Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-18T11:54:29.955833Z
Paper Citation Record · LEDGER
Polychromic Objectives for Reinforcement Learning
As of 5 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2509.25424.
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 2026-08-05T06:32:48.257954+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 exact32
- verified fuzzy12
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch4
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 0e4122ff-ca3b-4075-a6d5-c739314e406b · outbound
This paper cites Minimax Regret Bounds for Reinforcement Learning.
Polychromic Objectives for Reinforcement Learning Minimax Regret Bounds for Reinforcement Learning
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.827701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1914c4a8-fa7c-414b-b0ec-14e3df6247d2 · outbound
This paper cites Sutton, Mohammad Ghavamzadeh, and Mark Lee.
Polychromic Objectives for Reinforcement Learning Sutton, Mohammad Ghavamzadeh, and Mark Lee
Reference 2
- Resolution
- verified exact
- doi, observed 2026-05-18T11:56:19.701442Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b9a0af0a-d0ac-4be2-be05-b257b6ddbf6e · outbound
This paper cites Babyai: A platform to study the sample ef- ficiency of grounded language learning.
Polychromic Objectives for Reinforcement Learning Babyai: A platform to study the sample ef- ficiency of grounded language learning
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.199891Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c0e503b3-e523-4b8f-a701-ff9a49998555 · outbound
This paper cites Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks.
Polychromic Objectives for Reinforcement Learning Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:20.033957Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a28d9f83-96a2-4709-a56a-238ee8093fd0 · outbound
This paper cites Inference-aware fine-tuning for best-of-n sampling in large language models.
Polychromic Objectives for Reinforcement Learning Inference-aware fine-tuning for best-of-n sampling in large language models
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.894737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation dd7549a0-eae4-444e-8696-951a6f767169 · outbound
This paper cites The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models.
Polychromic Objectives for Reinforcement Learning The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.994404Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 85e8162f-36cd-4e2d-a5b7-0d20e902af26 · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
Polychromic Objectives for Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.833356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 234935e7-595c-47ca-aef3-af07a69a4801 · outbound
This paper cites Off-Policy Actor-Critic.
Polychromic Objectives for Reinforcement Learning Off-Policy Actor-Critic
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.983519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b04fe9f6-bf56-4d94-a267-a3711a02feae · outbound
This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.
Polychromic Objectives for Reinforcement Learning The Vendi Score: A Diversity Evaluation Metric for Machine Learning
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:20.028364Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 28dd0253-8e81-4ca1-9bec-701910b0481a · outbound
This paper cites Reinforcement Learning with Deep Energy-Based Policies.
Polychromic Objectives for Reinforcement Learning Reinforcement Learning with Deep Energy-Based Policies
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.864851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8b3fdffd-3dba-4940-8a86-87cb9cda0513 · outbound
This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.
Polychromic Objectives for Reinforcement Learning Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.859035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ba013230-dea9-4666-b986-e95ca2f46054 · outbound
This paper cites Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening.
Polychromic Objectives for Reinforcement Learning Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.854228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b736e6f2-fc7e-4476-b9b8-fb85ef0ec722 · outbound
This paper cites Marginalized state distribution entropy regularization in policy optimization.
Polychromic Objectives for Reinforcement Learning Marginalized state distribution entropy regularization in policy optimization
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.193050Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 891ae641-6a20-459c-b44b-17d9b4e287e5 · outbound
This paper cites A natural policy gradient.
Polychromic Objectives for Reinforcement Learning A natural policy gradient
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.196096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c31ca28c-2017-47c3-94e0-9fed02aa8782 · outbound
This paper cites an unresolved cited work.
Polychromic Objectives for Reinforcement Learning Unresolved cited work
Reference 15
- Resolution
- unresolved
- raw_fallback, observed 2026-05-18T11:56:20.189783Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 15c00602-be23-4d54-a125-c55e43d1c737 · outbound
This paper cites Kakade and John Langford.
Polychromic Objectives for Reinforcement Learning Kakade and John Langford
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.177156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1fb923ae-a3f1-4e42-be94-8fce0d68c6c1 · outbound
This paper cites VinePPO: Refining Credit Assignment in RL Training of LLMs.
Polychromic Objectives for Reinforcement Learning VinePPO: Refining Credit Assignment in RL Training of LLMs
Reference 17
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T11:56:19.879623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e3ec2f14-269f-44eb-8395-1e8070994e67 · outbound
This paper cites One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL.
Polychromic Objectives for Reinforcement Learning One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.844565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1bdbf20f-7ede-4e7f-b3ea-34698fa88b5a · outbound
This paper cites Diverse Preference Optimization.
Polychromic Objectives for Reinforcement Learning Diverse Preference Optimization
Reference 19
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T11:56:19.999530Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d0ae5256-a762-42b8-908d-a7886cedb03b · outbound
This paper cites Jointly Reinforcing Diversity and Quality in Language Model Generations.
Polychromic Objectives for Reinforcement Learning Jointly Reinforcing Diversity and Quality in Language Model Generations
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:20.020157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 60c55b26-fb85-4d1a-9bf1-31bd405cbe5c · outbound
This paper cites Lillicrap, Jonathan J.
Polychromic Objectives for Reinforcement Learning Lillicrap, Jonathan J
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.183627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a255ff8d-3236-48bd-933c-e4d4ee1114dc · outbound
This paper cites Continuous control with deep reinforcement learning.
Polychromic Objectives for Reinforcement Learning Continuous control with deep reinforcement learning
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:20.039145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 60d0afb0-2839-4ec2-86e3-03d148585d63 · outbound
This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.
Polychromic Objectives for Reinforcement Learning Understanding R1-Zero-Like Training: A Critical Perspective
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.822497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d6bc9b26-11ab-4028-9d08-77562a8bf11e · outbound
This paper cites Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction.
Polychromic Objectives for Reinforcement Learning Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.989292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation aa64cf93-90a0-4ba0-851a-a2a8f0997d06 · outbound
This paper cites OpenAI o1 System Card.
Polychromic Objectives for Reinforcement Learning OpenAI o1 System Card
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.849526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 76417b47-4424-4e6d-839e-4fd20980bfe9 · outbound
This paper cites Training language models to follow instructions with human feedback.
Polychromic Objectives for Reinforcement Learning Training language models to follow instructions with human feedback
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:20.010004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 630d9676-82fc-46e6-a947-a643463414ce · outbound
This paper cites Efros, and Trevor Darrell.
Polychromic Objectives for Reinforcement Learning Efros, and Trevor Darrell
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.186627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6039360e-be90-4733-b6b6-5d194c6b1fa8 · outbound
This paper cites Qwq-32b: Embracing the power of reinforcement learning, March 2025.
Polychromic Objectives for Reinforcement Learning Qwq-32b: Embracing the power of reinforcement learning, March 2025
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.180321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 37f7dbc8-c5db-4fbd-84f9-c811685013d1 · outbound
This paper cites Tapered Off-Policy REINFORCE: Stable and efficient reinforcement learning for LLMs.
Polychromic Objectives for Reinforcement Learning Tapered Off-Policy REINFORCE: Stable and efficient reinforcement learning for LLMs
Reference 29
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T11:56:19.889672Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8bb215ef-ad8b-4635-a89c-be7418a0fa5d · outbound
This paper cites Trust Region Policy Optimization.
Polychromic Objectives for Reinforcement Learning Trust Region Policy Optimization
Reference 30
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-18T11:56:19.874820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 76d28e10-b94c-4c31-aaf5-dd2a4298d077 · outbound
This paper cites Proximal Policy Optimization Algorithms.
Polychromic Objectives for Reinforcement Learning Proximal Policy Optimization Algorithms
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.900579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation af8158fc-415f-44d3-86bc-83f44bd71a03 · outbound
This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.
Polychromic Objectives for Reinforcement Learning High-Dimensional Continuous Control Using Generalized Advantage Estimation
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.965871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a692778f-f9a5-4280-9270-d63c135da3be · outbound
This paper cites State Entropy Maximization with Random Encoders for Efficient Exploration.
Polychromic Objectives for Reinforcement Learning State Entropy Maximization with Random Encoders for Efficient Exploration
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.978592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b7bb512f-69f9-4d5d-9625-cb1889f37d94 · outbound
This paper cites Deterministic policy gradient algorithms.
Polychromic Objectives for Reinforcement Learning Deterministic policy gradient algorithms
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.161695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d4ccc6a4-9dbb-47c8-9534-019b9f48bcab · outbound
This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.
Polychromic Objectives for Reinforcement Learning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.905594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 72fefe37-dd24-4dca-8a40-c7316b8ce585 · outbound
This paper cites Outcome-based exploration for llm reasoning.
Polychromic Objectives for Reinforcement Learning Outcome-based exploration for llm reasoning
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.168482Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 93fc4f7e-c621-4a05-9be5-d73b16ef952b · outbound
This paper cites Outcome-based Exploration for LLM Reasoning.
Polychromic Objectives for Reinforcement Learning Outcome-based Exploration for LLM Reasoning
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:20.005263Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a0f56d34-ea0b-4dbf-8066-aee5c87193a0 · outbound
This paper cites Sutton and Andrew G.
Polychromic Objectives for Reinforcement Learning Sutton and Andrew G
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.174400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation cacd5bfc-388b-4140-be75-d63d9f2713f7 · outbound
This paper cites Sutton, David McAllester, Satinder Singh, and Yishay Mansour.
Polychromic Objectives for Reinforcement Learning Sutton, David McAllester, Satinder Singh, and Yishay Mansour
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.171628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d88b1852-3a90-4dcb-bae8-f1db68cd280a · outbound
This paper cites Optimizing Language Models for Inference Time Objectives using Reinforcement Learning.
Polychromic Objectives for Reinforcement Learning Optimizing Language Models for Inference Time Objectives using Reinforcement Learning
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.949595Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e13e429c-6794-42ae-bd0c-04190cecee3f · outbound
This paper cites Sample Efficient Actor-Critic with Experience Replay.
Polychromic Objectives for Reinforcement Learning Sample Efficient Actor-Critic with Experience Replay
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.973162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1c486f10-7be1-4d2e-b8c6-5bcf9cea97fd · outbound
This paper cites Williams.
Reference 42
- Resolution
- verified exact
- doi, observed 2026-05-18T11:56:19.717406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e066ec79-ab5f-434f-bbb9-981820444130 · outbound
This paper cites The invisible leash: Why rlvr may or may not escape its origin.
Polychromic Objectives for Reinforcement Learning The invisible leash: Why rlvr may or may not escape its origin
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.884243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 426ee7b8-c965-4566-a4da-0495b0120ce6 · outbound
This paper cites Younis, Rodrigo Perez-Vicente, John U.
Polychromic Objectives for Reinforcement Learning Younis, Rodrigo Perez-Vicente, John U
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T11:56:20.164947Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 00f8689d-e627-40f2-bc05-57a53958efb7 · outbound
This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.
Polychromic Objectives for Reinforcement Learning DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.839256Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 00fbc4c2-9c5b-40c1-aa1b-b0aefdd8721c · outbound
This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.
Polychromic Objectives for Reinforcement Learning Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
Reference 46
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:19.869625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 41e0bbe0-176c-4be0-b935-3b4d5c574488 · outbound
This paper cites NoveltyBench: Evaluating Language Models for Humanlike Diversity.
Polychromic Objectives for Reinforcement Learning NoveltyBench: Evaluating Language Models for Humanlike Diversity
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.942452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f5cb03da-797e-420f-9839-d10cf6717f17 · outbound
This paper cites Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining.
Polychromic Objectives for Reinforcement Learning Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining
Reference 48
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T11:56:19.958923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 2c8cc3e7-1bb4-45ac-b3e7-7c18835230f0 · outbound
This paper cites Group Sequence Policy Optimization.
Polychromic Objectives for Reinforcement Learning Group Sequence Policy Optimization
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T11:56:20.014957Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Pith citing papers
No inbound Pith citation observations are available.