Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T00:30:33.438380Z
Paper Citation Record · LEDGER
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning
As of 16 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2608.08158.
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 2026-08-15T06:32:42.880941+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
21 of 21 outbound references displayed
- verified exact8
- verified fuzzy0
- unresolved9
- parse uncertain0
- malformed identifier1
- metadata mismatch3
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 784c819c-c7f1-450e-ab28-661003ee0b00 · outbound
This paper cites Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al
Reference 10
- Resolution
- verified exact
- raw_fallback, observed 2026-08-12T00:30:34.194365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 621c1931-3a5a-4522-b387-072f2ac0beea · outbound
This paper cites Yujing Hu, Weixun Wang, Hangtian Jia, Yixiang Wang, Yingfeng Chen, Jianye Hao, Feng Wu, and Changjie Fan.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Yujing Hu, Weixun Wang, Hangtian Jia, Yixiang Wang, Yingfeng Chen, Jianye Hao, Feng Wu, and Changjie Fan
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.380419Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7def0c9b-206f-4f04-bbc5-6b2628a79826 · outbound
This paper cites Adaptive Reward Design for Reinforcement Learning.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Adaptive Reward Design for Reinforcement Learning
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T00:30:33.983121Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b7ba16a5-905d-445e-8ee6-73f009a8f600 · outbound
This paper cites Improving the Effectiveness of Potential-Based Reward Shaping in Reinforcement Learning.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Improving the Effectiveness of Potential-Based Reward Shaping in Reinforcement Learning
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T00:30:33.920720Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0959321e-ffc7-4aa6-a979-88646145f898 · outbound
This paper cites Automating Potential-based Reward Shaping with Vision Language Model Guidance.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Automating Potential-based Reward Shaping with Vision Language Model Guidance
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T00:30:33.897433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 579c68f3-b3bb-4799-9081-306ad7477257 · outbound
This paper cites Offline Reinforcement Learning with Imputed Rewards.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Offline Reinforcement Learning with Imputed Rewards
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T00:30:33.805757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d28876a2-e138-4536-9f91-996396d4226d · outbound
This paper cites Training Language Models with Language Feedback.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Training Language Models with Language Feedback
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.428381Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e543a49e-7729-41aa-b6ff-7b9baa04723d · outbound
This paper cites Richard S.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Richard S
Reference 20
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-12T00:30:33.765031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e4585cba-288c-442f-b3ed-abab54946091 · outbound
This paper cites Preprint: arXiv:2503.15724.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Preprint: arXiv:2503.15724
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.438380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ac6bbba-f328-4760-b7dc-0a49d7ae0571 · outbound
This paper cites SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning
Reference 2003
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T00:30:33.959833Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 12c4f907-4d91-4413-a2da-1075344c9f3c · outbound
This paper cites Concrete Problems in AI Safety.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Concrete Problems in AI Safety
Reference 2004
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.337729Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06a4be0d-ce7c-44bc-8c1e-b0e5d628c28c · outbound
This paper cites Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Reference 2010
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-12T00:30:34.023186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4e5647f9-ed4c-4576-a22c-724cd380c882 · outbound
This paper cites Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning
Reference 2016
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T00:30:34.061337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation fccf0884-caea-4685-8570-debabaf3e1fa · outbound
This paper cites Vision-Language Models as a Source of Rewards.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Vision-Language Models as a Source of Rewards
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.353399Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9156aeb-e0e3-4b49-9afb-f427b7132c33 · outbound
This paper cites Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.369554Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7cd305ff-1310-4359-90b9-744a94f37100 · outbound
This paper cites Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.385098Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 53f5622c-5e08-43f4-855e-6d73c66e15bc · outbound
This paper cites Preprint: arXiv:2104.06411.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Preprint: arXiv:2104.06411
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.418659Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d706fa9-bb33-4e1c-881f-420315478d9e · outbound
This paper cites 2022.1027340.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning 2022.1027340
Reference 2022
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-12T00:30:33.343177Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dfafb29f-ee29-4220-a9a3-2eca840c1fcf · outbound
This paper cites InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:30:33.400576Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb2dda5a-b769-4883-9785-8232e37977b4 · outbound
This paper cites Useful Policy Invariant Shaping from Arbitrary Advice.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning Useful Policy Invariant Shaping from Arbitrary Advice
Reference 2024
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-12T00:30:34.083387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 59c866d2-a085-43a9-be54-9e9d869c7020 · outbound
This paper cites AdrianK.AgoginoandKaganTumer.
A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning AdrianK.AgoginoandKaganTumer
Reference 2025
- Resolution
- verified exact
- doi, observed 2026-08-12T00:30:33.487245Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
No inbound Pith citation observations are available.