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Paper Citation Record · LEDGER

GFlowNet-EM for learning compositional latent variable models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2302.06576.

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

pith.paper-citation-record.v1
2302.06576 v2

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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:09:54.297874Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T19:09:57.112816Z

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 89dcb113-70df-4388-82f4-8c39d9f9e970 · inbound

Effective Reward Specification in Deep Reinforcement Learning cites this paper.

Effective Reward Specification in Deep Reinforcement Learning GFlowNet-EM for learning compositional latent variable models

Reference 134

Resolution
verified exact
local_arxiv, observed 2026-08-11T19:09:57.121226Z

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.

source=arxiv_source observed=2026-08-11T19:09:54.297874Z digest=sha256:2fa417de41f6a64a67983ec4506fd0f77bc83c71f5f0219370cb449edb44ba86

Observation 419839c0-5292-4ba6-826a-172bccbb3f19 · inbound

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models cites this paper.

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models GFlowNet-EM for learning compositional latent variable models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-03T14:52:19.945390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:52:19.945390Z digest=sha256:58fe7d55e75c539e86c2f9fb4303ad57dcd8af1ec76853fa6af78c774390a7d5