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Paper Citation Record · LEDGER

Graph Constrained Reinforcement Learning for Natural Language Action Spaces

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2001.08837.

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

pith.paper-citation-record.v1
2001.08837 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:17:17.891403Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T10:32:33.924392Z

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 f5669449-0e7d-4938-9fc2-3705db1041d0 · inbound

LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study cites this paper.

LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study Graph Constrained Reinforcement Learning for Natural Language Action Spaces

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:17.891403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:17.891403Z digest=sha256:9844b4066e44a048d4419aaa76c5d8085ba1894e6daf29a5ef83e9822291fa9f

Observation 9dc0c724-c700-4f6c-81ad-33c8d9d18991 · inbound

TextQuests: How Good are LLMs at Text-Based Video Games? cites this paper.

TextQuests: How Good are LLMs at Text-Based Video Games? Graph Constrained Reinforcement Learning for Natural Language Action Spaces

Reference 2020

Resolution
verified exact
local_arxiv, observed 2026-08-06T10:32:33.929689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T10:32:33.638115Z digest=sha256:a12e60e16b1a7746e05ca888347fce5fa5f39514468653356105ba2edab96b7a