Paper Citation Record · LEDGER

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 10 inbound Pith citation observations for arXiv:2508.00222.

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

pith.paper-citation-record.v1
2508.00222 v5

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T01:15:49.658412Z

measured 40 of 40 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:34:06.659805Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T22:27:26.448121Z

Reference resolution

30 of 30 outbound references displayed

verified exact19
verified fuzzy7
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7b22eef4-03c0-4df3-93c4-657d5cecb0a0 · outbound

This paper cites How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-19T01:16:56.985752Z

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.

Observation 6ae68cf3-9cc2-46be-b62b-fa959f8e26cb · outbound

This paper cites Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-19T01:16:56.993587Z

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.

Observation b96672c0-0bed-4193-9ae3-4c8ab85aac4f · outbound

This paper cites Evaluating Large Language Models Trained on Code.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Evaluating Large Language Models Trained on Code

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.060549Z

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.

Observation 1f0cd3ad-c5a0-4f7f-b891-ee41de0ec916 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:56.979210Z

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.

Observation c02c08fd-8d9a-4359-bab6-f00a2eeacb8f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Training Verifiers to Solve Math Word Problems

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:56.999752Z

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.

Observation d341d843-23ac-4668-85cc-2a4cd35da58b · outbound

This paper cites Process Reinforcement through Implicit Rewards.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Process Reinforcement through Implicit Rewards

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.015996Z

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.

Observation d9588cdb-293f-4ccf-bcee-e4773f677bf4 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.007208Z

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.

Observation 4cccdf65-b86f-4eaa-8584-d24c5534aadb · outbound

This paper cites Teaching Large Language Models to Reason with Reinforcement Learning.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Teaching Large Language Models to Reason with Reinforcement Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-19T01:16:57.097300Z

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.

Observation a05552eb-1069-4963-ae17-3dd4728d2867 · outbound

This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.023034Z

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.

Observation d23aded9-6e25-426d-8887-42be8eb3e84e · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T01:16:57.066799Z

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.

Observation e437c7e8-593e-4319-abf4-4ed102200f61 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.045584Z

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.

Observation 90be6e53-f651-415a-b8a1-e2b32ae7e8e1 · outbound

This paper cites SuperRL: Reinforcement Learning with Supervision to Boost Language Model Reasoning.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization SuperRL: Reinforcement Learning with Supervision to Boost Language Model Reasoning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-19T01:16:56.964027Z

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.

Observation 22272cb6-e500-487e-8f2a-9bfa08c6b452 · outbound

This paper cites John Wiley & Sons.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization John Wiley & Sons

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T01:16:57.416280Z

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.

Observation fab2b4f4-8975-4504-98b3-c6df7177afda · outbound

This paper cites Proximal Policy Optimization Algorithms.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Proximal Policy Optimization Algorithms

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.039204Z

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.

Observation 3614caa9-715a-41bb-9e26-b7ea0abf370f · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.032605Z

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.

Observation c62b401a-4ddc-4b1c-8099-2e529cba08b3 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization HybridFlow: A Flexible and Efficient RLHF Framework

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.078802Z

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.

Observation 25803ded-892f-4eb0-8dfd-08c9acb5dbfa · outbound

This paper cites Reinforcement Learning for Reasoning in Large Language Models with One Training Example.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.104689Z

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.

Observation 7e1e3c82-2fa3-4b9b-8617-47f0d56dc2e1 · outbound

This paper cites UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T01:16:57.084581Z

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.

Observation 7132f0ea-f4be-46f5-b269-58b3540dbda3 · outbound

This paper cites Learning to Reason under Off-Policy Guidance.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Learning to Reason under Off-Policy Guidance

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.090142Z

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.

Observation 022e8f73-c2b8-4372-957f-d47abeb89bf0 · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.073079Z

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.

Observation 179d49d9-5143-4989-a9c2-f99e8d816395 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:57.054416Z

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.

Observation 1690ad0a-2a23-4c81-9284-9c8fccd9f666 · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-19T01:16:56.971782Z

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.

Observation 724ed7d1-bae9-44e3-b0da-9ea529315ae7 · outbound

This paper cites First, we dissect the bias and variance issues inherent to standard Importance Sampling (IS) when using data from a single behavior policy.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization First, we dissect the bias and variance issues inherent to standard Importance Sampling (IS) when using data from a single behavior policy

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T01:16:57.405776Z

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.

Observation b3195e19-88f0-4e10-a4bc-89576fb6853d · outbound

This paper cites Both theχ 2-divergence and the more commonly known KL-divergence (DKL(πθ∥πω)) are measures of dissimilarity between distributions (both are instances of f-divergences).

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Both theχ 2-divergence and the more commonly known KL-divergence (DKL(πθ∥πω)) are measures of dissimilarity between distributions (both are instances of f-divergences)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T01:16:57.419753Z

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.

Observation e7d5442c-d63d-492c-86aa-29264ba58b21 · outbound

This paper cites safety net.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization safety net

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T01:16:57.402310Z

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.

Observation 1de1f09c-09e0-4355-983e-fcdd4ee855d9 · outbound

This paper cites an unresolved cited work.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-19T01:16:57.399065Z

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.

Observation b5dc6cc1-442a-4442-89f9-ab6ee5d81ee2 · outbound

This paper cites For our approach, one of the model-generated rollouts is replaced with a correct reasoning trajectory from the training dataset.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization For our approach, one of the model-generated rollouts is replaced with a correct reasoning trajectory from the training dataset

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T01:16:57.392025Z

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.

Observation 44f5a834-1b23-4e05-842f-ab92b39fa250 · outbound

This paper cites an unresolved cited work.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-05-19T01:16:57.413093Z

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.

Observation 732bfdc5-a802-4494-8539-6bbe1f4e0228 · outbound

This paper cites During evaluation, we set the sampling temperature to 0.6 and report the average pass@1 score over 5 runs by default.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization During evaluation, we set the sampling temperature to 0.6 and report the average pass@1 score over 5 runs by default

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T01:16:57.409654Z

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.

Observation 609a9226-2fbf-49ab-a86f-b939df71e029 · outbound

This paper cites The second category consists of four straightforward baselines: 1)SFT, supervised fine-tuning using external reasoning trajectory data.

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization The second category consists of four straightforward baselines: 1)SFT, supervised fine-tuning using external reasoning trajectory data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T01:16:57.395871Z

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.

Pith citing papers

Observation f544093f-ec86-4b34-af8a-850645821afe · inbound

Beyond the Sampled Token: Preserving Candidate Support in RLVR cites this paper.

Beyond the Sampled Token: Preserving Candidate Support in RLVR RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T09:34:06.659805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da083899-7760-4574-bf2b-59638c3afc2a · inbound

Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models cites this paper.

Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T05:28:11.383416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f852c4b-d9b7-4d86-b2d2-d3e785e29c46 · inbound

Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy cites this paper.

Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-13T19:53:11.743641Z

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.

Observation 895f2d4e-3db9-4fed-a88e-6bfe5a51724f · inbound

Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.

Rethinking Agentic Reinforcement Learning In Large Language Models RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-12T10:21:30.040182Z

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.

Observation 9b2c95e0-46d3-4174-9cd4-f5bdfe9d76c1 · inbound

Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.

Rethinking Agentic Reinforcement Learning In Large Language Models RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-11T22:11:16.745580Z

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.

Observation 54793fe2-ed12-4d07-9310-e806fa66d86f · inbound

Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.

Rethinking Agentic Reinforcement Learning In Large Language Models RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-19T17:02:40.643093Z

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.

Observation 988a5c69-d1bf-4da7-a7e1-f9ce65807ce5 · inbound

Beyond Uniform Credit Assignment: Selective Eligibility Traces for RLVR cites this paper.

Beyond Uniform Credit Assignment: Selective Eligibility Traces for RLVR RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-11T16:36:10.539031Z

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.

Observation 026b34fc-4a97-43da-bb62-a9a4b3d59496 · inbound

Rollout-Level Advantage-Prioritized Experience Replay for GRPO cites this paper.

Rollout-Level Advantage-Prioritized Experience Replay for GRPO RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-02T06:06:41.027689Z

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.

Observation 4ee16055-4ec2-4e07-a709-aaf498fc08b5 · inbound

When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff cites this paper.

When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 31

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T22:27:26.449420Z

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.

Observation 637b06e1-7920-48cd-8901-029268f58fa7 · inbound

Preference Tuning as Spectral Update Reorganization cites this paper.

Preference Tuning as Spectral Update Reorganization RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-02T14:23:19.627366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.