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Paper Citation Record · LEDGER

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL

As of 18 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2608.03108.

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

pith.paper-citation-record.v1
2608.03108 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:57:30.265625Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

24 of 24 outbound references displayed

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External citation measurements

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Outbound references

Observation 31556764-0c0c-4591-a04c-39bbbfdc47b2 · outbound

This paper cites OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning

Reference 1

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Observation bcdfc21f-8072-4a17-8f5e-09af7b5279b6 · outbound

This paper cites Off-policy deep reinforcement learning without exploration.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Off-policy deep reinforcement learning without exploration

Reference 5

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source=pdf_text observed=2026-08-15T14:57:30.186527Z digest=sha256:3d4dccdaabb55b53b28bdc403b2db96ce11fc02e2d313310eb7425d81ea8a8f4

Observation 44011897-b9d1-4779-869c-538b49e3bca3 · outbound

This paper cites Improving Offline RL by Blending Heuristics.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Improving Offline RL by Blending Heuristics

Reference 7

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source=pdf_text observed=2026-08-15T14:57:30.195104Z digest=sha256:cb5adc39a42dc1fcbf195a8fa31741197d717d3fa1de083878136ce0d5465360

Observation 69e91176-85a2-4f7b-8e62-19d3034dd66a · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Offline Reinforcement Learning with Implicit Q-Learning

Reference 8

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source=pdf_text observed=2026-08-15T14:57:30.199368Z digest=sha256:cbf907d03ac415d8e3c3dd3b821d41a270bee2ce10da2ab3ef02728bf3cdaa2d

Observation b3de7287-9c74-4cb8-bc26-486b000c7a2a · outbound

This paper cites Conservative q-learning for offline reinforcementlearning.Advancesinneuralinformationprocessingsystems,33:1179–1191,2020.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Conservative q-learning for offline reinforcementlearning.Advancesinneuralinformationprocessingsystems,33:1179–1191,2020

Reference 9

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T14:57:30.203753Z digest=sha256:5ff344f1aa836e30a92bd3457f9c8d8f29eb8657d3a58c24f21c9e97d662de17

Observation de02cbe8-e3ec-48b8-86ef-df1050bf6ff4 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 10

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source=pdf_text observed=2026-08-15T14:57:30.207852Z digest=sha256:0c44c39c9d1008106ad17fff63fdc495e01ee16571889b515e55260fa0080b1a

Observation 4ce00208-711a-4828-ad89-62c6a668c82e · outbound

This paper cites When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning

Reference 11

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source=pdf_text observed=2026-08-15T14:57:30.212100Z digest=sha256:d110c40d0ac4c83a36d5a68f97fb6e9cc06766a4cbcd6daaf4d64425d9fe9f22

Observation 3e6e6836-309c-49cd-9ff1-285174ff51cb · outbound

This paper cites Off-Policy Policy Gradient with State Distribution Correction.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Off-Policy Policy Gradient with State Distribution Correction

Reference 12

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source=pdf_text observed=2026-08-15T14:57:30.216181Z digest=sha256:b854c56406ca2f7f62278d9f79a72b3e04178d68ec898ca0b1891386b10ad6d3

Observation 51416b22-4dde-452d-a45d-59278335e56e · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 14

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source=pdf_text observed=2026-08-15T14:57:30.224473Z digest=sha256:9100dea6c2dadb58360023fd894dca7853d13a60c1f2f7522559e17773f2715a

Observation 14b9d6b3-51b8-4049-aaad-e5cc55e07e01 · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 15

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source=pdf_text observed=2026-08-15T14:57:30.228498Z digest=sha256:e9e18f1a5a29eb1f0219eefe98da2d1193ad3ee8c073910d5f9c2fc13f1bbcfc

Observation 685f7411-20ce-4c0a-968f-f571d87c969c · outbound

This paper cites Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

Reference 19

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source=pdf_text observed=2026-08-15T14:57:30.244330Z digest=sha256:cfd988c46a8d10cec0eb9f73f750b9b700ba127ca39eb6c03c8ce568cd12f47d

Observation 6265a41e-95b0-4d24-8451-a58bb9a906f0 · outbound

This paper cites The In-Sample Softmax for Offline Reinforcement Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL The In-Sample Softmax for Offline Reinforcement Learning

Reference 20

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source=pdf_text observed=2026-08-15T14:57:30.248495Z digest=sha256:e9a753f073dbafaef60fd4b8c3fda54c103d3e00b43033b914721485069f9ba2

Observation 092f5000-38bc-4cb8-a34a-c5af677ff58b · outbound

This paper cites Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood

Reference 22

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T14:57:30.257312Z digest=sha256:56e5b222be0ce93cf9390664e8e7de5c7783f2dd2de2a20554328f3082f87fae

Observation bb8e6db4-684a-4092-97f0-f2ce8480b389 · outbound

This paper cites For Gym locomotion, each evaluation uses 10 trajectories, whereas each AntMaze evaluation uses 100 trajectories.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL For Gym locomotion, each evaluation uses 10 trajectories, whereas each AntMaze evaluation uses 100 trajectories

Reference 23

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T14:57:30.261162Z digest=sha256:8df891f2f1664201d32040e6b63167323fc3c895490266c5529a270291766aa4

Observation cf67447e-6003-4453-b105-2009f791ce14 · outbound

This paper cites The mixture coefficient directly controls the amount of generalized information propagated by bootstrapping.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL The mixture coefficient directly controls the amount of generalized information propagated by bootstrapping

Reference 24

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source=pdf_text observed=2026-08-15T14:57:30.265625Z digest=sha256:e4cbe34caf55362abe120417c3ec8884c700429cc6178c828cd219b79b65013b

Observation 8f33bbb0-e5d1-4be1-afd5-abcaa9635a06 · outbound

This paper cites Less is more: Clustered cross-covariance control for offline rl.arXiv preprint arXiv:2601.20765,.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Less is more: Clustered cross-covariance control for offline rl.arXiv preprint arXiv:2601.20765,

Reference 2001

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T14:57:30.232431Z digest=sha256:db3ec32541907335a627269c66e0f68fdf1b0e239b22f9a482b8b9afffd21bdf

Observation 433cf653-0b74-44f2-9417-53b115e3f9be · outbound

This paper cites UNIQ: Conformal Calibration for Adaptive Conservatism in Offline Reinforcement Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL UNIQ: Conformal Calibration for Adaptive Conservatism in Offline Reinforcement Learning

Reference 2016

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T14:57:30.236224Z digest=sha256:efda48aa8843a7dcaa4a79e60b5c0d03ecf21fced2aa84114c646c76a71970bd

Observation 1ec10dd5-0bad-4ef6-afaa-06772f177aeb · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Behavior Regularized Offline Reinforcement Learning

Reference 2018

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source=pdf_text observed=2026-08-15T14:57:30.240241Z digest=sha256:77079e2cf7cdc932e57c0d8f9f7a2b8a9e9d0e027972b5a274ec1be6959d50f9

Observation 804d8ce9-a6bc-43be-a021-0c169128cbf5 · outbound

This paper cites Extreme Q-Learning: MaxEnt RL without Entropy.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Extreme Q-Learning: MaxEnt RL without Entropy

Reference 2019

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source=pdf_text observed=2026-08-15T14:57:30.190639Z digest=sha256:94508a1d7fe31c5a86d43fc2785980a1dde675381f3cab924587b4ad47e10231

Observation de025adc-5ed7-48e6-a40c-1d068feefcc9 · outbound

This paper cites UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning

Reference 2020

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source=pdf_text observed=2026-08-15T14:57:30.176724Z digest=sha256:4095b3a50ea0779d39da58b5c8537cb92b5e0c3f3af8c8c465aef9328d57b35a

Observation 952896ea-770e-4b55-855f-748acc8ccf9d · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 2021

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source=pdf_text observed=2026-08-15T14:57:30.181451Z digest=sha256:44d51008cf2f22d12907806cee1325956a7f599aebabc71fc6807eadfb841a53

Observation fcd56ebf-da73-4c32-a269-bb0cb8890c48 · outbound

This paper cites Flow actor-critic for offline reinforcement learning.arXiv preprint arXiv:2602.18015,.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Flow actor-critic for offline reinforcement learning.arXiv preprint arXiv:2602.18015,

Reference 2022

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source=pdf_text observed=2026-08-15T14:57:30.172608Z digest=sha256:948d9c8da4bd3756d1cdcf7c968e973e59434aef8bc91be946996e4147f01813

Observation 7f5d9b58-8389-43fe-a872-f437c8fd2cd9 · outbound

This paper cites Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization

Reference 2023

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source=pdf_text observed=2026-08-15T14:57:30.253091Z digest=sha256:6093be4f331ce4dc3e776a3ed1bb66c5c36c5c947e09c10e894edf0fcd5eb289

Observation f18bcc3d-6db1-4cda-a9c5-31b83a18726a · outbound

This paper cites Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization.

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

Reference 2024

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source=pdf_text observed=2026-08-15T14:57:30.220449Z digest=sha256:60c8f36646aff17410dacc53170ae6a9bc2699aeb2979baddcc35613a46f8b59

Pith citing papers

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