Paper Citation Record · LEDGER

Continual Task Learning through Adaptive Policy Self-Composition

As of 17 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2411.11364.

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

pith.paper-citation-record.v1
2411.11364 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:43:02.792410Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

17 of 17 outbound references displayed

verified exact2
verified fuzzy2
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 492b9325-5d1f-4314-88f1-23a4ce261f3d · outbound

This paper cites Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal.

Continual Task Learning through Adaptive Policy Self-Composition Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:43:02.942633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9e214f58-064d-4f9d-a688-313a39ce7ce6 · outbound

This paper cites Merging decision transformers: Weight averaging for form- ing multi-task policies.

Continual Task Learning through Adaptive Policy Self-Composition Merging decision transformers: Weight averaging for form- ing multi-task policies

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:43:03.066575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fb4209c1-50ce-4b10-9444-46a4b35a1433 · outbound

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

Continual Task Learning through Adaptive Policy Self-Composition Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.757009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fce135ad-4685-4a96-9c9b-dbfae5d5279e · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Continual Task Learning through Adaptive Policy Self-Composition Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.765627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a4f1e8c-0551-40c0-b356-3208de4efe06 · outbound

This paper cites Progressive Neural Networks.

Continual Task Learning through Adaptive Policy Self-Composition Progressive Neural Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.770151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31a53479-bd7f-4433-9596-05097faefc12 · outbound

This paper cites t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making.

Continual Task Learning through Adaptive Policy Self-Composition t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.774690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c49ffab-c00d-41c2-8a79-759697d104bc · outbound

This paper cites Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning.

Continual Task Learning through Adaptive Policy Self-Composition Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:43:02.840699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1e270dc-1b3a-413a-8d50-130b122187de · outbound

This paper cites an unresolved cited work.

Continual Task Learning through Adaptive Policy Self-Composition Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:43:03.052836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 23dc4be9-2448-4cea-a043-4816697601cf · outbound

This paper cites an unresolved cited work.

Continual Task Learning through Adaptive Policy Self-Composition Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:43:03.033737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef5243dd-cdf0-4791-87dc-a0d844c903e0 · outbound

This paper cites In contrast, regularization-based and rehearsal-based methods introduce additional loss terms to mitigate catastrophic forgetting.

Continual Task Learning through Adaptive Policy Self-Composition In contrast, regularization-based and rehearsal-based methods introduce additional loss terms to mitigate catastrophic forgetting

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:43:03.017312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8fa348ff-459a-421f-8677-502e70672e88 · outbound

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

Continual Task Learning through Adaptive Policy Self-Composition Offline Reinforcement Learning with Implicit Q-Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.748298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1596077-3fed-4702-b872-d3586f90ee8c · outbound

This paper cites Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?.

Continual Task Learning through Adaptive Policy Self-Composition Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.726068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31607587-facf-4d87-aa1b-4756d7114e95 · outbound

This paper cites OER: Offline Experience Replay for Continual Offline Reinforcement Learning.

Continual Task Learning through Adaptive Policy Self-Composition OER: Offline Experience Replay for Continual Offline Reinforcement Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.734624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c432601c-beec-4caa-9361-be3d2d71009e · outbound

This paper cites Efficient Lifelong Learning with A-GEM.

Continual Task Learning through Adaptive Policy Self-Composition Efficient Lifelong Learning with A-GEM

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.720966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 374a8992-ed8a-43da-80e1-9aa8c588381b · outbound

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

Continual Task Learning through Adaptive Policy Self-Composition Off-policy deep reinforcement learning without exploration

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.730607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71f4c87a-8d58-44b1-ae0d-a2c67a171689 · outbound

This paper cites Variational Continual Learning.

Continual Task Learning through Adaptive Policy Self-Composition Variational Continual Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.761308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3666ea64-1760-4a4e-8f54-422824ee58d2 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Continual Task Learning through Adaptive Policy Self-Composition LoRA: Low-Rank Adaptation of Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:02.739411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.