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Paper Citation Record · LEDGER

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies

As of 8 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2602.01196.

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

pith.paper-citation-record.v1
2602.01196 v2

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:50:06.167056Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

10 of 10 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved9
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b1390a52-77aa-4089-a64b-b01cb5e9cfc4 · outbound

This paper cites an unresolved cited work.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies Unresolved cited work

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:05.522646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:05.522646Z digest=sha256:4912b86dbee08abb37f16c016bd52c5ee7e34a84d7c5c6f1fc463b520c41c79d

Observation c660e1df-1414-4b67-b159-da49356ddd9f · outbound

This paper cites Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:05.277520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:05.277520Z digest=sha256:b610e6768bb4507bf9b67161018dbcb34c65a864ddbe98ed55b2e40c879e5d4c

Observation cb4235ce-098b-464c-8dfc-254c44323a84 · outbound

This paper cites Due to the contractive nature of the trained network, most trajectories will collapse onto stable manifolds.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies Due to the contractive nature of the trained network, most trajectories will collapse onto stable manifolds

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:05.930553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:05.930553Z digest=sha256:a2fabaa1ae68eba791793e11416331ad1b0e6aff3fcad0da9acb411323222479

Observation b793cbde-f541-4db3-a5ae-580ecef2b1c4 · outbound

This paper cites To address concerns regarding high-dimensional closure, we explicitly measure theClosure Error δ=∥h T −h 0∥2.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies To address concerns regarding high-dimensional closure, we explicitly measure theClosure Error δ=∥h T −h 0∥2

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:06.043865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:06.043865Z digest=sha256:00e2e0759a943a402ab069764eebf357b6ea262d3158facb350ffcbf83eb68fd

Observation 04ca470c-b0c4-4b00-8ff2-5c301522e217 · outbound

This paper cites Definition D.3(Dissipative Policy Dynamics (Restricted)).Let ht ∈R n be the policy memory state evolving according to ht+1 =f θ(ut, ht).

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies Definition D.3(Dissipative Policy Dynamics (Restricted)).Let ht ∈R n be the policy memory state evolving according to ht+1 =f θ(ut, ht)

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:05.587653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:05.587653Z digest=sha256:551abca9493549e693f622c03ba45c4132db60332d91279a09c809317c6b7253

Observation 311bfe3f-c3c7-4991-8361-89cc305c0ed6 · outbound

This paper cites an unresolved cited work.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies Unresolved cited work

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:05.694321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:05.694321Z digest=sha256:e4c27e43217a0c969189ed2e2e4be90a71d6c0702cdbe5faa47d585b484a8c10

Observation 929d2c5c-acc3-4222-b985-89909f7c4963 · outbound

This paper cites an unresolved cited work.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies Unresolved cited work

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:05.811656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:05.811656Z digest=sha256:f33b7581e19138e5e00cd5274b6370b7b44aa7e04892fc04f59afef9dff6d24d

Observation 595d91a4-a18f-4b7d-bad8-294d4246ae03 · outbound

This paper cites radiance field.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies radiance field

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:06.167056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:06.167056Z digest=sha256:a78774a1e57853c3871e0b76c9f03fbf4e3dbd6f4322d9eb2943602eddcb1477

Observation 657b47b2-dc24-45b0-89a4-4703e9fe7840 · outbound

This paper cites RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-03T05:50:05.182751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:05.182751Z digest=sha256:2545060aa337e404e7bd678c131afe1a7063010536e558d03a1a1e873b9bbc78

Observation 3c9611c1-0945-4174-a4da-d8a22fda16cf · outbound

This paper cites Human-Timescale Adaptation in an Open-Ended Task Space.

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies Human-Timescale Adaptation in an Open-Ended Task Space

Reference 3328

Resolution
malformed identifier
no resolver link, observed 2026-08-03T05:50:05.398571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:50:05.398571Z digest=sha256:0119cb71b829a2e3d73d57c0bf09266b31d8a1c971e1b07f9633b63a1bb094db

Pith citing papers

No inbound Pith citation observations are available.