040214Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9a673c5b-ab11-4021-b247-1cc395544f93 · outbound
This paper cites an unresolved cited work.
Improving Certified Robustness via Adversarial Distillation Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-07-06T19:12:51.010512Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3251c365-c528-4217-88b9-7d2ebd65aa8c · outbound
This paper cites an unresolved cited work.
Improving Certified Robustness via Adversarial Distillation Unresolved cited work
Reference 22
- Resolution
- unresolved
- raw_fallback, observed 2026-07-06T19:12:51.048958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 238258a5-f5b5-4837-bb9d-afacf9f4a296 · outbound
This paper cites Towards deep learning models resistant to adversarial attacks.
Improving Certified Robustness via Adversarial Distillation Towards deep learning models resistant to adversarial attacks
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.028850Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e5d04049-26ad-4403-ac64-5d880fb7bef2 · outbound
This paper cites Connecting certified and adversarial training.
Improving Certified Robustness via Adversarial Distillation Connecting certified and adversarial training
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.052802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 868bacc5-b97a-436f-9cc6-09d48dfa074d · outbound
This paper cites Understanding certified training with interval bound propagation.
Improving Certified Robustness via Adversarial Distillation Understanding certified training with interval bound propagation
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.036571Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9a450ff5-e50e-4557-993c-989cef480add · outbound
This paper cites Ctbench: A library and benchmark for certified training.
Improving Certified Robustness via Adversarial Distillation Ctbench: A library and benchmark for certified training
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.967830Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation adb684e1-c67a-4448-be04-7dda8cf1e22c · outbound
This paper cites Differentiable abstract interpretation for provably robust neural networks.
Improving Certified Robustness via Adversarial Distillation Differentiable abstract interpretation for provably robust neural networks
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.979631Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 603ea08b-7ee7-4382-a941-6e69d26519be · outbound
This paper cites Certified training: Small boxes are all you need.
Improving Certified Robustness via Adversarial Distillation Certified training: Small boxes are all you need
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.063495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3c0e5c7b-5557-453c-8a8f-5a1deabc7720 · outbound
This paper cites Pytorch: An imperative style, high-performance deep learning library.
Improving Certified Robustness via Adversarial Distillation Pytorch: An imperative style, high-performance deep learning library
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.997453Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9392de4c-778d-44b9-a30b-3c67129b573e · outbound
This paper cites Fast certified robust training with short warmup.
Improving Certified Robustness via Adversarial Distillation Fast certified robust training with short warmup
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.025460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e9a72c9d-ee6b-4e47-bd15-07295d1ab241 · outbound
This paper cites An abstract domain for certifying neural networks.Proc.
Improving Certified Robustness via Adversarial Distillation An abstract domain for certifying neural networks.Proc
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.043215Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3dec6767-dde2-40ff-95a1-dcae688026e0 · outbound
This paper cites Intriguing properties of neural networks.
Improving Certified Robustness via Adversarial Distillation Intriguing properties of neural networks
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.037605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4e867a8b-4f82-448c-b150-69c7df6fede5 · outbound
This paper cites Evaluating robustness of neural networks with mixed integer programming.
Improving Certified Robustness via Adversarial Distillation Evaluating robustness of neural networks with mixed integer programming
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.039446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 930f5377-b45d-434a-a6c5-e253e82c86ae · outbound
This paper cites On adaptive attacks to adversarial example defenses.
Improving Certified Robustness via Adversarial Distillation On adaptive attacks to adversarial example defenses
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.041126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b45458b1-82cd-49ad-829c-34edee1df26d · outbound
This paper cites Beta-CROWN: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification.
Improving Certified Robustness via Adversarial Distillation Beta-CROWN: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.046982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2360f043-58a2-4074-9fe6-6e3a6a814c5d · outbound
This paper cites Zico Kolter.
Improving Certified Robustness via Adversarial Distillation Zico Kolter
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.991620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 16fc48f5-b9a5-4676-ab17-eaaf4fdc4392 · outbound
This paper cites Automatic perturbation analysis for scalable certified robustness and beyond.Advances in Neural Information Processing Systems, 2020.
Improving Certified Robustness via Adversarial Distillation Automatic perturbation analysis for scalable certified robustness and beyond.Advances in Neural Information Processing Systems, 2020
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.029580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5b805d12-2b85-472f-ae14-934c86f32df5 · outbound
This paper cites Fast and Complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers.
Improving Certified Robustness via Adversarial Distillation Fast and Complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.033926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1518904c-4873-45e8-adfd-6bff78189075 · outbound
This paper cites Rethinking lipschitz neural networks and certified robustness: A boolean function perspective.
Improving Certified Robustness via Adversarial Distillation Rethinking lipschitz neural networks and certified robustness: A boolean function perspective
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.047920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 383acd68-578a-4a23-a780-22e191f4eac9 · outbound
This paper cites Boosting the certified robustness of l-infinity distance nets.
Improving Certified Robustness via Adversarial Distillation Boosting the certified robustness of l-infinity distance nets
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.045023Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 969d7fbf-d306-4c41-baeb-6387511176c6 · outbound
This paper cites Efficient neural network robustness certification with general activation functions.
Improving Certified Robustness via Adversarial Distillation Efficient neural network robustness certification with general activation functions
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.031858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 700a03ba-0725-40fd-a3d4-954653563a76 · outbound
This paper cites Towards stable and efficient training of verifiably robust neural networks.
Improving Certified Robustness via Adversarial Distillation Towards stable and efficient training of verifiably robust neural networks
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.977388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8fd583a5-f43a-40fa-bdca-e4c75db59d60 · outbound
This paper cites General cutting planes for bound-propagation-based neural network verification.
Improving Certified Robustness via Adversarial Distillation General cutting planes for bound-propagation-based neural network verification
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.018802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a2af17b3-335c-4a76-8ba5-7b91f0d760ae · outbound
This paper cites Generating less certain adversarial examples improves robust generalization.Transactions on Machine Learning Research, 2025.
Improving Certified Robustness via Adversarial Distillation Generating less certain adversarial examples improves robust generalization.Transactions on Machine Learning Research, 2025
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.045761Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 674ad7f2-58b0-43c6-84ec-bcc139a2f002 · outbound
This paper cites Reliable adversarial distillation with unreliable teachers.
Improving Certified Robustness via Adversarial Distillation Reliable adversarial distillation with unreliable teachers
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.030726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e7ccb5c1-1910-42ef-b131-bf9d38c21a6a · outbound
This paper cites Revisiting adversarial robustness distillation: Robust soft labels make student better.
Improving Certified Robustness via Adversarial Distillation Revisiting adversarial robustness distillation: Robust soft labels make student better
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:50.984501Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 31719d8e-bc52-4076-a52c-d7366d112280 · outbound
This paper cites [29], with weight 0.5 on MNIST and CIFAR-10, and 0.2 on TinyImageNet, during warmup.
Improving Certified Robustness via Adversarial Distillation [29], with weight 0.5 on MNIST and CIFAR-10, and 0.2 on TinyImageNet, during warmup
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.005915Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 83203196-fbda-4141-90c9-ff8c897949f2 · outbound
This paper cites an unresolved cited work.
Improving Certified Robustness via Adversarial Distillation Unresolved cited work
Reference 48
- Resolution
- unresolved
- raw_fallback, observed 2026-07-06T19:12:51.021004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4c76dda3-0705-4e6d-938e-dc0f03d2da81 · outbound
This paper cites The final hyperparameters used for AD-CERT across all settings are reported in Table 6.
Improving Certified Robustness via Adversarial Distillation The final hyperparameters used for AD-CERT across all settings are reported in Table 6
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-06T19:12:51.023586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.