Paper Citation Record · LEDGER

Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.12094.

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

pith.paper-citation-record.v1
2405.12094 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:12:58.144502Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T17:26:09.984350Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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

Continual Task Learning through Adaptive Policy Self-Composition cites this paper.

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 8f71172d-919a-4c9a-a7ca-e21d2eb4ba34 · inbound

Skill Expansion and Composition in Parameter Space cites this paper.

Skill Expansion and Composition in Parameter Space Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-08T17:26:09.987921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 353b6159-e330-47b4-9ca8-7ba8c2d2a0e6 · inbound

Meta-Black-Box-Optimization through Offline Q-function Learning cites this paper.

Meta-Black-Box-Optimization through Offline Q-function Learning Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-16T04:12:58.144502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11a8608c-7c5f-4e4a-b464-734b49f8588b · inbound

TrojanTO: Action-Level Backdoor Attacks against Trajectory Optimization Models cites this paper.

TrojanTO: Action-Level Backdoor Attacks against Trajectory Optimization Models Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:11:19.774047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.