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Paper Citation Record · LEDGER

Structured State Space Models for In-Context Reinforcement Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2303.03982.

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

pith.paper-citation-record.v1
2303.03982 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:52:13.969192Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 70b91b6e-3c5e-49f5-9d29-fea9a2ef7df1 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Structured State Space Models for In-Context Reinforcement Learning

Reference 215

Resolution
verified exact
arxiv_id, observed 2026-05-16T08:12:35.153144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-16T08:12:30.984870Z digest=sha256:bb02475795546cfe73400e556dd91d0f5979eeb0c778d5adbeeabfea1204fc33

Observation 8b551f16-99b3-442f-b236-dc6811fb8fac · inbound

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers cites this paper.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Structured State Space Models for In-Context Reinforcement Learning

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-12T18:52:13.969192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:52:13.969192Z digest=sha256:bc76cab116b8e93d86f15bd74114b19990b0c07ba39219dbb4f368823e6f944b

Observation a5cbe5c1-b853-4384-b36e-1b6a202a2c2a · inbound

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex cites this paper.

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex Structured State Space Models for In-Context Reinforcement Learning

Reference 271

Resolution
unresolved
no resolver link, observed 2026-07-31T23:52:15.692204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T23:52:15.692204Z digest=sha256:b060df7ee443d7cde1ad071ad8ea02cf5a829182233f3dbc6b50516b4b194831