Paper Citation Record · LEDGER

Flowing Through States: Neural ODE Regularization for Reinforcement Learning

As of 12 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2608.06595.

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

pith.paper-citation-record.v1
2608.06595 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:22:03.652658Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

12 of 12 outbound references displayed

verified exact0
verified fuzzy2
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 767e755a-c393-42c8-ac56-86ba618213ee · outbound

This paper cites Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.607022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8323f93-5c7c-4c11-abf9-8c4ad4cba8f7 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.623828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b629782-d888-4dd1-b462-d06109bb56c7 · outbound

This paper cites On the difficulty of training Recurrent Neural Networks.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning On the difficulty of training Recurrent Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.632145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e991aa44-d435-4ed5-805b-87bdaf3f1930 · outbound

This paper cites Data-Efficient Reinforcement Learning with Self-Predictive Representations.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning Data-Efficient Reinforcement Learning with Self-Predictive Representations

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.644762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32632547-65a0-417e-b9b6-7c2edcb754f4 · outbound

This paper cites Ruijie Zheng, Xiyao Wang, Yanchao Sun, Shuang Ma, Jieyu Zhao, Huazhe Xu, Hal Daum ´e III, and Furong Huang.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning Ruijie Zheng, Xiyao Wang, Yanchao Sun, Shuang Ma, Jieyu Zhao, Huazhe Xu, Hal Daum ´e III, and Furong Huang

Reference 1999

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:22:03.782164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4634eefb-2451-4790-866d-20155d3ffb3c · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning Asynchronous methods for deep reinforcement learning

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.628059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 905c5290-2f34-4e01-a2b4-4603fc041dc8 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.640340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7005ab08-14b2-42ed-8936-7bf7591901a8 · outbound

This paper cites Deep residual learning for image recog- nition.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning Deep residual learning for image recog- nition

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:22:03.800451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0c5909dd-f9d5-400d-9bd2-c38eb8a43d17 · outbound

This paper cites Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.615292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06cd70e2-b431-4db0-b786-508b814728d0 · outbound

This paper cites An overview of gradient descent optimization algorithms.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning An overview of gradient descent optimization algorithms

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.636260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6870f112-0947-4f9a-b755-fd78dfc22994 · outbound

This paper cites DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T04:22:03.611646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1636213b-800f-4184-bd8b-f562893511f2 · outbound

This paper cites 12 Published as a conference paper at ICLR 2026 B FLOWREGCONFIGURATIONS ANDRUNTIME Table 4: FlowReg configurations used for each environment and their corresponding runtimes.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning 12 Published as a conference paper at ICLR 2026 B FLOWREGCONFIGURATIONS ANDRUNTIME Table 4: FlowReg configurations used for each environment and their corresponding runtimes

Reference 2023

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T04:22:03.770295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.